Development of Acoustic Simulations using Parametric CAD …1325702/... · 2019-06-17 ·...

Linköping University | Department of Management and EngineeringMaster’s thesis, 30 ECTS | Aeronautical Engineering

Spring term 2019 | LIU-IEI-TEK-A-19/03468-SE

Development of AcousticSimulations using ParametricCAD Models in COMSOL

Antoine Bouilloux-LafontRubén Noya Pozo

Supervisors: Anton Wiberg, Linköping UniversityUlf Sellgren, KTH UniversityJakob Nyström, SCANIA CV ABDario Vignali, SCANIA CV AB

Examiners: Mehdi Tarkian, Linköping UniversityUlf Sellgren, KTH University

Linköping UniversitetSE-581 83 Linköping

013-28 10 10, www.liu.se

AUTHORS:

Rubén Noya PozoM.Sc. Student in Machine Design

KTH University | Sweden

Antoine Bouilloux-LafontM.Sc. Student in Aeronautical Engineering

Linköping University | Sweden

SUPERVISORS:

Jakob NyströmExhaust Systems V8

Engines (NXDE)Scania CV AB | Sweden

Dario VignaliFluid Dynamics and Acoustics

Simulation (NXPS)Scania CV AB | Sweden

Anton WibergDivision of Machine Design


Ulf SellgrenDepartment of Machine Design


EXAMINERS:

Ulf SellgrenDepartment of Machine Design


Mehdi TarkianDivision of Machine Design


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AbstractWith constantly changing regulations on emissions, heavy commercial vehicles man-ufacturers have to adapt for their products to preserve their quality while meetingthese new requirements. Over the past decades, noise emissions have become a greatconcern and new stricter laws demand companies to decrease their vehicle pass-bynoise target values.

To address the requirements from different disciplines, Scania follows a simulationdriven design process to develop new concept models EATS. The collaboration amongengineers from different fields is thereby necessary in order to obtain higher perfor-mance silencers. However, the pre-processing step in terms of acoustic simulationsis time-consuming, which can slow the concept development process.

In this thesis, a new method was introduced to automate the pre-processing of si-lencer acoustic models and allow for design optimisation based on acoustic perfor-mance results. A common Scania product study case was provided to several theseswithin the NXD organisation. The collaboration among the master thesis workersaimed to demonstrate the benefits of KBE and MDO and how they can be integratedwithin Scania’s current concept development and product introduction processes.

The performed work was divided in the following steps: data collection, methoddevelopment and concluding work. The first step consisted in gathering sufficientknowledge by conducting a thorough literature review and interviews. Then, an ini-tial method was formulated and tested on a simplified silencer model. Once approvedand verified, the method was applied to the study case EATS.

The study case showed that a complex product can have its acoustic pre-processingstep automated by ensuring a good connectivity among the required software anda correct denomination of the geometrical objects involved in the simulations. Themethod investigated how morphological optimisations can be performed at bothglobal and local levels to enhance the transmission loss of a silencer. Besides opti-mising the acoustic performance of the models, the method allowed the identificationof correlations and inter-dependencies among their design variables and ouput pa-rameters.

Keywords: parametric CAD models, KBE, acoustic simulations, optimisation, col-laboration, MDO

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AcknowledgementThe work performed in this thesis was conducted at Scania CV AB in collaborationwith Linköping University and KTH Royal Institute of Technology during the springof 2019. The carried out work has been challenging but also educational, giving usvaluable knowledge regarding acoustics and the truck industry. In addition, the col-laboration with other master thesis workers allowed us to understand the complexityin designing a model satisfying requirements across several disciplines.

First and foremost, we would like to sincerely thank all the people at Scania CV ABwho helped us achieve the goals of this master thesis successfully and made us feelwelcomed from the very first day. We would also like to give a special thank youto the Exhaust After-treatment Performance and Exhaust After-treatment Designdepartments at Scania CV AB for their continuous support and feedback. We arealso especially grateful to Kim Petersson, who ensured the best conditions for ourthesis and personal development within the company.

We would like to give a big thanks to our supervisors, Dario Vignali and JakobNyström at Scania CV AB, who guided us since the very beginning and throughoutthe entire thesis. It has been a pleasure to have you as supervisors and your guidancehas been crucial to our work.

We would like to also thank our respective universities supervisors and examiners,namely Anton Wiberg, Mehdi Tarkian and Ulf Sellgren, as well as our opponentsRubén Brau, Víctor Pérez and Matas Buzys, providing us constructive feedback.Lastly, we would like to thank the other master thesis workers for the focus groupmeetings and great company.

Södertälje, June 2019

Antoine Bouilloux-Lafont Rubén Noya Pozo

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Nomenclature

Abbreviations and Acronyms

Abbreviation MeaningASC Ammonia Slip CatalystCAD Computer aided designCF Content FreezeCFD Computational fluid dynamicsDA Design AutomationDF Design FreezeDG Design GenerationDOC Diesel Oxidation CatalystDOE Design of ExperimentDPF Diesel Particulate FilterEATS Exhaust After-treatment SystemFD Finite DifferenceFE Finite ElementFEM Finite Element MethodFV Finite VolumeHCV Heavy Commercial VehicleIOSH Institution of Occupational Safety and HealthIL Insertion LossKBE Knowledge Based EngineeringKTH Kungliga Tekniska HögskolanLiU Linköping UniversityMDO Multidisciplinary Design OptimisationNM Engine DevelopmentNX Emission Solutions DevelopmentNXD Exhaust System DesignNXDE Exhaust System V8 EnginesNXDX Exhaust System Inline EnginesNXP Exhaust After-treatment PerformanceNXPS Fluid Dynamics and Acoustics SimulationPCF Preliminary Content FreezeRR Result ReviewSCR Selective Catalytic ReductionSHERPA Simultaneous Hybrid Exploration that is Robust, Pro-

gressive and AdaptiveSPL Sound Pressure LevelTL Transmission LossUN United NationsUNECE United Nations Economic Commission for EuropeVG Verification GenerationWOT Wide Open Throttle

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Latin Symbols

Symbol Description UnitsE Element [−]

F Flexibility [−]

G Operator [−]

L Lift [N ]

p, A, B Acoustic pressure [Pa]

P Acoustic power [W ]

q Volume velocity[m3/s

]R Robustness [−]

S Design Space [−]

T Transfer Matrix [−]

U n-dimensional Space [−]

V Dimensionless Mean Design Space [−]

V Volume[m3]

W Weight [N ]

Greek Symbols

Symbol Description Units∆ Dimensionless variable range [−]

σ Bending Stress [N ·m]

Subscripts and superscripts

Abbreviation Meaninga inletb outletC feasiblei running numberj running numbermax maximummin minimumref reference

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another help

Table of Contents

List of Figures xiii

List of Tables xv

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Purpose and Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Delimitations and Limitations . . . . . . . . . . . . . . . . . . . . . . 4

2 Theoretical Framework 62.1 Acoustics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Acoustics of heavy commercial vehicles . . . . . . . . . . . . . . . . . 82.3 Exhaust After-treatment System . . . . . . . . . . . . . . . . . . . . 92.4 Transmission Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.4.1 Multi-port concept . . . . . . . . . . . . . . . . . . . . . . . . 112.4.2 Transfer Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.5 Product development . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.6 Design Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.6.1 Knowledge Based Engineering . . . . . . . . . . . . . . . . . . 132.6.2 Multidisciplinary Design Optimisation . . . . . . . . . . . . . 14

2.7 Simulation Fidelity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.8 CAD Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.8.1 Smart Parametric Modelling . . . . . . . . . . . . . . . . . . . 162.8.2 Model Flexibility and Robustness . . . . . . . . . . . . . . . . 17

2.9 Discretisation Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . 192.10 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3 Methodology 213.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.1.1 Former Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.1.2 Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.1.3 Literature Study . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2 Method Development . . . . . . . . . . . . . . . . . . . . . . . . . . . 243.2.1 Method Formulation . . . . . . . . . . . . . . . . . . . . . . . 243.2.2 Risk Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 243.2.3 Model Preparation . . . . . . . . . . . . . . . . . . . . . . . . 243.2.4 Model Generation . . . . . . . . . . . . . . . . . . . . . . . . 243.2.5 Model Simulation . . . . . . . . . . . . . . . . . . . . . . . . . 253.2.6 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.3 Concluding Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.3.1 Method Finalisation . . . . . . . . . . . . . . . . . . . . . . . 263.3.2 Method Documentation . . . . . . . . . . . . . . . . . . . . . 26

3.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

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4 Results 284.1 Interview Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.1.1 Product Development Process . . . . . . . . . . . . . . . . . . 284.1.2 Design and Simulation departments interaction . . . . . . . . 29

4.2 Method Application . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.2.1 HEEDS - Optimisation Software . . . . . . . . . . . . . . . . 314.2.2 EXCEL - Connecting Software . . . . . . . . . . . . . . . . . 324.2.3 CATIA V5 - Modelling Software . . . . . . . . . . . . . . . . 334.2.4 COMSOL Multiphysics - Simulation Software . . . . . . . . . 34

4.3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.3.1 Common General Method . . . . . . . . . . . . . . . . . . . . 354.3.2 Pre-CAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.3.3 CAD Model Generation . . . . . . . . . . . . . . . . . . . . . 364.3.4 Analysis Setup . . . . . . . . . . . . . . . . . . . . . . . . . . 374.3.5 Optimisation Setup . . . . . . . . . . . . . . . . . . . . . . . . 414.3.6 Result Review . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.4 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.4.1 Theses work collaboration . . . . . . . . . . . . . . . . . . . . 434.4.2 First box of a two-box EATS . . . . . . . . . . . . . . . . . . 434.4.3 Define Design Scope . . . . . . . . . . . . . . . . . . . . . . . 444.4.4 CAD Model Generation . . . . . . . . . . . . . . . . . . . . . 474.4.5 Analysis Setup . . . . . . . . . . . . . . . . . . . . . . . . . . 524.4.6 Optimisation Setup . . . . . . . . . . . . . . . . . . . . . . . . 564.4.7 Result Review . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.5 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5 Discussion 635.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.2 Simplifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.3 Delimitations and Limitations . . . . . . . . . . . . . . . . . . . . . . 645.4 Accuracy Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645.5 Work reflection and potential alterations . . . . . . . . . . . . . . . . 65

6 Conclusions 676.1 Research Question 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 676.2 Research Question 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 676.3 Research Question 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . 686.4 Research Question 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . 686.5 Method implementation within Scania . . . . . . . . . . . . . . . . . 69

7 Future Work 71

Appendices 74

A First Appendix - Risk Analysis A0

B Second Appendix - Interview Guide A1

C Third Appendix - Interviews Summary A3

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D Fifth Appendix - Simplified one-box silencer A6

E Sixth Appendix - Object Denomination A9

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List of Figures1 Partial organisation chart of NX at Scania . . . . . . . . . . . . . . . 22 Range of Human audibility with normal hearing [8] . . . . . . . . . . 63 Normal Equal Loudness Contour curves (ISO 226:2003) [8] . . . . . . 74 Sound level weighting curves across the frequency range [8] . . . . . 75 View of an Euro 6 engine and the Exhaust After-treatment System

(Scania CV AB, 2018) . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Transmission Loss Schematic - Illustration by Munjal (2014) . . . . . 107 Acoustical two-port schematic of a EATS - Illustration based on Åbom

(2010) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Design paradox: the MacLeamy Curve. The relation among freedom

of action, product knowledge and modification cost (Illustration byOverbey, 2018) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

9 Product development at Scania [3] . . . . . . . . . . . . . . . . . . . 1210 KBE system offering different variants of a product [20] . . . . . . . 1311 Benefits of using KBE in the product development process [20] . . . 1412 Different stages of Morphological (left) and Topological (right) trans-

formations [21] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1613 Design space of the geometrical model [21] . . . . . . . . . . . . . . . 1714 Thesis Methodology Flow Chart . . . . . . . . . . . . . . . . . . . . 2115 Flow overview of the iterative loop . . . . . . . . . . . . . . . . . . . 2516 Method decomposition architecture . . . . . . . . . . . . . . . . . . . 3017 Common Method flowchart proposal for NX . . . . . . . . . . . . . . 3518 Define Acoustic Design Scope . . . . . . . . . . . . . . . . . . . . . . 3619 Acoustic CAD Geometry Modelling . . . . . . . . . . . . . . . . . . . 3720 Selections process for getting the denomination and the number for

each surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3921 Types of denomination COMSOL asks as input . . . . . . . . . . . . 3922 Acoustic Analysis Setup . . . . . . . . . . . . . . . . . . . . . . . . . 4123 Acoustic Optimisation Setup . . . . . . . . . . . . . . . . . . . . . . 4224 Acoustics Result Review . . . . . . . . . . . . . . . . . . . . . . . . . 4225 Exploded view of the first box of the two-box case study EATS acous-

tic model - modelled in CATIA V5 . . . . . . . . . . . . . . . . . . . 4526 Master Skeleton first box of two-box case study product - in CATIA V5 4727 Active External References using Publications (left) and case study

EATS references and copied solids (right)- in CATIA V5 . . . . . . . 4828 Outer Volume (left) and Outer Turn (right) of the case study first box

two-box EATS acoustic model - modelled in CATIA V5 . . . . . . . 4929 Substrates surfaces and pole points of the first box of the two-box case

study EATS - in CATIA V5 . . . . . . . . . . . . . . . . . . . . . . . 5030 Substrate source and destination poles schematic . . . . . . . . . . . 5131 Poroacoustics Wool Area indicated in blue color - in COMSOL Mul-

tiphysics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5332 Interior Hard Soundary Wall indicated in blue color - in COMSOL

Multiphysics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

33 Cylinder where perforated plate condition is applied - in COMSOLMultiphysics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

34 TL plots for the first box of the two-box case study EATS, from A toB (top) and B to A (bottom) - in COMSOL Multiphysics . . . . . . 58

35 Correlation matrix for the case study EATS without applied localoptimisation - in HEEDS MDO . . . . . . . . . . . . . . . . . . . . . 59

36 Correlation matrix for the case study EATS with applied local opti-misation - in HEEDS MDO . . . . . . . . . . . . . . . . . . . . . . . 60

37 Local optimisation for L_test at the perforated plate . . . . . . . . . 6138 CAD model geometry of the simplified one-box EATS (without extru-

sions) - in CATIA V5 . . . . . . . . . . . . . . . . . . . . . . . . . . A639 Discretised simplified one-box EATS (without extrusions) - in COM-

SOL Multiphysics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A740 TL plot of a simplified one-box EATS - in COMSOL Multiphysics . A8

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List of Tables1 Limit values for pass-by noise of road vehicles (Miloradovic et al., 2017) 82 Case study first box components used for the acoustic simulations . . 443 Design Scope Summary of case study first box for automatic acoustic

simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464 Object denomination examples of first box of the two-box case study

EATS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 Default discretisation setting applied to the study case EATS . . . . 546 Global optimisation settings . . . . . . . . . . . . . . . . . . . . . . . 567 Local optimisation settings . . . . . . . . . . . . . . . . . . . . . . . 578 Correlation matrix parameter correspondence . . . . . . . . . . . . . 609 Thesis Project Risk Analysis Table . . . . . . . . . . . . . . . . . . . A010 List of simplified silencer parts included in the acoustic simulations . A611 List of parameters of the EATS utilised in acoustic optimisation loop A812 Object denomination of first box of the two-box case study EATS . . A9

1 IntroductionThis master thesis was performed in collaboration with the departments of FluidDynamics and Acoustics Simulation of Exhaust After-treatment System (NXPS)and Exhaust System V8 Engines (NXDE) at Scania CV AB (herein referred to as"Scania"), Linköping University and KTH University.

With the combined knowledge from the students having different backgrounds inaeronautical engineering and machine design, a new method is proposed for con-ceptual design of Exhaust After-treament Systems (EATS) for trucks, buses andindustrial applications in order for the Computer-Aided Design (CAD) models tobe automatically prepared for acoustic simulations and be optimised based on theobtained simulation results.

1.1 BackgroundFounded in 1897 in Södertälje, Sweden, Scania is today a global company active inmore than 100 countries manufacturing trucks, buses and combustion engines forheavy-duty vehicles, marine, as well as general industrial applications. Followingan environmentally friendly development process that aims at minimising waste andimproving resources efficiency, the goal at Scania is to offer sustainable modularproducts having a minimal footprint, while achieving higher efficiency [1]. Thesecutting edge high quality products are the reasons why Scania is a front-runnerwithin its field. With evolving emissions regulations, Scania has to constantly adaptto stricter environmental laws in order to keep its position on the market. The designof EATS is therefore crucial to comply with the air pollutant and noise emissionstandards.

The concept development process at Scania follows an interdiscipline iterative loopmethod, connecting design, simulations and evaluations [2]. Design engineers modifyand improve the created CAD models based on the feedback from the differentdepartments involved in the development process. The objective of the companyis to enhance this process by being able to connect and combine the work performedat the different departments. Thus, offering superior products while ensuring areduction in cost [3].

The development of EATS at Scania is performed within the Emissions SolutionsDevelopment (NX) organisation. Figure 1 shows the organisation chart of NX andits sub-divisions and departments. Note that all departments from these organisa-tions have not been listed. NXPS belongs to the Exhaust After-treatment Perfor-mance (NXP) organisation, responsible for performing simulations of the differentexhaust system products whereas NXDE belongs to the Exhaust System Design(NXD) organisation, in charge of the development and design of exhaust systems forengines. Note that the mission at NXDE is the maintenance and development ofafter-treatment systems for medium and large silencer. The collaboration betweenthese departments is essential to conceive a silencer that meets all the requirements.

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Figure 1: Partial organisation chart of NX at Scania

The automation of smart parametric CAD models has been proven to be efficient forthe personnel at Scania across different departments, allowing fast manipulation ofgeometrical topologies, and it has improved the performance of EATS to meet theongoing requirement changes in terms of emissions.

Design evaluation time has been significantly decreased by implementing parametriza-tion and Design Automation (DA) within various fields at Scania such as turbinehouses, inlet ports and more recently in EATS. This reduction in time allows for adeeper exploration of the design space and perform further evaluations [4].

In order for DA to be implemented, the parametric CAD models need to be developedin such a way that the failure rate upon parameter changes remains minimal. Therobustness of a model describes its quality by generating a multitude of modelshaving different design parameters to see if it transforms successfully or not. Theexploration of the design space consists of determining a set of suitable parametersby a process called Design of Experiments (DOE) [5].

1.2 Purpose and GoalsThe variation of models among the different disciplines at Scania creates a long andexpensive development process. The aim of various ongoing projects is to have acommon model on which the different departments are able to perform simultaneoussimulations and evaluations as well as design changes based on their results. Themain purpose of this thesis was to simplify the coupling between the design andacoustic simulations divisions.

A previous project aimed at introducing the acoustic simulations into the iterativeloop process as other disciplines have been implemented in the past. By automatingthe acoustic model preparation, the most time-consuming step of the design au-tomation process was greatly reduced. This method was shown to be efficient andpossible to incorporate within the current product development process. However,the method requires a series of steps and the use of various software to prepare thefiles for acoustic simulations [3].

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The objectives of this thesis were therefore to first simplify the preexisting automatedmethod used to prepare the CAD models for acoustic simulations. This new methodshould also account for transmission loss. Then, after applying the method to asimplified EATS and having validated the results with existing models, the levelof complexity was increased. While ensuring the robustness and flexibility of theCAD model, this represented a more realistic design. This method was also madeapplicable to special cases, such as adding a poro-acoustic material in the muffler.Lastly, general guidelines summarising the steps of the proposed methodology werecreated in order to implement it in real internal projects at Scania.

1.3 Research QuestionsTo meet the objectives previously stated and fulfil the requirements from Scania andboth universities, the following research questions were posed:

RQ1 - How can a parametric CAD model of an EATS be modelled to auto-mate simulations of acoustic performance?

RQ2 - How can the developed method for the simplified case be applied to acomplex geometry in the industry?

RQ3 - How efficient is the proposed method in terms of time expenses com-pared to the methodology currently in use?

RQ4 - How well does the proposed method allow for design optimisation interms of acoustics?

To address the questions formulated above, the thesis outline had to be defined. Thework was divided into three stages: data collection, methodology development andconcluding work. The first stage consisted of gaining insight on the topic of acoustics:a literature review on acoustics and previous theses performed at Scania were studied,as well as conducting interviews with specialists at the company regarding this topic.Then, with the gained knowledge, a first draft of the new method to be appliedwas created, assessed and tested on a simple case. Once the method verified, thecomplexity of the model was increased and a documentation for the proposed methodwas written. Further details on the conducted methodology can be found in Section3 of the report.

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1.4 Delimitations and LimitationsThis thesis aimed at automating the iterative loops of the concept development ofa silencer regarding the acoustic performance. That is, from the preparation ofthe parametric CAD model to the acoustic simulations. This process belongs todifferent departments in the company and therefore the main objective was to makethis process less time consuming and hence, more effective.

This project is the continuation of a previous thesis carried out during last yearat Scania. Aside from CATIA V5 and COMSOL Multiphysics, the thesis involvedthe use of ANSA software and NASTRAN files for the discretisation phase andpreparation of the model for simulations within COMSOL Multiphysics. The aim ofthis thesis was to develop a new simpler and efficient method that could be comparedto the one currently in use. Therefore, CATIA V5 and COMSOL Multiphysics werekept as the main software to be studied.

From an industrial point of view, the main requirement from Scania was that all theperformed work involved the compatibility with other software available within thecompany. In other words, the requirements from other disciplines had to be takeninto consideration when developing the methodology.

One limitation regarded the time constraint to perform the work as the masterthesis was intended to be completed within a 20-week timeframe. The iterationsinvolved simplifications in the first CAD parametric models. This delimitation wasset to ensure correct acoustic simulations of the models. Consequently, complexityadded into the design at a later stage limited detail design changes due to the timeconstraint. To reduce the computational costs, other design simplifications consistedof the omission of mechanical parts not having a significant acoustic impact on thesimulations.

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2 Theoretical FrameworkThis chapter focuses on the theoretical background required to conduct the thesiswork. Note that some information might only be mentioned or referred to the previ-ous thesis on acoustics simulations. The chapter includes a brief content on acoustictheory as well as its implementation within the current concept development processat Scania.

2.1 AcousticsSound can be described as the audible vibrations propagating through a transmissionmedium; a fluid or a solid body for instance. Acoustics represents the science thatstudies production, control, transmission and reception of sound waves (Merriam-Webster’s collegiate dictionary, 2019). The energy transferred by a sound wave canbe defined as the pressure difference over a specific period of time, where the soundpressure defines the amplitude of the wave. Pressure is an important property ofsound, as some magnitudes can result in pain experienced by humans. The humanaudibility frequency range varies from 20 to 20 000 [Hz ]. It should be noted that thefrequency of the sound waves does not determine the level of pain, if any is observed.In fact, according to Assistant Professor Matthias Möbius, pain threshold is almostindependent of frequency, as shown in Figure 2 below. [7]

Figure 2: Range of Human audibility with normal hearing [8]

Noise can have health effects on the human body that can be both physical andpsychological when regularly exposed to consistent elevated sound levels. Accordingto the Institution of Occupational Health and Safety (IOSH), the most well-knowneffect of noise is hearing impairment but can also lead, amongst others, to cardio-vascular effects, irritation and annoyance, stress and other effects on psycho-socialwell-being. Thereby, the study of acoustics is necessary to be included into thedevelopment process of a product in order to minimise those effects. [9]

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The sound level represents the relative intensity of sound and is expressed by thedimensionless unit [dB ]. The human ear is particularly sensitive in the frequencyrange of 300 to 6000 [Hz ] but has a lower sensitivity at lower and high frequencies,as seen in Figure 3. The contour curves can be inverted at a particular intensity level,which give the relative frequency response plot of the human ear. Each contour isdefined as a "phon" level, which corresponds to a unit for loudness equal to the soundpressure level in decibels and where a phon represents a 10 [dB ] step and is perceivedapproximately as twice as loud as the previous level [8]. The loudness levels in phonscorrespond to the sound pressure levels (SPL) at 1000 [Hz ] [10].

Figure 3: Normal Equal Loudness Contour curves (ISO 226:2003) [8]

Frequency weighting filters are used in sound level meters to attenuate the soundsignals. The different A, B, C and D weighting curves can be observed in Figure 4.The A weighting curve, also denoted dB(A) curve, is the most commonly used filterfor background noise and represents a baseline for noise emissions regulations.

Figure 4: Sound level weighting curves across the frequency range [8]

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2.2 Acoustics of heavy commercial vehiclesAs a Heavy Commercial Vehicle (HCV) European manufacturer present all over theglobe, Scania must comply with the emission regulations from the United Nations(UN). In Europe, these are written by the European regional commission: the UnitedNations Economic Commission for Europe (UNECE). The European Commissionclassifies vehicles on emission standards, where category M describes vehicles havingat least four wheels and used for the carriage of passengers, and category N forpower-driven vehicles having at least four wheels and used for the carriage of goods[3] [11]. Each category is divided in sub-categories based on the mass and/or powerof the vehicle. Table 1 below describes the noise limits for vehicles used for thecarriage of goods, the category of interest in this thesis. Note that Phase 1 to Phase3 represent the limit values applicable for new vehicles types from July 1st of 2016,2022 and 2026 respectively.

Table 1: Limit values for pass-by noise of road vehicles (Miloradovic et al., 2017)

VehicleCategory

Vehicle used for thecarriage of goods

Phase 1[dB(A)]

Phase 2[dB(A)]

Phase 3[dB(A)]

N1mass ≤ 2500 [kg ] 72 72 69

2500 [kg ] < mass ≤ 3500 [kg ] 74 73 71

N2rated engine power ≤ 135 [kW ] 77 75 74rated engine power > 135 [kW ] 78 76 75

N3

rated engine power ≤ 150 [kW ] 79 77 76150 [kW ] < rated engine power 81 79 77

≤ 250 [kW ]rated engine power > 250 [kW ] 82 81 79

The acoustic performance of HCV involves different complex systems where fourmajor noise sources can be defined: the engine, the air-intake system, the exhaustsystem and the tyres. Standard methods are implemented by the UNECE in Eu-rope to test the vehicle pass-by noise emissions. The original method, denominatedmethod A, demonstrated that the power train was the main contributor to this noisepollution. The test consisted of measuring the noise emitted by the vehicle at wideopen throttle (WOT), i.e. at maximum acceleration. The microphones are placedat the middle of the test track, therefore the air-intake is dominant at the beginningof the test, followed by the power train at the middle of the test and the exhaustnoise being dominant at the end of the track, all three masking tyre noise [13]. Thismethod can also be referred as the two or multiple microphone method. However,urban traffic noise is not characterised by full accelerating vehicles and therefore tyrenoise has a greater contribution in urban centres. According to method B, the newapproved but not yet implemented method performed at lower speeds, tyre noise isdominant over power train noise for speeds between 35 and 80 [km/h] [14].

In order to keep its position in the market, Scania must adapt to the ever changingregulations and hence keep reducing the noise emissions from the EATS, therebycomply with the defined noise regulations.

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2.3 Exhaust After-treatment System

Figure 5: View of an Euro 6 engine and the Exhaust After-treatment System (ScaniaCV AB, 2018)

In Figure 5 a schematic view of an Euro 6 engine and the exhaust after-treatmentsystem (EATS) is shown. The main purpose of the whole system is to fulfil theemission regulations [15].

It is known that the emissions requirements not only depend on the EATS but alsoon the fuel composition used in each vehicle. In general, there are two major issuesin regards to the combustion process in the engine: the release of carbon dioxide(CO2) and oxides of nitrogen (NOx), both harmful for the environment and health.Moreover, the incomplete combustion of diesel may also produce carbon monoxide(CO) and hydrocarbons (HC), therefore EATS are needed to reduce the amount ofdangerous chemicals and thereby fulfil the requirements. [3] [15]

The EATS contains five major components, named substrates. The first componentin the system is the Diesel Oxidation Catalyst (DOC), which has the main functionof oxidising carbon monoxide and hydrocarbons. The second system that comes intoplay is the Diesel Particulate Filter (DPF), a filter made of porous material whichhinders the particles to passing through it. The following step in the system is theaddition of urea. This is decomposed into ammonia and isocyanic acid, which inturn reduces the concentration of NOx and creates water, CO2 and nitrogen in theSelective Catalytic Reduction (SCR) by means of a chemical reaction. Incompletechemical reactions might lead to remains of ammonia in the exhaust gases, whichare retained by the Ammonia Slip Catalyst (ASC) before the gases are released intothe environment. [3] [15]

A common problem with the DPF is the build-up of soot in the filter. This canbe partially fixed by oxidising this soot into ash, which reduces the build-up andthereby the back-pressure. Nevertheless, this ash may also build-up and increasethe back-pressure with time and hence a regular change of the filter is necessary.The urea may also cause build-up of soot due to an incorrect vaporisation causedby the decrease of temperature in the SCR. Note that this built-up increases theback-pressure and therefore decreases the fuel efficiency. [15]

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2.4 Transmission LossThe performance of a EATS or silencer can be evaluated by measuring the trans-mission loss and insertion loss. The transmission loss (TL) represents the differencebetween the incident acoustic energy into a system and the transmitted acousticenergy into an anechoic – free from echoes and reverberations – environment afterthe system (Figure 6). In other words, it describes the sound intensity or the powercarried by sound waves being emitted from the silencer to an anechoic termination.[16]

Figure 6: Transmission Loss Schematic - Illustration by Munjal (2014)

In Figure 6, the acoustic pressure An of the incident wave from the inlet reachesthe system, represented by a filter, or resonator in this example. This larger volumeis used in order for the propagating sound waves to hit the walls of the filter andbe reflected, which in turn allows the sound waves to cancel each other. A reducedacoustic pressure A1 of the transmitted wave reaches the anechoic termination whilea reflected acoustic pressure Bn travels back towards the inlet. The length of thesystem is determined in order to cancel certain ranges of sound frequencies generatedin the EATS.

There are several ways to determine TL but at NXPS the acoustic power on thesurface at the inlet and outlet are measured and inputted into Equation 1. Extrusionsat both ends of the silencer are created to simulate the multiple microphone method.The values obtained are then compared to experimental measurements performedat the same location. It should also be noted that the simulation is also performedtwice, with sound waves starting at the inlet and outlet, respectively, to verify theobtained results. The model setup is further explained in Section 4.

TL = 10 · log10

(Pn

P1

), (1)

Where P is the acoustic power at the given surface and the subscripts refer tothe points shown in Figure 6.

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2.4.1 Multi-port conceptModelling sound generation and transmission of sound waves in ducts is performedutilising acoustical multi-ports. According to Åbom (2010), a multi-port can bedefined as a system where a casual relation exists between a set of input and outputvariables (x and y, respectively). Note that it is normally assumed the input andoutput variables observe the same dimensions and their relation is mathematicallydescribed as the existence of an operator (G) such that:

y = G [x]. (2)

This approach can also be referred to a "black-box" model, as the internal workings,operations and implementations are applied without full knowledge of the inner prop-erties of the system. In acoustics, the input and output variables studied are oftenpressure, force and displacement. [17]

2.4.2 Transfer MatrixA silencer can be assumed to be a two-port model coupling acoustic pressure (p) andvolume velocity (q) as both the input and output variables. The coupling results fromthe conservation of mass and momentum over a small control volume surroundingthe interface, assuming continuity of pressure and volume velocity, and no fluid flow.The coupling leads to the transfer matrix (T ), which is a formulation employed todescribe the relationship between inlet acoustic pressure and volume velocity withthe outlet acoustic pressure and volume velocity, as seen in the equation below: [17][

p̂aq̂a

]= T

[p̂bq̂b

]=

[T11 T12T12 T12

][p̂bq̂b

], (3)

Where the circumflex accent represents the Fourier transform of the variable.A silencer can thereby be represented as a acoustic two-port schematic, as seen inFigure 7 below. Note that a and b represent the inlet and the outlet of the EATS,respectively.

Figure 7: Acoustical two-port schematic of a EATS - Illustration based on Åbom(2010)

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2.5 Product developmentThe design of a product follows a series of phases from creation to manufacturing:preliminary design, conceptual development design and detailed design. Althoughthe denomination of these phases and their sub-parts might differ according to thecompany and the industry, the same structure can be observed. This is the engineer-ing design process [18]. The preliminary phase consists of establishing the require-ments of a new product based on the needs of the customer, which will define thedesign space of the product. In the concept development phase, different combina-tions of parameters are tested based on the given requirements. The proposed modelsgo through a concept screening phase where simulations and evaluations regardingdifferent disciplines are conducted. Finally, the detailed design phase involves thespecification of the different components and final parameters of the product [3] [19].

Figure 8: Design paradox: the MacLeamy Curve. The relation among freedom ofaction, product knowledge and modification cost (Illustration by Overbey, 2018)

The latter described phases can be related to the design paradox as seen in Figure8, where the possibility of introducing design changes reduces as these modificationsbecome costly and, on the other hand, the knowledge on the product increases asthe project evolves.

Figure 9: Product development at Scania [3]

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At Scania, the current product development process follows three phases (Figure9). The first phase, yellow arrow or concept development, emphasises on solvingcurrent problems and integrating new technological feasible solutions into the currentbusiness model. This phase is based on the need to improve existing designs ornew demands arising such as new design requirements. Green arrow or productintroduction is the following phase and deals with the refinement of the conceptuntil the product manufacturing and launch. Note that both yellow and green arrowphases are based on iterative loops from CAD modelling to simulation and evaluationof results. Once the product is well established into the market, the red arrow orproduct follow-up phase begins, in which solutions are provided for deviations of theproduct. [1] [3]

2.6 Design Automation

2.6.1 Knowledge Based Engineering

Knowledge Based Engineering (KBE) can be defined as integrating technology knowl-edge and engineering design processes within the concept development process of aproduct [20]. This KBE system can then use the gathered information for the de-velopment of similar models with a new set of input specifications, as seen in Figure10.

Figure 10: KBE system offering different variants of a product [20]

The benefits of KBE include the reduction of time expenses in regards to routinetasks and enhancing the proportion of creative tasks. According to Devaraja HollaV (2018), an organisation can observe a reduction of 20 to 40 % in cycle time aswell as effort in the development process. An outcome of KBE is Design Automation(DA), where KBE methods are employed through modern CAD systems allowing theflexibility of geometric models [21]. The morphology and topology of high level CADmodels can be modified through the use of rules, relations and facts. The influenceof KBE on product development can be seen in Figure 11.

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Figure 11: Benefits of using KBE in the product development process [20]

The great qualities of KBE come however at a cost and some of the critical factorsinclude having a structured developed methodology and literature, as well as goodmanagement support [3] [20].

2.6.2 Multidisciplinary Design OptimisationOptimisation can be defined as "the act, process, or methodology of making some-thing (such as a design, system, or decision) as fully perfect, functional, or effectiveas possible" (Merriam-Webster’s collegiate dictionary, 2019). In other words, it is theexploration of the design space and search to determine design parameters to iden-tify solutions, optimal designs, fulfilling the requirements imposed and respectingthe pre-defined constraints [3].

Multidisciplinary optimisation (MDO) describes a complex optimisation connectingdifferent disciplines with one of more objective functions and/or constraints. Anexample in aeronautical industry could be to have two functions f and g, representingthe weight (W wing) and lift (Lwing) of a wing, respectively. The wing is subjected tobending stress (σ) and must contain a minimum fuel volume (V fuel). The objectiveswould therefore be to minimise the weight and maximise the lift, while ensuringminimal/maximal values requirements, or in other words, comply with the givenconstraints. In this case, the fuel volume and bending stress must be less than thewing volume (V wing) and the stress limit (σlim), respectively. Equation 4 summarisesthe complex optimisation problem example.

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maxx

f(x) where f(x) = Lwing

minx

g(x) where g(x) = Wwing (4)

s.t.

σ < σlim Vwing > Vfuel

with respect to: x.

Developing detailed models can be difficult when knowledge about the product islimited. The collaboration among disciplines leads to more expert knowledge. If thisoccurs during the earlier stages of the design process, time expenses can be reducedby decreasing the cycle time in the development of complex systems. Note thatthe word collaboration is defined as the work performed jointly among experts ondifferent disciplines (Merriam-Webster’s collegiate dictionary, 2019). CollaborativeMDO is considered to be a new approach to deal with complex multidisciplinarysystems in the design phase of a product, which integrates high fidelity design tools inits process [24]. At Scania, joint efforts can be observed among the design, simulationand test engineers for instance, where experts are involved earlier in the process tobetter conceptualise the design, allowing for its optimisation.

2.7 Simulation FidelitySimulation fidelity can be defined as the extent or degree to which the simulation canreplicate the actual environment. In other words, it refers to the level of "realness" ofthe simulation. An industrial example in the aviation industry can be flight trainingand simulation devices where the simulation fidelity represents the degree of realismin terms of looks, sounds, responds and manoeuvres compared to a real aircraft.In this thesis, the simulation fidelity regards the accuracy and precision of acousticperformance of an EATS determined by the employed simulation software. [25]

It is natural to assume that higher fidelity simulation tools will grasp better thephysics involved and return valid results. It should however be noted that the increasein fidelity implies gains in complexity and hence an overall increase in computationalcosts. The level of fidelity is also related to the knowledge of the physics involvedin the simulation. Lower fidelity simulation might be preferred at earlier stagesin product development process despite uncertainties regarding the accuracy of theresults. On the other hand, high fidelity simulations are generally adopted towardsthe end of the development cycle of a product to be able to correctly verify theobtained results. [25]

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2.8 CAD Modelling

CAD systems have been part of the design industry for more than 60 years and haveproven to be essential engineering tools. The first parametric modelling techniquesin the mid-80’s marked the beginning of the exponential use and possible outcomesby generating CAD models [1]. The aircraft Boeing 777 for instance, whose maidenflight was on the 12th of June 1994, is a great example of the extensive use of CADmodels. In fact, the Boeing 777 was the first aircraft to have been entirely modelledin a CAD system: CATIA [26].

2.8.1 Smart Parametric Modelling

In order to increase the profitability of CAD systems, KBE methods are applied tocreate geometrical DA. This parametrisation of models allows for a flexible geome-try based on defined parameters. The transformation can be categorised as eithermorphological or topological.

The term morphology can be defined as the shape or form of an object. A mor-phological change therefore implies the modification of the geometry of an object bychanging its dimensions. According to Amadori et al. (2012), morphological modelscan be identified as four different stages (Figure 12, left).

The first stage represents an object with given dimensions that cannot be modified,denoted Fixed Object. Then, the Parametrised Object stage allows for dimensionchanges by modifying the parameters of the object. The parametrisation of theobject can also be based on mathematical relations (Mathematic Based Relationstage). Finally, the object geometry can follow a set of rules based on the inputparameters: this is the Script Based Relation stage.

Figure 12: Different stages of Morphological (left) and Topological (right) transforma-tions [21]

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The term topology refers to the location of features or objects in a geometrical model.A topological transformation involves therefore the addition, deletion or replacementof a geometrical feature or object, an "instance", in the geometrical model. Similarlyto the morphological transformation stages, topological transformations observe fourdifferent stages (Figure 12, right). The first two stages are not context-dependentand represent the copy and paste, or "instantiation", of functions on various objects.The Automatic Instantiation stage differentiates from theManual Instantiation stageby having the number of instances modified parametrically. The Generic Manual In-stantiation stage involves the instantiation of features or objects in different contextsmanually, and the last stage, Generic Automatic Instantiation allows for this processto be performed automatically [21].

2.8.2 Model Flexibility and RobustnessWhen designing a parametric CAD model, two major requirements need to be takeninto account: flexibility and robustness. According to Amadori et al. (2012), flexibil-ity "refers to the ability to represent a wide range of different product configurations,arrangement and sizes". In other words, it describes the possibility of a CAD modelto modify and adapt its geometry based on given input parameters. Robustness onthe other hand can be defined as the quality of the CAD model when evaluatingthe errors or instability issues arising from the changing geometry. Therefore, therobustness is inversely proportional to the number of errors in the model.

Figure 13: Design space of the geometrical model [21]

To ensure that these quality properties are satisfied, they require to be measuredquantitatively. Amadori et al. (2012) proposed a method that compares the numberof successful model updates with the total number of updates in the geometry inrelation with a specifically constrained design space based on the input parameters(Figure 13).

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Following the proposed method, an overall n-dimensional space U comprises all thetheoretically possible configurations where the input parameters can be modified overunlimited ranges. By assigning minimum and maximum values to these parameters,this design space can be reduced (S ). Note that this space contains both feasibleand unfeasible design. The design space comprising the totality of feasible designs(SC) is a union of sub-spaces (SCj), a disjointed union, in which only morphologi-cal transformations can take place as topological transformations do not guaranteecontinuity. [3][21]

Considering one sub-space SCj and assuming that each variable xi has a defined max-imum and minimum values (ximax and ximin, respectively), a dimensionless variablerange ∆i can be obtained as follows:

∆i =xi

max − ximin

xiref , (5)

Where ∆i is applicable for each parameter and xiref represents a baseline valuefor the variable xi that must not be equal to zero. Note that the same baselinevalue needs to be used among variants in order to allow for quantitative comparison.The dimensionless variable ranges allow then the obtention of a dimensionless meandesign space [21].

V SCj=

n∏i=1

∆i. (6)

As previously mentioned, the robustness of a model can be measured quantitativelyby comparing the number of successful updates with the total number of updates inthe geometry. This can be translated into the following equation [21]:

RCj = 1− NFailures

NUpdates, (7)

Where NFailures represents the number of models whose parameters input valueslead to errors or instabilities in the geometry, and NUpdate defines the total numberof iterations. Finally, the flexibility of the model can be evaluated by factorising thedimensionless mean design space with its robustness [21]:

FCj = RCj · V SCj. (8)

Flexibility of a model is dependent on its mean design space and robustness. As thedesign space increases, so does the flexibility, the model is more prone to observerobustness issues. Inversely, high robustness in a smaller design space leads to adecreased flexibility [21].

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2.9 Discretisation CriteriaDiscretisation represents the approximation of continuous functions, models, vari-ables and equations into "discrete" quantities. This necessary step within the sim-ulation process to be able to evaluate functions and integrate governing equationsat each element. Different methods exist to discretise geometrical object into sets offinite elements (FE), volumes (FV) or differences (FD) in order to perform simula-tions. Their differences include the ease of implementation and the type of equationsdefining these finite quantities. [27]

Acoustic simulations involve generally a set of FE, or a FE "mesh", to be evaluated.The mesh of a geometrical object has several properties affecting the accuracy of thesimulation that can be modified. First, element size is an important parameter as itaffects the mesh density. A denser mesh implies a higher fidelity and accuracy butalso severely increases computational time. The mesh should therefore be denser inthe zones of interest, and allow for a larger element size in the remaining geometry.Then, there are different mesh types that should be used accordingly and adapt tothe shape of the geometrical object to avoid skewed elements that can reduce theaccuracy of the results and destabilise the solution. [28]

2.10 Chapter SummaryThis chapter has provided a review of the theoretical background for this thesis.First, acoustic theory about the human perception of sound was outlined. Then,the application of acoustic regulations on HCVs and the method employed measurepass-by noise emissions were described. The different components or substrates of aEATS were named and portrayed, and acoustic performance of a silencer was spec-ified by defining the transmission loss. Product development and its process withinScania were then detailed and illustrated. DA was next defined by introducing KBEand CDMO concepts, and smart CAD modelling was presented by differentiatingmorphological and topological transformations and introducing flexibility and ro-bustness. Finally, the accuracy of the predicted results was discussed by takinginto consideration simulation fidelity and discretisation criteria and their effect onsimulations.

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3 MethodologyThe thesis methodology structure is summarised in the flow chart in Figure 14 belowand consists of three main parts or steps: data collection, method development, beingthe longest one, and finally concluding work.

Figure 14: Thesis Methodology Flow Chart

3.1 Data CollectionThe first phase consists of gathering technical information and gain knowledge on thegiven problem by conducting a literature study. Furthermore, the research performedthrough former master theses on acoustic simulations has been studied and used asa baseline for this thesis to formulate the objectives and expected outcomes.

3.1.1 Former WorkFollowing a similar methodology flow, the thesis conducted by Hellberg and Nyströminvolved gathering data, developing and finalising a method in the form of guide-lines to perform DOE studies of acoustic simulations of a EATS. Achieving a 98.75%robustness for 2000 tested designs allowing morphological transformations, the mod-els were automatically discretised within ANSA using a Python code. The use ofa LiveLinkTM with the software MATLAB was used to run the simulations withinCOMSOL Multiphysics. The pre-process of CAD models for acoustic performancewas automated and the proposed method was verified and validated to be able to beimplemented into the product development process.

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The thesis work underlined that the most time-consuming part for acoustic simula-tions is the pre-processing step for each design iteration. EATS are complex productsand any minor design change might affect the simulation setup. Therefore, the timespent in running simulations, and most importantly, running the correct simulationson feasible designs is greatly reduced. The conducted work proposed the introduc-tion of the software ANSA, often used within NXPS, to keep track of the surfacesand other objects in the geometry. Thus, by recognising their correct denomination,the correct settings in the pre-processing step were automated. Defining the objectsin the geometry within the modelling software based on their corresponding appliedsimulation settings was therefore crucial for the automation to be implemented. Theconnectivity among the involved software allowed the development of seven DOEstudies. [3]

To achieve this automation, it was necessary to create a parametric acoustic modelfrom the original geometry. The time estimation to generate such model was 35 [h].Then, the pre-processing step and simulation setup were estimated to take around35 [h] each. The design changes to be performed between DOE studies were alsoestimated to take about 35 [h]. Finally, the new pre-processing step and simulationsetup were assumed to take the same amount of time as for the first DOE study.Overall, the performance of this automation offered a reduction of 10% compared tothe general manual method applied at Scania.[3]

The conclusions from the thesis were that the implementation of the proposedmethod within NXD could greatly reduce the pre-processing step in the conceptdevelopment phase. By automating the repetitive and time-consuming actions, thetime between each iteration could be decreased. Additionally, the different devel-opment loops across the studied disciplines could be linked and favour informeddecisions to reduce the cost of the new products development. [3]

3.1.2 InterviewsIn order to get an insight on the involvement of the different departments in thedesign process of an EATS at Scania, a useful tool is to conduct interviews to gatherinformation about expert knowledge, practices and experiences [1].

Several interviews were conducted with engineers from both the Exhaust SystemDesign departments (NXDE, NXDX) and the Acoustics Simulations department(NXPS). In total, six people were interviewed on the following topics: DA, collabo-ration between departments, MDO, acoustics simulations, parametric CAD design,and CAD pre-processing for simulation in various disciplines. The conducted inter-views were of the type semi-structured, which can be defined as interviews followinga guide with questions and topics to be covered. The aim of this type of interviewis to collect information deeply into a specific topic [1]. Nonetheless, the questionsare standardised and hence leading to a more conversational interview where theinterviewer can modify or omit questions based on the conversation [29].

The interview guide followed for the interviews conducted at Scania, as well as asummary of the gathered information are documented in Appendices B and C, re-spectively.

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3.1.3 Literature StudyTo have a better understanding on the background of the thesis, a literature studyhas been conducted with the focus on product development and management withinthe industry and in particular at Scania, as well as technical subjects. The gatheredinformation from the literature review that was necessary to conduct this thesis isdocumented in Chapter 2. The latter subjects involved the following general topics:

• Acoustics: physics, modelling, pre-processing and simulations

• Emissions regulations

• Smart and high level CAD models

• Parametric CAD Modeling

• KBE and DA

• MDO

Note that throughout the duration of the thesis, additional literature was addedand the gathered information reviewed. The literature has been primarily collectedfrom e-resources from both universities libraries, institutes, and internal reports fromScania. The literature is also being shared among the other theses workers at Scaniawithin the same department during weekly focus meetings regarding MDO. Scaniahas been conducting master theses on the latter topic since 2015, which allowed theaccess to numerous references on the studied topics.

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3.2 Method Development

3.2.1 Method FormulationThe method development phase is the main body of the thesis in which the simu-lations and the optimisation varying the parameters are performed in an iterativeloop process. This process starts by formulating a new method to be implemented.In other words, defining the processes and tools to be employed to reach the setobjectives. This step was performed by analysing and evaluating the previously pro-posed method, as well as determining how it can be modified and improved. Thenew features of the software involved were investigated and tested to be integratedwithin a common framework.

3.2.2 Risk AnalysisOnce formulated, the feasibility and applicability of the method to a case study needto be taken into consideration. The Risk Analysis, which can be found in AppendixA, is a crucial step as it ensures to have alternative scenarios to reach the objectives.The table summarises the likelihood of events as well as their severity affecting thework, and counter measures to mitigate their consequences.

3.2.3 Model PreparationThe following step is the pre-CAD phase, which consists of gathering all the neces-sary information of the product to be modelled, as well as the tools being utilised.The thesis aims at studying the first box of the two-box case study silencer in termsof acoustics, where the same product is used in related theses conducted at NXD(Sub-section 4.4.1). More details on the components and functionalities of the si-lencer can be found in Section 2.3. First, a simplified CAD is generated in order tocomplete the desired loop and once satisfying results are obtained, the CAD com-plexity is increased in order to have a more realistic model. The aim is then to beable to apply this methodology to an existing product created at Scania. The mod-elling and acoustic simulation software used at Scania are CATIA V5 and COMSOLMultiphysics, respectively. Therefore the same software were used for this thesis.This phase emphasises in determining the different parameters to be studied as wellas the specific features of the EATS necessary to be modelled.

3.2.4 Model GenerationThe CAD model requires to be parameterised in such way so that different designscan be simulated. Thus, allowing morphological changes in the design to performmodel optimisation. Note however that the design and simulation of silencers atScania is performed at different departments, hence manually. To automate thisprocess, an optimisation software needs to be incorporated within the frameworkto define the lower and upper bounds of input parameters. These parameters shallautomatically modify the geometry of the model by employing design procedures,rules and relations as described in section 2.8. The CAD models require also sufficientlevels of robustness and flexibility.

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3.2.5 Model SimulationSimilarly to the Model Generation step, the acoustic simulations should be able tobe performed automatically for all imported CAD geometries. Format compatibilitybetween the design and simulation software is fundamental in order not to loosedetails when importing the geometry. Also, as the geometry is flexible, it is necessaryto keep track of the surfaces of the model in order to be able to apply the correctboundary conditions and desired descretisation specifications automatically.

3.2.6 Model EvaluationFinally, the last phase regards the evaluation of the model, where the design isvalidated based on the results obtained from the acoustic simulation. The acousticperformance results should then give new ranges for the different design parametersthe model is being optimised for. This step shall be performed within the introducedoptimisation software. Note that simulation software often observe optimisationfeatures that could also be implemented. However, as the simulation is run for eachiteration, the in-built optimisation should be performed at local level. For instance,determining the adequate amount of poroacoustic material.

The process described above allows the formulation and documentation of a methodto automatically simulate the acoustic performance of a parametric CAD model. Asmore knowledge and experience is acquired throughout the thesis time frame, morecomplexity in the CAD model is added, which requires iterating the loop multipletimes to validate the results and deem the method efficient. A summary flow of themajor steps of the method can be seen in Figure 15 below.

Figure 15: Flow overview of the iterative loop

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3.3 Concluding Work

3.3.1 Method FinalisationThe proposed method connecting the different software and allowing for an automaticacoustic performance simulation of parametric CAD models required to be functionaland validated. The obtained results with the new method were compared to the onecurrently in use. The efficiency in terms of computational costs and accuracy of theresults was assessed. Finally, the morphological optimisation on an EATS based onits acoustic performance was evaluated.

3.3.2 Method DocumentationThe developed method was based on the gathered information obtained through theliterature study and the conducted interview combined with the case study for thisthesis. The collection of data allowed the gain of knowledge not only in acousticsimulations but also on the development process at Scania. The study case aimedat automating the CAD model preparation process for acoustic performance andperform the required simulations. The combination lead to creating a specific methodbetween the design and simulation engineers and generalise a method that is notonly applicable to the study case. The guidelines to this automation procedure ispresented in Chapter 4.

3.4 Chapter SummaryThis chapter described the followed methodology of this thesis defined in three steps:data collection, method development and concluding work. First, the necessary infor-mation to conduct the project was gathered through former theses work, interviewswith Scania employees and a thorough literature study. Then, the method devel-opment listing the software and steps necessary to perform the optimisation waspresented. The collaboration with other thesis workers and the common assemblyproduct was then referred. Finally, the method validation comprising the tools toanswer the research questions, as well as the documentation of the proposed methodwere listed.

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4 ResultsThis chapter is divided in three sections: the Interview Summary presents the gath-ered information on the product development process and more specifically on thefollowed design and simulation procedures as well as the interaction between therespective departments. The Proposed Method is presented in the second section.Finally, the case study section shows the implementation and application of thedeveloped method on an EATS.

4.1 Interview ResultsThe results from the conducted interviews are divided into several areas correspond-ing to the different topics discussed with the interviewees. Note that only the currentdevelopment process at Scania, as well as the interaction between the design andsimulation departments are presented in this section. Further information on theremaining discussed topics is documented in Appendix C.

4.1.1 Product Development ProcessThe product development process at Scania is divided into three main stages; namelyyellow, green and red arrows. The first stage is the concept development phase thatconsist of a series of milestones, from the analysis of a need on the market to theapproval to move on the product development phase, the green arrow stage. Theyellow arrow therefore starts with the planning of a new project. Once approved,the correct budget is assigned and based on the new requirements, a set of conceptsare created. A screening of the different concepts is then performed, where the risksand resources required are evaluated.

The concept development phase is performed through multiple iterative loops wherethe objective is to eliminate or minimise at best the risks regarding the differentdisciplines. Both morphological and topological changes in the design are investi-gated in this phase. The yellow arrow stage can last for a few years in total andfinishes when a concept is deemed mature and promising. The product developmentphase represents the evolution of a concept from its selection to its detailed designand manufacturability readiness. The concept is iteratively modelled, simulated andevaluated for the different disciplines. Design changes in this stage are usually onlymorphological.

When a concept is deemed good enough, the design is "frozen", or in other words,a development generation (DG) freeze is performed. The DG is then virtually andphysically tested and evaluated against acceptance criteria. A Verification Genera-tion (VG) freeze is then conducted and the concept undergoes thorough verificationtesting. The VG is then certified and prepared for production. Once on the market,the product enters the last phase, the red arrow or follow-up stage. This phase em-phasises on finding solutions to deviations of the product. Note that as this thesisregards the concept development of a EATS, the red arrow stage was not furtherexplored.

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4.1.2 Design and Simulation departments interactionThe three stages mentioned earlier are applied to the different organisations withinScania but work processes and methods may vary across the departments workingwith product development. At NX, the process is as follows: the design engineergenerates a model that is modified for the specific simulation it must be tested forand sends it to the simulation engineer. In the case of acoustic simulations, theassembled product is saved as a surface part. The latter person performs furthermodifications and simplifications in the geometry and discretises the model.

Once the model is simulated and evaluated, the required design changes are commu-nicated to the design engineer through a compiled result report. It should be notedthat the "back-and-forth" actions between the two engineers varies in time based onthe persons and type of product involved. Indeed, design modifications and discreti-sation procedure are relatively less time consuming for experienced engineers.

As design and simulation engineers belong to different departments, the availableresources can differ. In fact, design engineers do not have access to simulation toolsand inversely for simulation engineers. Thereby, design engineers are unable forinstance to perform quick tests in order to assess the quality of the model before itis sent to the simulation engineer for a thorough simulation.

Section 2.2 describes the four major contributors to the pass-by noise emitted byHCVs. The regulations apply however to the entire vehicle and hence the attributedvehicle noise pollution is divided among the different concerned departments. Thedesired performance is defined by development targets, or acceptance criteria thatthe compromise among the disciplines aims at fulfilling.

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4.2 Method Application

In Section 3.2 an overview of method development is presented. The method appli-cation and more specifically the method finalisation belongs to the concluding workpart since the process requires to be repeated multiple times until the method isdeemed robust enough to be validated. This section emphasises on detailing theprocedure performed in each phase and hence the different steps followed using thedescribed software.

Figure 16: Method decomposition architecture

In Figure 16 the architecture of the method is presented. The simulation programHEEDS, developed by SIEMENS, provides an excellent way to automate processes,and is the main software in charge of running the iterative loops from the designphase to the optimisation phase, as well as determining the correlation of the designparameters with the acoustic results. A Microsoft Excel analysis is placed withinHEEDS workflow and the worksheet parameters are tagged to the ones in the opti-misation software. The Excel node has two main functions: modify the geometry ofthe CAD model in CATIA V5 via macros scripted in the VBA Editor and triggerCOMSOL Multiphysics via an Excel LiveLinkTM. This is to ensure the automaticstart up of the application since the software HEEDS (version 2018.10) cannot beconnected with COMSOL Multiphysics.

The following step is the import of the CAD model into COMSOL Multiphysics.This is made possible by having the Design Module licence and Import for CATIAV5 add on. These functionalities are necessary to keep track of the surfaces in thegeometry and therefore apply the correct required mesh type based on the surfacedenomination (surface ID). Once the simulation is performed, the results will providefeedback to the Excel node (within HEEDS) and allow for the next iteration to start.

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Additionally, the Optimisation module within COMSOL Multiphysics is employed tomodify local parameters and hence enhance the acoustic performance of the studiedmodel. During each iteration, the module determines the optimal values for param-eters affecting only the acoustic results without distorting the overall geometry ofthe model. Or, in other words, perform a local optimisation.

4.2.1 HEEDS - Optimisation SoftwareThe optimisation software HEEDS allows for the automatic simulation of numerousnumber of models and reduce the design space to find "optimal" designs. The maingoal being to increase the transmission loss, optimum designs can be defined in thiscontext as models with the highest TL.

All parameters involved in the optimisation need to be specified. The variables, orinput parameters have to be created and their upper and lower bound defined. Thesoftware is then able to vary the values of the variables within the given ranges totest different combinations. The responses, or output parameters have also to bedefined and each iteration is based on the results of the previous one. At least oneobjective function has then to be applied to one or several responses. Additionally,the design space can be further restricted by adding constraints to the responses.

To connect the remaining software, an EXCEL node within HEEDS is used to inte-grate the values and trigger the necessary macros (Figure 16). The different variablesand responses used for the optimisation are "tagged" or connected to their corre-sponding parameters within the EXCEL file described in Section 4.2.2. The func-tionality of the EXCEL node also involves the option to automatically run scripts orcall macros before or after all parameters for the specific iteration have been modi-fied. This option ensures that the change in the parameters has correctly been takeninto account and the different scripts are run in the desired order.

Although HEEDS offers different algorithms to perform optimisations, the in-builtSHERPA or Simultaneous Hybrid Exploration that is Robust, Progressive and Adap-tive algorithm is the one most commonly used. The uniqueness of SHERPA is thatit employs multiple search strategies at once to adapt to the problem by gainingknowledge about the design space. Moreover, the MO-SHERPA or Multi-ObjectiveSHERPA is a modified algorithm that can be used for multi-objective Pareto search.This is essentially beneficial for objectives that are in conflict with one another, withthe advantage of handling multiple objectives independently and trade offs can beexplored. [30]

As previously mentioned, optimisation is a tool to gain time in repetitive work andperform simulations on numerous models to focus on designs deemed "good" or suf-ficient. However, other important knowledge can also be extracted from this optimi-sation. Correlation matrices for instance allow to determine the inter-dependenciesamong parameters and results. This in turn reveals where the optimisation shouldbe focused on.

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4.2.2 EXCEL - Connecting SoftwareThe role of the software EXCEL is essential in the framework presented in Figure 16as it connects all the different software involved in this optimisation loop. The use ofthe EXCEL LiveLinkTM is necessary for the automation of the acoustic simulationsfor the parametric model and allows the geometry to be optimised for the givenparameters in HEEDS.

The EXCEL interface is composed of one main worksheet, named "Input-OutputData" in which the product and part names and numbers, the parameters to be usedfor the optimisation and the output results from the acoustic simulations for thegiven data are listed. Macros are used to connect and trigger the different neededfunctions.

There are two major scripts or modules in the VBA editor of the file. The firstone concerns the geometry in CATIA V5. The script ensures first that the correctparameter values obtained from the optimisation software are inputted and the ge-ometry modified accordingly. Then, a screen capture of the geometry is taken andthe coordinates of the four poles of the substrates are exported into a text file. The"coordinates" script uses CATIA V5 commands to extract the coordinates in thedesign space of the desired points. These points are necessary to apply the correctcoupling conditions within COMSOL Multiphysics. The text files are saved in afolder and each file contains a number that matches its corresponding iteration. Thelatter actions are performed by two separate smaller modules. Finally, the part issaved.

The second main script regards the simulation in COMSOL Multiphysics. The codeopens a command window of the software and connects to the server. The newlycreated geometry in CATIA V5 is imported into a "dummy" acoustic file containinga series of methods or in-built scripts. These codes are described in section 4.2.4. Thefunction of the methods are, in order, to ensure surface tracking, assigning the correctcouplings and selections, import and recognise substrate coordinates, discretisationof the geometry, set up boundary conditions and batch sweep study setups. Thelatter script allows to run in parallel simulations on smaller intervals, which in turnreduces computational cost. Once the setup is performed, the acoustic model file issaved and the simulation begins. Then, the TL values are exported to EXCEL andadditionally a text file of the TL values is saved as a backup. Note that a short scriptnamed "run" is used to activate the scripts in the correct order, for each iteration.

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4.2.3 CATIA V5 - Modelling SoftwareCATIA V5 is the software in which the original parameterised geometry is stored.Based on the parameter input, the geometry is accordingly modified without gener-ating any design error. In order to test the robustness and flexibility of the design,several optimisation loops were run with different number of models to be simulated.

Design engineers at Scania have specific methods and procedures to follow whencreating or modifying existing silencer parts in CATIA V5. Using the ENOVIAenvironment, several engineers are able to connect to the server and modify thesame product simultaneously by locking the specific part to be modified. A Scaniaproduct contains several sub-products in which more products or parts can be found.To be able to link the different parts and allow parameterisation, the Publicationsand Contextual Links features of the software are required. Thereby, the modificationof a parameter in a specific part will automatically change the same parameter inthe other tree nodes where it is effective.

To keep track of the surface denomination through the different software, the sur-faces from the solid silencer model need to be extracted. To not affect the collab-oration among the engineers on the same product, an additional "acoustic model"sub-product is created within the assembly. The different solid parts of tree nodesare published and then copied into the new part. This model contains thereby allparts that are required for acoustic simulations. Welds, screws and nuts are omittedfor instance. Finally, the surfaces are extracted from these solids to form an enclosedvolume to be simulated within COMSOL Multiphysics.

Once the parameters have been changed and the geometry adapted to the new inputs,a part from the product is saved using theGenerate Part from Product tool. This stepis necessary as COMSOL Multiphysics only accepts part format files from CATIA V5to be imported. To ensure that the automation is effective, several software optionsneed to be set. First, the DF1 - Product Data Filtering 1 licence needs to be activedin order for CATIA V5 to allow the Generate CATPart from Product options viascripts. Another option to keep in mind is the automatic closing of the CATIA V5after a specified amount time. This option must be disabled as the optimisationprocess requires several hours to be completed.

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4.2.4 COMSOL Multiphysics - Simulation SoftwareThe COMSOL Multiphysics environment is composed of two main interfaces, namelythe Model Builder and Application Builder. The first interface observes a model treethat is a graphical representation of the data structure that represents the model.This structure includes a list of settings to be applied to the model: geometry,discretisation, physics, boundary conditions, study and solver type, post-processingtools and visualisations. In other words, this interface allows different studies andsimulations to be performed manually.

On the other hand, the Application Builder allows the creation of customised ap-plications based on the multiphysics models. There are two important tools in thisinterface: the Form editor and the Method editor. The work is focused on the lattereditor, a programming environment that allows the modification of the model andits settings through JAVA® scripted codes, named "methods".

As mentioned before, a "dummy" COMSOL Multiphysics file containing the neces-sary methods to automatically carry out the acoustic simulations was created. Somemethods also follow preexisting in-built settings used by the acoustic engineers atNXPS. A description of the different scripts utilised for the simulations is presentedbelow:

• Surface-tracking : this method allows to keep track of the denomination ofthe surfaces created in CATIA V5. After converting the imported model to aCOMSOL Multiphysics geometry entity, one selection is created for each singlesurface in the model. This allows to connect and match the names defined inthe modelling software with the randomly assigned selections created upon theimport in the simulation software, and ensure all required settings are appliedto the correct surface.

• Couplings: in this method, special selections needed for the functions are cre-ated, e.g. the first inlet of the silencer, the inlet of the Diesel Particulate Filter,etc. These selections were made possible by methods created in a separatedclass. In JAVA® programming language, a class can be defined as a templatethat contains scripts modifying objects. Its operation process takes the nameof the surface wanted for the selection and returns the number or string thatCOMSOL Multiphysics internally assigns.

• Discretisation: this method contains the operations needed to build the mesh,depending on specific features such as the type and size elements. Specialselections are created, for example for the cross section boundaries in the inletand outlet pipes, where a different type of mesh is applied.

• Boundary conditions: with this method the specific BCs are applied to thecorresponding surfaces using the selections created in the previous methods.

• Recognition of substrate coordinates: the last method reads the text file con-taining the coordinates of the points from the substrates exported from CATIAV5 and creates points at these locations.

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4.3 Proposed MethodThis section describes the different steps to follow to implement automatic acousticsimulations of a silencer and to optimise its design based on the TL. An example ofthe proposed method can be found in Section 4.4.

4.3.1 Common General MethodThe common general method was developed in collaboration with the other the-sis worker at Scania within the NXD organisation. The different phases of thismethod follow a MDO process applicable to product optimisation within several dis-ciplines. The method is derived for acoustic simulations in the following sections.The flowchart in Figure 17 summarises the common developed method.

Figure 17: Common Method flowchart proposal for NX

The different stages of the method are represented by blocks containing a series ofcheck-boxes to be fulfilled before moving to the next stage, to ensure the requirementsare being met. Note that although the arrows in Figure 17 are only pointing in onedirection, changes within the different stages can be made without having to startthe loop again from the "Define Design Scope" phase. The condition is that allcheck-boxes in the modified phase or phases need to be verified again.

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4.3.2 Pre-CAD

This phase is derived from the common "Define Design Scope" phase described inSection 4.3.1. The pre-CAD phase consists of gaining sufficient knowledge on theproduct to be modelled or modified, as well as planning the type of simulation andoptimisation to be performed. The parameters to be used in the optimisation needto be defined: variables to be modified and output responses representing the targetvalues desired to be achieved. The derived block from the common method can beseen in Figure 18 below.

Figure 18: Define Acoustic Design Scope

4.3.3 CAD Model Generation

The first step in modelling a parametric design is to decide the environment to workon. This can be done either locally where the restrictions and constraints are onlybased on the modelling software available licences, or via an ENOVIA server. Thelatter option offers the possibility to have several people modifying different partswithin a common product assembly by locking those.

An EATS being a complex product and containing a multitude of parts, it is rec-ommended to have a part within the assembly containing points, lines and planes,as well as parameters, necessary to place the different components into the designspace. In other words, it is the creation of a "skeleton" of the geometry that will bereferred to the other parts within the assembly. If the complex assembly containssub-assemblies, skeletons at lower levels can also be created and linked to the masterskeleton. The benefit of employing a master skeleton is that the modification of theparameters is only required in one single part and the geometry automatically adaptsto the new input values.

As mentioned in Section 2.8.1, smart CAD modelling processes involve the use ofrules and relations to link the different components of a geometry to allow for correctmorphological modifications. Within the CATIA V5 environment, the connectionamong parts can be performed in several ways but the use of Contextual Links andPublications is recommended. These features allow the instances to keep a link withtheir reference. In this case, elements and objects created in the master skeletoncan be referred into part to correctly place and resize if necessary the componentsinvolved. The Publications features is necessary for copied solids from one to anotherpart within the same assembly in order for its geometry to be modified.

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Once the product is fully parameterised and saved, an acoustic model from thisassembly can be created. A new part within the tree needs to be created and all thedifferent parts necessary for the acoustic simulations copied as solids. The surfacesfrom these parts can then be extracted, their denomination accordingly modified, andbe placed in geometrical sets separating the surfaces into the COMSOL Multiphysicsdomains entities to be simulated. Note that the parts require to be enclosed volumesin order for the simulation software to run the in-built scripts. This can be checked forinstance by joining the surfaces of a part and applying a Close Surface transformationin the Part Design module.

Acoustic simulations require the inclusion of additional parts: extrusions at both theinlet and outlet of the silencer. These extrusions are to place several microphonesbefore and after the silencer to measure TL and IL. Note that each extrusion mustbe an enclosed volume on its own, i.e. cross-section surfaces belonging to differ-ent geometrical set will overlap each other. This allows COMSOL Multiphysics todistinguish separate domains being in contact.

Lastly, in order for the script to be able to automatically select the poles of thesubstrates involved in the simulation, the points need to be defined in their respectivegeometrical sets. The denomination of those is also essential as source and destinationvertices need to be differentiated.

A general summary of the modelling phase for acoustic simulations is shown in Figure19 below.

Figure 19: Acoustic CAD Geometry Modelling

4.3.4 Analysis SetupThe first step in the Analysis Setup is to ensure having all the necessary tools toperform the simulation. The connection can be established by enabling the requiredreferences in the VBA environment of EXCEL. All CATIA V5 related referencesshould be selected. Additionally, the FileSystemObject (FSO) reference needs alsoto be selected in order for the coordinates of the substrate poles to be saved intoa text file. EXCEL requires as well the installation of the LiveLinkTM module toconnect the software to COMSOL Multiphysics.

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Using the provided file, the user must then input the following information in theEXCEL interface: product and parts name and number identification, folder pathsfor the CAD geometries, poles coordinates of the substrates and batch files, as wellas the parameters to be optimised for and the responses to be exported into HEEDSMDO. The product and part numbers allow the VBA scripts to locate the param-eters in the tree architecture of the silencer. The variables and responses involvedin the optimisation are to be "tagged" within HEEDS MDO. CATIA V5 must beopened with the correct settings described in Section 4.3.3 and the top node of theassembly selected (highlighted). Additionally, the number of substrates and theirdenomination need to be inputted in the same interface.

Note that the user has not been given the option to input preferences for the scriptused to take a screen capture of each model. This can therefore be manually doneby the user in the VBA environment by modifying the script accordingly. Similarly,any enhancements of the provided scripts is up to the freedom and creativity ofthe user to modify. For instance, the script exporting the results from COMSOLMultiphysics can be modified. The current script exports only the TL values inthe EXCEL interface and a plot of this variable over the frequency range. Otherresults such as surface plots or mesh historgram can be integrated to the script to beexported. As a backup, tables containing all TL values can be exported. The scriptline in the code COMSOL needs to be commented out.

As mentioned in Section 4.2.4, COMSOL Multiphysics contains in-built scripts withthe operations and functions needed to perform the acoustic simulations automati-cally. A detailed explanation for the main common operations in the main methodsfor all cases is explained below, the other methods are modified depending on thecase study and they use the single and group selections made at the Surface-trackingcode.

• Surface-tracking, the main function of this script is the possibility to keeptrack of the surfaces from CATIA V5. When importing the model into COM-SOL Multiphysics, the software assigns a random number to each surface of theCAD model. The way to know these numbers is to apply these 2 operationsto each of the surfaces:

– 1. CONVERT TO COMSOL: this operation is applied to each surface inorder to be convert it into COMSOL Multiphysics’s internal format andtherefore use it within the simulation.

– 2. Explicit selection: this operation is applied to each surface as well.COMSOL Multiphysics uses an internal tag for this operation, whichlater will be used in the scripts as input for boundary conditions andother operations. Besides from these selection tags, this allows to use thecommand InputEntities() in order to know the number COMSOL Mul-tiphysics assigns to each surface and also use them in other scripts. InFigure 20 an example of the process is shown.

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Figure 20: Selections process for getting the denomination and the number for eachsurface

– 3. Union Selections: this operation combines entity selections into asingle group. Thereby, these union selections are used as input in otherscripts, for example: the Extrusions_1 union contains all the selectionscorresponding to this part in V5. This union is later used to apply NarrowRegion Acoustics A in the boundary conditions (refer to Section 4.4.5).

– 4. CONVERT TO SOLID : after the special union selections have beencreated, the CAD model needs to be converted into a solid so the physicsconditions, i.e. Pressure Acoustics, can be applied to all the domains.This operation is applied to each part creating domains.

– 5. Removing Details: this operation removes small details in the CADmodel automatically. More specifically, it removes short edges, small faces,silver faces to simplify the geometry.

COMSOL Multiphysics requires different types of input depending on the func-tion or operation to be applied to a specific domain or surface(s). These dif-ferent types of inputs and their corresponding surface names in CATIA V5 areshown in the last 4 columns in the Figure 21. As it can be seen, there are4 types of inputs: boundary, point or edge tag, and then their correspondingnumber.

Figure 21: Types of denomination COMSOL asks as input

The way to search for these names or numbers that COMSOL Multiphysics re-

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quires for each operation is to create methods in a separate class using JAVA®

programming language. Later on, these methods are used in each script andthe original object name from CATIA V5 is searched for in the method. Thisreturns the required name, tag or number of that object for that specific oper-ation.

Once all these operations have been performed, the Union Selections and theCONVERT TO SOLID need to be adjusted. These are based on the studiedsilencer and can be easily modified to incorporate a different product to besimulated.

• Couplings, this method contains the operations needed for all the actionsperformed in the component couplings node. In this script, specific selectionsneeded for each operation are created. The list below explains each type ofoperation performed by this method.

– 1. Variables: these operations are used to link local variables in specificsurfaces. For instance, different union selections are created to apply thevariables to each inlet and outlet of every substrate.

– 2. Functions of interpolation: these functions import a file containing thetransfer matrix elements of each substrate.

– 3. Average: this coupling operator integrates the expressions over thesource selection and divides them with the measure of the source selection.

– 4. Integration: this coupling operator integrates the expressions over thesource selection.

– 5. Linear extrusion: this coupling operator linearly maps an expressiondefined on a source to an expression that can be evaluated in the destina-tion. This is used to link both inlet and outlet surfaces of every substrate.

Prior to attempt running a simulation, the user should ensure that the correct foldersare created and/or emptied. These folders include: CAD geometries, substrate polescoordinates and batch sweep. The path to those folders need to be inputted into theEXCEL interface of the file provided as mentioned in Section 4.3.4.

At Scania, the TL plots are compared with the curves of previous models that arein production. An acceptance criteria is set for low frequency sound attenuation. Adevelopment target characterised by a dashed line is added to the plot to evaluatethe performance of the silencer. The objectives are therefore to have a TL curveabove this development target. However, curves below the target do not necessarilyindicate an unacceptable acoustic performance of the EATS.

A general list for the Analysis Setup is presented in Figure 22.

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Figure 22: Acoustic Analysis Setup

4.3.5 Optimisation SetupThe first step in the optimisation setup is to insert an EXCEL node in order forthe provided connecting file to be linked to HEEDS MDO. In the EXCEL node, theoption to save the workbook after each iteration needs to be selected. Macros withinthe provided file can be executed before or after setting the new inputs variablesat each iteration: "Run_simulation" needs to be typed into the provided box formacros to be executed after setting the new variables.

All variables and responses to be utilised in the optimisation should be added. Theirtype (continuous, discrete, etc) and bounds, if any, need also to be defined. Themethod to be employed and number of iterations to be performed need to be se-lected. As mentioned in Section 4.2.1, the SHERPA method is recommended andthe number of iterations depends on the type of optimisation study. At least oneobjective function is required and the responses can also be subjected to constraints.Additionally, to ensure that one obtains coherent results, the user can set bound-aries for the responses, which will distinguish the unfeasible designs and set them aserroneous models.

When the optimisation setup has been implemented, the software should display amessage indicating that the study has passed all validity checks and is ready to berun. Note however that if changes have been made in EXCEL file, the "Reload File"button should be clicked. If the parameter bounds are uncertain, a DOE study canalso be run prior to the optimisation. It is recommended to have a relatively lownumber of parameters to be utilised in the simulation.

Besides from the global optimisation loop performed in HEEDS MDO, a local opti-misation is carried out within COMSOL Multiphysics for each iteration. Some localparameters and conditions such as the amount of poroacoustics material or length ofthe perforated plates, are to be tested and optimised for a specific range of frequen-cies. These operations are automatically performed in such a way that the user is notrequired to modify or select any new parameters in the Model Builder interface ofthe software. However, if the optimisation study conditions or objectives are desiredto be changed the corresponding script would require some modifications.

A general summary of the optimisation setup is described in Figure 23 below.

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Figure 23: Acoustic Optimisation Setup

4.3.6 Result ReviewWhen the optimisation is completed, there are several steps to follow in order toobserve and interpret the obtained results. Some designs are expected to have faileddue to unfeasible parameter combinations and will be represented as design errormodels. After having saved the study, the study tab will indicate the "best obtained"design. In other words, the model with the combination of variables resulting inresponses meeting the best defined objectives and constraints set.

Several plots, tables and graphs can be retrieved in the HEEDS POST interface. Asimple table with all the data of plausible design can be extracted for instance. Formultiple objective function optimisations, a Pareto front plot can also be extractedfrom the data. Correlation matrices are another type of tools that can used to identifyinter-dependencies among variables and responses. This can be very useful to planfurther optimisation loops with a reduced number of parameters and emphasise theoptimisation on specific design space regions.

An important step in post processing results is to ensure the obtained data is verifiedand valid. Verification can be performed by running a manual simulation in theopposite direction (outlet to inlet). The results plots should match. Validationof the results can be conducted in several way. The models can first be quicklyverified by simulating manually within COMSOL Multiphysics the model or modelsdeemed "good" or sufficient enough and observe if the results match. Validationcan be performed by obtaining experimental data for real models having the exactsame dimensions as optimised models. Note that as multiple models cannot bemanufactured for the purpose of experimental testing due to the cost involved, acomparison of the "optimal" design is necessary.

A general summary of the steps of the results review is described in Figure 24.

Figure 24: Acoustics Result Review

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4.4 Case StudyIn order to verify the feasibility of the proposed method, a first iterative loop wasperformed using a simplified one-box silencer. The CAD model is composed of onlysurface elements and observes eight parts and eleven parameters able to be altered.The details of this simplified model, as well as a description of the different testsperformed can be found in Appendix D.

4.4.1 Theses work collaborationTwo other theses regarding MDO have been conducted within the NXD organisa-tion during the same allocated timeframe. These theses aimed at combining FiniteElement Method (FEM) and CFD simulations in an automated framework [31], andintegrating cost and manufacturability within the iterative product development pro-cess at Scania [32], respectively. To demonstrate the efficiency and importance ofthe collaboration among the disciplines in real engineering design environment, acommon Scania EATS CAD product was used as the baseline for all three theses.

This joint effort among the thesis workers allowed to distribute the parametrisationof the model and emphasise the research on the simulation and optimisation of theproduct in their respective disciplines. The parametrisation for this thesis comprisedmain the Baffle plate product and the creation of the acoustic model. This alsoallowed the comparison of a valid model within one disciplines across others.

4.4.2 First box of a two-box EATSThe case study EATS is composed of two boxes in series, where the first box comprisesthree substrates: SCR, ASC and DPF. To simplify the complex geometry of thesilencer, only the first box was considered for the simulations in all the disciplinesinvolved in the theses mentioned above. The aim of using a common product withthe other master thesis workers at NXD was to demonstrate the advantages of MDOand demonstrate how the collaboration among departments can be enhanced.

The given assembly required to be parametrised and adapted for the different simu-lations and optimisation iterations to be performed within the different theses. Allmembers of the latter theses were assigned to parametrise several parts to reduceoverall time expenses of CAD modifications.

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4.4.3 Define Design ScopeThe first box main assembly is divided in eleven sub-products that can containone or several components. Nonetheless the acoustic performance of the silencer isnot affected by all the parts within the tree. It is therefore important to list thecomponents that will not or have a negligible effect on the simulation. With the helpfrom acoustic engineers from NXPS, the following components were omitted from thesimulation: welds, brackets, outer jackets and insulation, nuts and bolts, substratecovers. These components are not in contact with the flow and omitting them cangreatly reduce the computational cost of acoustic simulations. All the componentsinvolved in the simulation are listed in Table 2. Note that the numbers next to thepart names correspond to the numbers in Figure 25. The brown cylinders in Figure25 represent the extrusions located at both inlet and outlet of the silencer.

Table 2: Case study first box components used for the acoustic simulations

Product Name Part Name Description

Intake AssemblyInlet connector (1) Connection with engine exhaust pipeInlet Pipe (2) Pipe cross-section expansionIntake Plate (3) Flow wall expansion to substrate dimensions

Baffle Plate Assembly Baffle Plate (4) Connection among stages and inner jackets

Stage 1SCR (5) Selective Catalytic ReductionASC (6) Ammonia Slip CatalystDPF (7) Diesel Particulate Filter

End Plate Assembly

End Plate (8) Connection among stages and inner jacketsTurn Concept (9) Flow passage from Stage 1 to Stage 2Guide Plate (10) Part enabling exhaust gases - urea contactCone (11) Part directing the flow to be mixedProtective Casing (12) Seal between urea injector and flow

Stage 2Inner Evaporator Pipe (13) Mixed flow directing pipeInsulation Cover (14) Soundproofing materialMixer (15) Urea and exhaust gases mixing fan

Outlet Pipe Assembly Outlet Pipe (16) Pipe cross-section contractionOutlet Connector (17) Connection with second box of EATS

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Figure 25: Exploded view of the first box of the two-box case study EATS acousticmodel - modelled in CATIA V5

The acoustic simulations performed employing COMSOL Multiphysics by the engi-neers within the NXPS department are to retrieve the TL over a specific frequencyrange. In the case of the case study silencer, some specifications have to be followed.First, the simulation shall be of the linear extrusion type, connecting substrate inletand outlet surfaces using transfer matrix elements at their four poles. The necessarytext files to be imported within COMSOL Multiphysics were provided. Then, thesimulation was specified to be run at 380 [◦C] over a frequency range of 10 to 800[Hz ]. Additionally, the standard distances from the inlet and outlet for the placementof the microphones had to be respected.

Next, the type of optimisation or optimisations needs to be decided. In the case ofthe first box of the two-box case study EATS, the acoustic simulations are mainlyaffected by volumes. Hence, a morphological optimisation is preferred. A DOE studyshall be performed first in order to determine the bounds of the selected parameters.Then, an initial optimisation shall be run with a specific number of variables. Oncecompleted and the results analysed, a second optimisation shall be conducted witha reduced number of parameters, i.e. the emphasis being on the variables affectingthe most the obtained responses.

Assuming the provided scripts do not require any modifications, the different tasksare expected to have the following time durations: the geometry, connected to theENOVIA server, should update within two to three minutes. The generation of apart from the product, as well as the secondary scripts involved in the simulationshould be almost instantaneous. A margin of one minute can be considered. Theacoustic simulation is expected to take 20 minutes.

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Hence, for 100 iterations, the optimisation loop could last up to 40 hours.

A summary of the scope and specifications of the optimisation to be performed islisted in table 3 below.

Table 3: Design Scope Summary of case study first box for automatic acoustic simu-lations

Name DescriptionAcoustic target value Maximise TL based on frequency range

Type of simulationAcoustic Linear ExtrusionFrequency range: 0 - 800 [Hz ]Temperature: 380 [◦C]

Expected parameters affecting TLDiametersSpline tensionsDistance among components

Components to be omittedWeldsBrackets and outer jacketsNuts and bolts

Time estimation

CAD parametrisation: 30-35 [h]CAD acoustic model creation: 20 - 30 [h]Provided files modifications: 1 - 2 [h]CAD modification on new inputs: 2 - 3 [min]Product to Part generation: 1 [min]Coordinate text file generation: 1 [min]Geometry screen capture: 1 [min]Acoustic simulation: 20 [min]DOE study: + 24 [h]Optimisation study: + 24 [h]

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4.4.4 CAD Model GenerationThe assembly of the first box of the two-box case study product is located on theENOVIA server common to the design engineers within the NXD organisation. Whenopening CATIA V5, in the Options window, the DF1 - Product Data Filtering 1Product licence should be activated in the Shareable Product tab of the Generalnode. In Part Infrastructure, the Synchronize all external references when updatingshould be selected. Additionally, the Automatic disconnection after option shouldbe deselected to disable CATIA V5 terminating after a specific amount of time.

In the pre-created skeleton part of the assembly, the different objects such as points,lines and curves should be modified in such way that they can be altered via param-eters and relations. In Figure 26, the parameters main parameters are the Stage 1diameter and Stage 2 diameter. To connect the parts within the assembly using theContextual Links tool, all objects and parameters are required to be published, asseen in the bottom left corner of the same figure. Then, the desired object can becopied using links into the other parts and be referred to those.

Figure 26: Master Skeleton first box of two-box case study product - in CATIA V5

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Once the master skeleton is saved, the remaining parts of the assembly can be pa-rameterised. For sub-assemblies, the creation of skeletons is advised. Similarly tothe master skeleton, parameters can be created and external objects added, andthen referred to the local geometrical objects. When pasting reference elements intoa new part, the defined Contextual Links must be active. Figure 27 (left) shows asub-skeleton on top of the image and active published references from a differentpart within the assembly. Once the part has been correctly parameterised, the solidparts involved in the acoustic simulations require to be published for the next steps.

Figure 27: Active External References using Publications (left) and case study EATSreferences and copied solids (right)- in CATIA V5

After the parameterisation of the product, a new part within the assembly can beinserted to create an acoustic model to be simulated. First, as for the parameterisa-tion, the necessary external parameters and references need to be pasted, as well asthe published solids. Figure 27 (right) shows the copied elements from the tree intothe newly created acoustic model.

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To create a surface model out of the copied solids, several tools such as the Extractand Offset tools within the Generative Shape Design module can be used. The partsof interest involved in the acoustic simulations are the ones in contact with the flow.Therefore, the inner surfaces need to be extracted. The acoustic simulations withinCOMSOL Multiphysics are performed on solid parts. Once again, the surfaces areused in this case in order to enable the correct application of settings and BCs bykeeping track of there denomination. Therefore, the extracted surfaces need to beconnected in order to form volumes that will be transformed into solids.

The conversions within the simulation software enables the enclosed volumes to beread as separate domains. Note that the complex geometries of silencers do nothave enclosed volume parts; the assembly of the different parts creates this enclosedvolume where the flow passes. It is therefore necessary to connect the surfacesof various parts to recreate this volume. Figure 28 shows two examples of thiscombination of surfaces.

Figure 28: Outer Volume (left) and Outer Turn (right) of the case study first boxtwo-box EATS acoustic model - modelled in CATIA V5

The Outer Volume combines surfaces from the Baffle plates, stages 1 and 2 and innerjackets. The Outer turn connects into one domain the Turn volume, allowing theflow to pass from first to the second stage, the inner pipe of stage 2 and the outletassembly comprised of a pipe and connector. It can be seen in the image on the rightof Figure 28 an empty space between surfaces of the Outer turn and the outlet. Thecylinder occupying this space is created in COMSOL Multiphysics and described inSection 4.4.5. The cylinder is created using the edge of the Outer turn and a centrepoint, both created in CATIA V5 and denominated Edge1 and Point_cyl_1.

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Linear extrusion type of acoustic simulations involve the use of transfer matricesamong the substrates of the silencer. The coordinates of the four poles at the begin-ning and end of each substrate are necessary for the simulation. Using the copiedsubstrate solids, only the inlet and outlet surfaces are extracted and the gap be-tween those and the connected parts is filled to create enclosed volumes. The firstsubstrate, the SCR, has its inlet surface in contact with the intake assembly, whilethe last substrate, the DPF, has its outlet surface in contact with the Outer Turn.Figure 29 shows the substrate surfaces and pole points created in CATIA V5 toenable the linear extrusion acoustic simulations in COMSOL Multiphysics.

Figure 29: Substrates surfaces and pole points of the first box of the two-box casestudy EATS - in CATIA V5

The last steps in creating the acoustic model of the first box of the two-box casestudy EATS are the extrusions and the inlet and outlet of the silencer, and ensurethe correct denomination has been applied to all surfaces. The extrusions are toplace the microphones at their correct location. Each extrusion is composed of foursurfaces and placed in series, i.e. two surfaces can be observed between each twoextrusions. The locations of the microphones are standardised and as follows: 300,360, 850 and 150 [mm] from the inlet and outlet. The denomination of the surfacesis crucial as it is needed by the in-built scripts within COMSOL Multiphysics toperform the conversions and apply all settings and BCs for the simulations. Table4 gives examples of the order of the objects within the tree and their respectivedenomination. All other object denominations can be found in Appendix E.

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Table 4: Object denomination examples of first box of the two-box case study EATS

Geometrical Set Object DescriptionExtrusions_1.1 Cross.section.HAR12 Cross sections to be used for the microphones

SCR_ASCSCR.1.Out_RD Outlet surface of SCRASC.1.In_RD Inlet surface of ASCSCR.12.Body Fictive volume between SCR and ASC

Points_Set_2 Dsv_1 Destination vertex SCRSrv_1 Source vertex ASC

Inlet_and_EndplateEnd.plate.body Connector - End plate body surfaceSCR.Around_RD SCR - End plate surface connectionSCR.1.In_RD Inlet surface of SCR

Points_Cylinder Points_cyl_1 Centre radius Stage 2

As an example, Figure 30 describes the logic behind the denomination of the fourpoles of the different substrates. The source (Srv_) and destination (Dsv_) verticeshave corresponding number in order for the correct substrate settings to be applied.

Figure 30: Substrate source and destination poles schematic

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4.4.5 Analysis Setup

To prepare the automatic acoustic simulations of the model, some inputs are requiredin the provided Study_Case_Script EXCEL file. Also, some settings are necessary tobe modified. First, the user should ensure that macros are enabled, that the decimalseparator used is a dot, the FileSystemObject (FSO) and all CATIA V5 referencesin the VBA Editor are enabled. The LiveLinkTM module should be installed andconnected to the provided COMSOL Multiphysics Study_Case.mph file.

The Product and all parts numbers having parameters involved in the simulationshould be entered in the indicated Info Product - Part box. This will allow thecorrect denomination of the output files. Then, all parameters to be modified inthe optimisation should be listed in the provided Input variables to be tagged box.Similarly, the outputs and responses should be defined in the Outputs and Responsesto be tagged boxes, respectively. The cell taking into account the number of substratesshould be set to 3 for the study case. The different necessary folders for the CADmodels, the batch sweep, plots and coordinates of the substrate poles should becreated and their paths entered in the provided Folder Paths box.

When all variables and responses have been entered, the different scripts can betested using the provided buttons. Note that the Run Code button activates allremaining buttons. Changes to the provided scripts can be performed in the VBAEditor of the software. For instance, specific settings for the simulations in COMSOLMultiphysics can be modified such as discretisation or the step size.

As mentioned in Section 4.3.4, the Surface-tracking code needs to be adapted toeach case including the list of parts corresponding to the silencer. This is preparedto be functional in this study case as it contains the specific parts for this type ofsilencer, therefore, the user does not need to modify anything from the scripts in theApplication Builder. To perform acoustic simulations, the two-microphone methodmentioned in Section 2.2 needs to be applied. Based on existing acoustic simulationfiles from the NXPS department at Scania, extrusions at the inlet and outlet of themodel were created within CATIA V5 in order to ensure the tracking of the surfacesand apply the correct mesh and BCs.

The simulations are performed using the physics interface for Pressure Acoustics inthe Frequency Domain. The specific boundary conditions used in this case are listedbelow:

• Boundary conditions, Domains

– 1. Poroacoustics Wool : this node defines a fluid domain with a porousmaterial as seen in Figure 31.

– 2. Narrow Region Acoustics: this fluid model mimics the thermal andviscous losses that exist in narrow tubes where the tube cross-sectionlength-scale is comparable to the thermal and viscous boundary layerthickness. This applies at the inlet and outlet extrusion pipes.

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Figure 31: Poroacoustics Wool Area indicated in blue color - in COMSOL Multiphysics

• Boundary conditions, Boundaries

– 1. Plane Wave Radiation Inlet, Incident Pressure Field : this node adds aradiation BC for plane wave, including an incoming wave at the inlet oroutlet entry. Note that the simulation is performed twice, and only oneof the entries is enabled for each simulation.

– 2. Interior Perforated Plate: this node provides the possibility of speci-fying the characteristic properties for a perforated plate. This allows forgeometrical simplifications in the CAD model.

– 3. Interior Hard Boundary Wall : this node adds a boundary conditionfor a sound hard boundary or wall on interior boundaries. A sound-hardboundary is a boundary at which the normal component of the accelera-tion is zero. Figure 32 shows the interior sound hard boundary conditionsapplied to the study case.

– 4. Normal acceleration at the inlets and outlets: this is applied at the inletand the outlet of every substrate and adds an inward normal accelerationpreviously defined in the variables.

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Figure 32: Interior Hard Soundary Wall indicated in blue color - in COMSOL Multi-physics

• Discretisation

The discretisation of the CAD model is performed within COMSOL Multi-physics. To apply an adequate mesh on the desired surfaces, the latter wereregrouped in specific geometrical sets within CATIA V5. Then, using methods(scripts) in the simulation software, the denomination of the surfaces is keptand the correct mesh is applied.

As mentioned in Section 4.2, the script for the automatic discretisation createsthe selections that are needed for applying different mesh sizes on each sur-face. Additionally, it allows the user to change their element size by modifyingthe value of the MaxE parameter. This parameter defines the maximum ele-ment size based on the highest frequency. In Table 5 the standard or defaultdiscretisation settings applied to the silencer are presented.

Table 5: Default discretisation setting applied to the study case EATS

Component Geometricentity level

Element type Max.E size

Min.E size

CurvatureFactor

Volume Domain Free Tetrahedral MaxE 4 [mm] 0.6Boundaries Boundary Free Tetrahedral MaxE/2 2 [mm] 0.6Pipe Cross-sections Boundary Free Tetrahedral MaxE/6 1 [mm] 0.6Catalyst Inlet Outlet Boundary Free Tetrahedral MaxE/6 2 [mm] 0.6

byebye Where MaxE is a parameter defined within COMSOL Multiphysics.

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A description for the different discretisation configurations is explained below.

– Boundaries: size feature calibrated for general physics problems and ap-plied on all boundaries of the model.

– Pipe Cross-sections: size feature only applied on the cross-sections facesat the inlet and outlet extrusion pipes. A refined mesh is applied to theseboundaries as the average pressure is calculated at these surfaces and usedin the calculation of TL.

– Catalyst Inlet Outlet : applied to the circular surfaces representing thecatalyst boundaries. The inlet and outlet boundaries are used in thetransfer of the acoustic properties from the inlet to outlet volume of thecatalyst through the employment of the transfer matrix method (TMM).Finer mesh settings are used on these surfaces to grasp better the physicsof the acoustic properties transfer.

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4.4.6 Optimisation SetupTo perform an optimisation on the acoustic model, a HEEDS MDO file needs tobe created. The file requires the integration of an EXCEL node linked to theStudy_Case_Script. All variables and responses need to be created and "tagged"to the parameters in the Input and Output box of the provided EXCEL file. Thename of the macro executing all the scripts needs to be added into the EXCEL nodeoptions within HEEDS MDO to perform the simulations for each iteration.

The type of study needs also to be chosen. An initial DOE study can help to evaluatethe performance of the model if the bounds of parameters are unknown or uncertain.To optimise the model, the SHERPA method is recommended when using HEEDSMDO. Based on the given time frame, the number of evaluations should be setbetween 100 and 250. At least one objective function is required to be applied to avariable response.

For the study case, three objective functions were applied to three responses. Theresponses obtained in the provided EXCEL file are averages of TL for specific fre-quency ranges: 200 to 400 [Hz ], 401 to 600 [Hz ] and 601 to 800 [Hz ], respectively.The objective functions can therefore be used to maximimise those averages. How-ever, another way is to set different fictive maxima based on the frequency rangeand minimise the difference with their respective TL averages. The fictive maximawere set to be 50, 60 and 45, respectively. Those values are based on the initialsimulation performed to test the different scripts (see Figure 34). The settings forthis optimisation are shown in Table 6.

Table 6: Global optimisation settings

Setting DescriptionMethod SHERPANumber of iterations 250Objective Functions 50/60/45 - TLType MinimisationFrequency range 10-800 [Hz ]Parameters 14 variables

It is also important to set error conditions to ensure that HEEDS MDO continues theoptimisation. First, the option Do not stop HEEDS for a design-based error needsto be selected in the Study tab. Then, the error handling can be done by assigningerror conditions to the responses. Their values can be read erroneous by assigning arange of plausible values. Based on the fictive maximal, the responses are acceptedif their values are within 0.1 to 49.5, 0.1 to 59.5 and 0.1 to 44.5, respectively.

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Besides from the global optimisation in HEEDS MDO, a local optimisation is per-formed within COMSOL Multiphysics. In this local optimisation, the perforatedplate length is varied and evaluated according to the objective function. The set-tings for this optimisation are shown in Table 7.

Table 7: Local optimisation settings

Setting DescriptionMethod BOBYQAOptimality tolerance 0.01Max number of model evaluations 1000Objective Function 60 - TLType MinimisationControl Variable: L_test 0.045 - 0.065 [m]Initial L_test value 0.054 [m]Frequency range 105 - 305 Hz, step of 5 [Hz ]

The objective function is set to minimise the difference between a fictive maximumvalue equal to 60 [dB ] and the original value for the TL. Thereby, the optimised TLcurve will be above the original one as the objective is to increase the TL value. Theperforated length L_test, where the interior perforated plate condition is applied, isshown in blue in Figure 33.

Figure 33: Cylinder where perforated plate condition is applied - in COMSOL Multi-physics

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4.4.7 Result ReviewThe first result that can be verified is the TL plot obtained with the EXCEL andCOMSOL Multiphysics files. The verification is performed by simulating the samepart in the opposite direction. The results for the simulation with the plane waveradiation from the inlet (A) and the outlet (B) should match. Figure 34 shows thesame curve for simulations from A to B and B to A.

Figure 34: TL plots for the first box of the two-box case study EATS, from A to B(top) and B to A (bottom) - in COMSOL Multiphysics

The optimisation study was first run for a number of 250 designs with 177 feasibledesigns. Each iteration took about 30 [min. Note that the local optimisation wasomitted. The responses observe a small reduction: 0.42, 3 and 2 % respectively.Despite the small differences in TL, correlation matrices were plotted to observethe inter-dependencies among the parameters as well as their effect on the responses.Figure 35 shows the correlation of the different variables with Average_difference_3,which corresponds the average frequencies for the 601 to 800 [Hz ] range.

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Figure 35: Correlation matrix for the case study EATS without applied local optimi-sation - in HEEDS MDO

In the correlation matrix above, it can clearly be pointed out that Stage_2_diameter,Outlet_flange_width and outlet Lower_in_tension are inversely proportional to theaverage TL difference for the 601-800 [Hz ] interval. In other words, these param-eters are proportional to the TL, meaning that when Stage_2_diameter increases,the TL does as well. The matrix also reveals the strong correlation among someparameters. For instance, the Outer_flange_width is strongly proportional to theStage_2_diameter. Table 8 links the parameters used for the optimisations withtheir respective product in the tree. Note however that most of the parametersaffect more than one product in the assembly.

A second optimisation loop was run for a number of 150 designs with the samevariables and responses but including a local optimisation for the perforated platelength, named L_test. Each iteration took about 40 [min. A total of 51 modelswere returned as erroneous designs. Similarly to the first case, the TL values wereonly maximised of 1%. The local parameter within COMSOL Multiphysics regardsthe heights of the two cylinders in Stage 2 of the case study, where only the secondcylinder observes a perforated plate BC. The other cylinder is assumed to have ahard sound BC.

The correlation matrix for the study with the local optimisation can be seen inFigure 36. Once again, it is shown how Stage_2_diameter is inversely proportionallyaffecting the average TL difference for the 601-800 [Hz ] interval.

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Table 8: Correlation matrix parameter correspondence

Number Product NameR2 - Average_difference_2R3 - Average_difference_3P1 Stage 1 Stage_1_diameterP2 Stage 2 Stage_2_diameterP3 Baffle Plate Assembly Outlet_flange_widthP4 Intake Assembly Stage_1_tension_topP5 Intake Assembly Mid_section_angleP6 Intake Assembly Stage_1_tension_bottomP7 Intake Assembly Stage_1_start_lengthP8 End Plate Assembly Inner_dispenser_offsetP9 Stage 2 Blade_curve_angleP10 Outlet Pipe Assembly Outlet_interface_protrusionP11 Outlet Pipe Assembly Upper_in_tensionP12 Outlet Pipe Assembly Lower_in_tensionP13 Outlet Pipe Assembly Left_in_tension

Other correlations can be noted from this matrix, for example, theMid_section_angleand the Inner_dispenser_shape_offset have a direct proportionality relation affect-ing the average TL difference.

Figure 36: Correlation matrix for the case study EATS with applied local optimisationin HEEDS MDO

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The results of the local optimisation in COMSOL Multiphysics are shown in Figure37. The initial value of L_test is equal to 0.054 [m] and the original TL curveis seen in two different colours (green and red) as they belong to different rangesof frequencies in the batch study, on the other hand, the optimised curve for theL_test equal to 0.065 [m] is show in blue. It can be noted that the curve has shiftedup around 2 [dB ] in the top value at the peak. Therefore the longer the cylindercontaining a perforated plate is, the more the transmission loss is increased.

Figure 37: Local optimisation for L_test at the perforated plate

4.5 Chapter SummaryThis chapter listed all the results obtained throughout the thesis work. First, themajor points discussed in the interviews conducted at Scania were described, empha-sising on the product development process and interaction between engineers fromthe design and simulation departments. Then, the developed method was presentedin the form of step-by-step guidelines. Finally, the method was applied to the firstbox of the two-box EATS case study, detailing all stages from the Pre-CAD phaseto the Optimisation of the product.

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5 DiscussionThis Chapter emphasises on discussing the proposed method and its applicationto the study case. Note that the different points in the discussion are particularlyapplicable on the first box of the two-box case study silencer as the objective was todemonstrate the possibility to optimise this particular product.

5.1 AssumptionsIt is important to note that several assumptions were made in order to conductthe thesis work. Those assumptions regard the acoustic simulations performed inCOMSOL Multiphysics. First, the obtained TL results for the first box of the two-box case study EATS study case were performed at standard 380 [◦C] condition andfor a frequency range of 10 to 800 [Hz ]. Using previous products as a reference, thefrequency range is based on the interval where EATS noise is dominant compared toother sources and affecting the most the cabin noise.

Then, another assumption regards the omission of flow through the silencer. In fact,the acoustic simulations assume a static state with no interaction of the exhaust gasesflowing through the EATS. In other words, internal aeroacoustics are not consideredin this case and experimental data from tests might differ from the simulation results.

The acoustic model created from the original geometry assumes that all parts areempty volumes and converted to solids within the simulation software. While reduc-ing computational costs, this could have in turn affected the results as only the innersurfaces were taken into account.

5.2 SimplificationsComplex products containing a multitude of parts such as silencers can easily gener-ate errors at both design and simulation levels. The simplification of the geometryis necessary and based on the discipline to be studied. The first main simplificationregarding the study case was the omission of the second box of the two-box casestudy EATS. This simplification allowed great reductions in computational costs, asthe two boxes of the silencers are complex products, and on the parameterisationallocated time in the CAD modelling phase. The obtained acoustic performancethrough the TL output was therefore partial as the second box was not incorporatedin the simulations.

Another simplification involved the omission of parts not in contact with the flow suchas outer jackets and brackets. Additionally, smaller parts in contact with the flowwere considered negligible. These parts include, among others, flanges and air flowtwisting plates. Then, the inlet and outlet protrusions with a larger cross-sectiondiameter were omitted and a constant diameter at the connectors was assumed.The Outer Volume and Outer Turn parts were created from existing components asenclosed volumes to permit the conversion to solids within COMSOL Multiphysics.

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Some surfaces were slightly modified to allow the sealing of the parts. Overall, thegeometrical simplifications reduced the computational time to the expenses of theaccuracy of the results.

Lastly, the acoustic simulations were only performed in one direction, i.e. fromthe inlet to the outlet of the model. At NXPS, the simulations are required to beperformed twice, in opposite directions, to verify the obtained results. However,the optimisation loop being computationally expensive, the proposed method takesinto account only the simulations in one direction. It is therefore recommended tomanually simulate the optimised model or models in the opposite direction after thecompletion of the optimisation.

5.3 Delimitations and LimitationsAs described in Section 1.4, the project involved the use of the software CATIAV5, COMSOL Multiphysics and HEEDS MDO as they are employed at Scania.The connectivity among the latter software however greatly affected the performedwork and proposed method. Indeed, the current compatibility between CATIA V5and COMSOL Multiphysics could be improved in the same manner it is currentlyapplied to other modelling software via the use of LiveLinksTM. This would simplifythe surface tracking step. The HEEDS MDO node for CATIA V5 did not offer theoptions required for the simulations to be run and had to be bypassed by the use ofEXCEL and VBA scripted macros to trigger the modification of the geometry for eachiteration. Similarly, HEEDS MDO does not offer a node connection with COMSOLMultiphsyics and the connection was bypassed via the EXCEL LiveLinkTM. Hence,the method could be simplified if the connection among those software is improved.

The thesis time frame is an important limitation for this project. In fact, as theknowledge on the product and simulations evolved, changes possibilities decreased.Note that this relates to the design paradox mentioned in Section 2.5: the costsof design modifications increase as the product development process evolves. Theacoustic simulations in COMSOL Multiphysics are complex and computationallyexpensive and were performed on two design engineering computers. The retrievalof results was therefore delayed due to fine tuning in the scripts and settings towardsthe end of the allocated timeframe.

5.4 Accuracy ResultsThe obtained TL plots in both direction are identical, verifying the set up for theacoustic simulations. The development target is not met throughout the whole fre-quency. However, this target is valid for a complete EATS and the obtained resultsfor the study case represent the performance of only the first box of the two-box sys-tem. On its own, the first box observes a TL curve with relatively high values andthe combination with the second is expected to be above the development target.

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The optimisation method using different objective functions with fictive maximaseems promising despite the small increase in TL values. Based on the knowledge atScania on the acoustic performance of its products, more complex functions couldbe applied to maximise TL over the entire frequency range. Note also that the usedbatch sweep for the simulations of the study case was set to run in parallel eightsimulations, dividing the range in equal intervals of 100 [Hz ]. The number of thesebatches can be modified and the intervals changed in such way to emphasise thesimulations on the most critical frequencies.

The local optimisation applied to the perforated plate in Stage 2 of the study casewas run on each iteration for a smaller interval after the general simulation con-ducted. The results provided an optimised curve only for this specific interval as anoptimisation for the total range of frequencies would be very computationally expen-sive. The performance of the silencer could then be analysed with the new optimisedL_test value and investigate the effects, if any, on the rest of the frequency range.

5.5 Work reflection and potential alterationsBased on the outcomes of the thesis work, some considerations could have beentaken into account to implement the local optimisation in the loop at an earlier stage.This would have allowed to obtain results and confront arising issues earlier on in thedevelopment of the method. The selection of the L_test parameter for the perforatedplate length BC was used as an example and the optimisation was only performedover a smaller interval. The study aimed at retrieving information on the parameterwhile attempting to optimise the TL. The accuracy of the different algorithms werenot evaluated, and further constraints on the objective function could have beenapplied. Several objective functions could also have been implemented but wouldhave affected in turn the computational costs.

A deeper knowledge on acoustics and their simulations within COMSOLMultiphysicscould have helped in formulating the proposed method at a faster pace. Moreover,the self teaching period in scripting using the Java programming language delayedthe process to develop the in-built script in the Application Builder of the simulationfile.

Potential changes in the method could have involved the inclusion of the softwareANSA within the loop. ANSA is often used within NXPS for discretisation purposesand allowed the previous thesis work to keep track of the surfaces when importingthe model into COMSOL Multiphysics. However, this implied adding an additionalstep in the complex structure. The new features of the software allowed the proposedmethod to be simpler and discretise the geometry within the simulation software.

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6 ConclusionsReflecting on the work, the posed research questions need to be addressed and an-swered, and conclusions are drawn about the method implementation at Scania.

6.1 Research Question 1RQ1 - How can a parametric CAD model of an EATS be modelled to automatesimulations of acoustic performance?

The proposed method offers the possibility to automate acoustic simulations forEATS and several requirements need to be taken into account. The first and mostimportant step is ensuring the connectivity between the modelling and simulationsoftware. This is enabled by the introduction of a connecting software allowing thecorrect import of CAD geometry and automatic application of simulation settingsincluding discretisation and BCs.

The denomination of the surfaces involved in the simulation are crucial to the correctimplementation of the method. With a correct name assigned within the modellingsoftware, the scripts in the proposed method are able to keep track of the surfacesthat are imported into the simulation software.

Parametrisation of the CAD model to be simulated is necessary for automatic sim-ulations to be performed. Depending on the type of environment, the correct toolsneed to be employed to ensure flexibility and robustness of the design. The extractedacoustic model to be simulated needs to follow the morphological modifications ofthe product and ensure to be sealed using enclosed volumes.

The study case proved that by following the developed method, acoustic simulationscan be automated for a EATS subjected to morphological changes and topologicalmodifications are also possible to be integrated within the proposed loop.

6.2 Research Question 2RQ2 - How can the developed method for the simplified case be applied to acomplex geometry in the industry?

The collaboration with the other master thesis workers allowed the development ofa method introducing MDO and simulation driven design into the product develop-ment phase. This common method was then derived into the different disciplines andapplied to the same study case among the theses. The proposed method presentstools to simplify and optimise the interaction between design and simulation engi-neers. By automating simulations, engineers are able to emphasise their work ononly feasible designs, reducing time investments in design modifications.

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The complexity of the study case demonstrates that the proposed method for au-tomatic acoustic simulations can be applied to EATS in the industry. Despite thefocus of the performed work being on the first box of a two-box EATS, the developedmethod can be altered to meet the requirements of other silencers and simulate theacoustic performance of these latter products. This method could therefore be usedas a template for automatic acoustic performance of silencers. The provided filesare designed for the study case but the settings and other details can be changed bymodifying their in-built scripts and interfaces to accommodate for other geometriesand types of simulations.

6.3 Research Question 3RQ3 - How efficient is the proposed methodology in terms of time expensescompared to the methodology currently in use?

The method currently in use offers the possibility to perform automatic acousticsimulations of an acoustic model by conducting DOE studies. The most expensivestep in terms of time is the initial set up as the method involves a multitude ofsoftware to be connected. The proposed method offers a simplified decompositionarchitecture where the discretisation is performed within the simulation software.The reduction of software in use reduces the possibilities of connectivity and mesherrors.

The time expenses for the proposed method are relative. Design engineers experi-enced with CAD parameterisation and sufficient knowledge on acoustics could imple-ment this method faster. In fact, the most time demanding tasks regard the correctcreation of an acoustic model and applying the specific surface denominations.

6.4 Research Question 4RQ4 - How well does the proposed methodology allow for design optimisationin terms of acoustics?

Optimisation is applied at two levels in the proposed method: globally and locally.The variables and responses involved in the optimisation software regard parametersaffecting all disciplines. These global parameters allow morphological changes in thegeometry of the product. The optimisation consists in exploring the design spaceby determining the best parameter combinations addressing the set objectives andconstraints. The method allows the user to decide which parameters, as well as whichresponses is the product to be optimised for. Therefore, the method can be adaptedto different optimisation types.

The proposed method also allows the introduction of an optimisation at a local level.In fact, for each iteration, the user is able to optimise a parameter in the geometryor simulation settings that only affects acoustic performance.

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The study case demonstrated that a EATS can be optimised at both global levelwith morphological changes in the geometry and local level with the addition ofan optimisation study within the simulation software. An engineer could thereforebe aware of possible design changes to be made to enhance the performance of themodel.

6.5 Method implementation within ScaniaThe concept development phase of a silencer can be long and the proposed methodcould be implemented to reduce the time spent in conceiving and filtering conceptmodels based on their simulation results. The MDO process offers gains in time andfocusing on feasible designs. This would reduce the "back and forth" action betweenthe design and simulation departments. Repetitive tasks such as design changes thatare necessary to either run the simulations or improve the obtained results would beeliminated or greatly reduced.

Another benefit of the implementation of this method at NX would be to identifyearlier in the design process feasible designs to be tested physically. Indeed, the man-ufacturing of prototypes can be costly and time consuming. Reducing the number ofconcept models would decrease the experimental tests required to approve a designfor production and allow to move faster on into the next stages of the developmentprocess.

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7 Future WorkThe thesis work performed at Scania has some aspects which can be improved in orderto make the method less time-consuming and thus more efficient for the department.In this way, some topics which could be improved as well as future work to be furtherinvestigated is presented in this chapter.

First, the EXCEL interface currently used could be redesigned to be more user-friendly. The workbook has been modelled to operate for the study case and someadjustments could be done to generalise the presented tool. In fact, the user is re-quired to manually input all the necessary parameters. The scripts could be improvedto automatically find the parameters within CATIA V5.

Similarly, the COMSOL Multiphysics scripts were specifically created to meet thetwo-box EATS study case requirements. Simulation settings could also be modifiedor adjusted via the EXCEL interface. For instance, discretisation type and quality,step size and assigned number of batches could be entered as inputs prior to runningthe optimisation.

As mentioned in Chapter 5, the time was a limitation in the project, and it has beennoted that starting the local optimisation in an earlier stage would have allowed toinvestigate further configurations and ways of optimising TL by, for instance, tuningthe objective function in order to obtain more accurate results on the optimised curve.Furthermore, other optimisation algorithms available within COMSOL Multiphysicscould be tested.

The coordinates of the poles of each substrate are extracted and integrated within thesimulation software by the means of three different software. Despite the rapidityof the process, this crucial step for linear extrusion acoustic simulations could besimplified and improved by possibly involving only COMSOL Multiphysics.

The performance of silencers are today evaluated at NXPS by determining the inser-tion loss (IL). Although the thesis aimed at optimising TL, a further investigation onIL through COMSOL Multiphysics and integrating it within the optimisation loopcould be a matter of interest to evaluate the performance of the silencer. NXPS cur-rently determines IL via the software SIDLAB with the TL results from COMSOLMultiphysics. Therefore, research in including this additional software within theproposed loop could be carried out.

The proposed method was made possible thanks to the connectivity improvementsamong the different software involved. As a recommendation, a close contact withthe support team from COMSOL Multiphysics should be kept to be informed onnew features and improvements that could be of benefit to simplify and enhance theperformance of the proposed method.

Finally, the work performed by all the thesis workers within the NXD organisationduring the same allocated time frame could be combined. Thus, providing simulta-neously results across different disciplines for a common EATS.

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for Distributed After-Treatment System with Focus on CFD Simulation”. MAthesis. Scania CV AB; Linköping Universitet, Division of Machine Design, 2017.

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[16] Manchar Lal Munjal. Acoustics of Ducts and Mufflers. 2nd ed. Wiley, 2014.isbn: 978-1-118-44308-8.

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[28] Versteeg H K and Malalasekera W. An Introduction to Computational FluidDynamics. 2nd ed. Pearson, 2007. isbn: 978-0-13-127498-3.

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http://www.redcedartech.com/pdfs/SHERPA.pdf

A First Appendix - Risk AnalysisTable 9 below summarises the possible risks regarding the thesis, their likehood tohappen and severity on the work, as well as the actions taken to mitigate them.

Table 9: Thesis Project Risk Analysis Table

Risk Description Likelihood Severity Actions to mitigate

Issues importing CADmodels into COMSOL

Plan back up CADmodel import via sec-ondary software

Failure to discretisemodels in COMSOL

Use previous developedmethodology via MatlabLiveLinkTM

Incorrect interactionamong software withinthe iterative loop

Gain of deeper technicalknowledge after consul-tation with software ex-perts at Scania

Failure obtaining the in-sertion loss for the cre-ated models

Prepare method back upmodification based onprevious thesis work

Failure to optimise theCAD models withinCOMSOL based on theacoustic results

Back up optimisationsplanning using HEEDSsoftware

Final results not ob-tained within the giventime-frame

Thesis planning includ-ing methodology devel-opment and time plan(Gantt Chart)

Where the green, yellow and red cells represent low, medium and severerisks/severities, respectively.

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B Second Appendix - InterviewGuide

Agenda

• Present the students and the master thesis being undertaken as well as thereasons for having the person interviewed

• Ask interviewee if he/she minds being recorded

• Interview questions

Interview questions

BACKGROUND INFORMATION ON THE INTERVIEWEE

• What is your position and role at Scania?

• Where would you place yourself and how would you describe your role withinScania’s development process? (concept development, product development,follow-up)

• Could you briefly explain the design process of a silencer from your perspective?

• Have you had any working experience or knowledge on other departments thatyou interact with?

NXD(E) & NXPS RELATIONSHIP

• Do you think the collaboration between the two divisions is fluent/efficienttoday? If not, do you have any suggestions to improve it?

• How long does it take for a model to be prepared for CFD/acoustic simulations?

• How does the simulation results contribute to design changes in the conceptdevelopment process? What if the simulation results are unsatisfying?

MULTIDISCIPLINARY OPTIMISATION (MDO)

• Are you familiar with MDO? If so, is it used at NXD(E)/NXPS?

• How do the different disciplines at the simulation department collaborate amongthem?

SPECIFIC QUESTIONS FOR DESIGN ENGINEERS

• What are the things to keep in mind when designing/updating a CAD model?

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• Does the combination of everchanging regulations and performance demandsaffect the creativity and innovation of the products at Scania? How originalare the new concept ideas?

SPECIFIC QUESTIONS FOR CFD ENGINEERS

• What information or/and characteristics are considered necessary to be in-cluded in the CAD models in order to be robust models for CFD simulations?

• Is your role within the CFD department also involved with the concept devel-opment phase? If so, what inputs do you bring?

SPECIFIC QUESTIONS FOR ACOUSTIC ENGINEERS

• How does the preparation of CADmodels for acoustic simulations differ/resemblefrom the one for CFD simulations (for instance: models with only surfaces)?Any particular points to be taken into consideration, such as walls allowing forsound propagation?

• Based on previous acoustic simulations, what are some of the design parame-ters mostly affecting the insertion/transmission loss throughout the frequencyrange?

MISCELLEANOUS

• Who do you think we should interview next?

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C Third Appendix - InterviewsSummary

This Appendix focuses on summarising the findings from the interviews conductedat Scania. The interviews took place with employees from the following depart-ments: Exhaust Systems Inline Engines (NXDX) and Exhaust Systems V8 Engines(NXDE) from the Exhaust System Design (NXD) organisation, and Fluid Dynamicsand Acoustics Simulations (NXPS) from the Exhaust After-treatment Performance(NXP) organisation. Note that both NXD and NXP belong the Emission SolutionsDevelopment (NX) organisation. A total of seven people were interviewed, whichincluded design and simulation engineers as well as former and current thesis workeremployees.

Product Development ProcessAs mentioned in Section 2.5, the product development process at Scania consistsof three main steps denoted, in order, yellow, green and red arrows. The conceptdevelopment phase (yellow arrow) begins when the new project has been introduced,approved and assigned the correct budget. Once the new product is ready to beindustrialised, the project enters the green arrow phase. When the project ends, i.e.the product has been well implemented into the market, the red arrow phase allowsfor providing solutions to deviations of the product. Note that the responsibility toprovide solutions depends on the nature of the surging issue such as production andmanufacturing or field deviations for instance.

In Section 2.2, the four major sources contributing to the pass-by noise emitted byHCVs are described. However, the noise regulations apply to the entire vehicle andhence the budget attributed to reduce the vehicle noise pollution is divided amongthe different concerned departments. In order to achieve the desired performance,the different departments are given development targets. It is then up to each organ-isation, in this case the NX organisation, to combine efforts and compromises amongthe disciplines to meet those requirements.

When creating a new model, the design engineers need to ensure that the require-ments set by Scania are fulfilled. Having parametric models facilitates the design-simulation iterative process. Therefore, full parametrisation in order to make quickchanges in the geometry is often desired but depends on the given time frame. Notethat the originally created models and simulated models observe significant differ-ences. Simulation models are simplified, their geometry is cleaned and some featuresthat do not affect the results are removed to reduce computational time.

Both design and simulation engineers are required to take introduction and follow-up courses regarding their specialisation. Other tools and guidelines are available tounderstand the creation of models procedures and their preparation for simulationsuch as creating surface models.

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Design and Simulation departments interactionOverall, the collaboration between the design and simulations engineers is deemedgood and effective amongst the interviewees but could be improved in some aspects.Meetings among the different disciplines occur every week, some on a nearly dailybasis, and can interrupt the work flow of some employees. Instead, the communica-tion between the design and simulation engineer working on a particular topic of aproject could be enhanced.

When a design needs to be simulated, the CAD model requires to be prepared for thespecific simulation. This preparation and cleaning of the model is usually performed50-50 between the design and simulation engineer. This creates however a "back andforth" action between the departments slowing down the development process. Therequired time to prepare a model for a particular simulation is therefore uncertain(from a few hours to several days) and related to both the design and simulationengineer experience. Guidelines to follow simulation model preparation for CFDperformance have been recently introduced and allowed for a significant reductionin time expenses. Based on this method, the design engineers aim at obtaining onecommon model that can be then be adequately modified depending on the simulation.

Another aspect regarding the preparation of files for simulation is the unavailabilityof some resources based on the work department: design engineers for instance do nothave access to simulation tools. Despite reducing licensing costs, design engineers areunable to test models and assess their quality before sending them to the simulationengineer for a thorough analysis.

MDOSolving engineering problems requires the joint effort among different disciplines. De-sign and simulation engineers work closely with test engineers to validate the resultsobtained through simulation, but the synchronisation with the remaining disciplinescould be increased. The NX organisation at Scania follows a simulation driven designprocess (Taktat Flöde). This iterative process starts with the pre-selection of inputspecifications (Preliminary Content Freeze, PCF). Once approved (Content Freeze,CF), the design phase starts in which the adequate geometrical features modifica-tions are performed. The design is then "frozen" (Design Freeze, DF), assessed andapproved to proceed with the next step. This analysis phase comprises all the re-lated conducted simulations and finishes with a review and evaluation of the obtainedresults (Result Review, RR) that closes the loop. This can be seen as a "manualMDO" process among the different disciplines within the NX organisation.

While the close collaboration among the different departments within the NX organ-isation at Scania is essential, there is no thorough communication with departmentsfrom other organisations such as with the Engine Development (NM) organisationfor example, which can lead to unpredicted issues. When it comes to designing anEATS, the inlet flow velocity coming from the engine for instance, as well as the airproperties need to be taken into consideration. These are however not fixed valuesand there is no communication between the concerned departments in order to makethe correct adjustments and compromises.

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Instead of focusing on one major system of the vehicle, a stronger collaborationamong all involved organisations could be established to determine parameter corre-lations and consequences of design features modifications.

Acoustics and related simulationsTime needs to be allocated to prepare the CAD models to be simulated for thedifferent disciplines. Based on the gathered information from the interviewees, thepreparation for CFD and acoustic simulations is quite similar. First, the unnecessaryparts of the geometry are removed as the study concerns only internal flow. Similarly,acoustic performance is simulated for internal volumes. Then, simplifications in thegeometry include the removal of sharp edges and covering small tolerance gaps. Thisallows for a faster and smoother discretisation process. Note that importing a CADmodel into a simulation software needs to be performed with care as some formatconversion issues might arise such as surface overlapping or surface denomination(ID) tracking. Finally the adequate types of mesh and correct boundary conditionsneed to be applied in order to better grasp the physics and ensure the simulationsare realistic, respectively.

The pre-simulation phase allocated time depends on the experience of the designand simulation engineers as well as their collaboration. The introduced guidelinesfor CFD simulations CAD preparation allowed to reduce the model revision andsimulation setup time expenses. There is however no such document for acousticsimulations and a certain lack of knowledge on acoustics remains among the designengineers. Currently, no deemed "good" method has been applied to discretise thegeometry. While some simulation engineers prefer importing the CAD model directlyinto COMSOL and then make the correct adjustments, others prefer performing thediscretisation process in ANSA, which adds an extra step and second file conver-sion. Note that for both CFD and acoustic simulations, the CAD models must becomposed of surface objects only.

Acoustic simulations consist of evaluating the pressure difference from the inlet tothe outlet of the EATS. This difference is then used to calculate the transfer matrix,which can be defined as the relationship between the inlet pressure and volumevelocity and outlet pressure and volume velocity of a model. Or in other words, it isa 1D representation of the pressure variation through the EATS. The two microphonemethod is applied: two microphones are placed before the inlet and after the outlet ofthe silencer. The simulations are run twice, from the inlet to the outlet and inverselyin order to ensure that the pressure difference obtained is equal. Differences might becaused by an incorrect simulation setup or geometrical instabilities. The simulationare performed for a frequency range of 10 to 800 [Hz] with a step size of 2 [Hz] andrun for 20 and 380 [◦C].

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D Fifth Appendix - Simplifiedone-box silencer

This Appendix describes the application of the proposed method to a simplified one-box EATS. This model was used to verify the feasibility of connecting the differentsoftware involved in the method and perform an optimisation. Figure 38 showsthe created geometry of the simplied silencer model in CATIA V5. Note that theextrusions at both the inlet and outlet have been removed for visual purposes.

Figure 38: CAD model geometry of the simplified one-box EATS (without extrusions)- in CATIA V5

Table 10 below shows the different parts that were taken into consideration for acous-tic simulations. Note that all these parts are in contact with the exhaust gases.

Table 10: List of simplified silencer parts included in the acoustic simulations

Name DescriptionInlet Connection to exhaust pipe from enginePipe 1 Connection between Inlet and End plateEnd plate 1 Outer wall of one-boxTube 1 Outer wall of substrateTube 2 Outer wall of substrateEnd plate 2 Outer wall of one-boxPipe 2 Connection between End plate and OutletOutlet Connection from exhaust pipe to the atmosphere

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The parts listed in Table 10 are parametrised, linked among them with relations andformulae. The separation between the tubes in Figure 38 was designed to mimicthe modelling of a substrate for acoustic simulations. For the same reasons, the fourpoles on both parts are explicitly represented by points. Those points are saved intwo geometrical sets, allowing the provided EXCEL file to extract their coordinatesin the design space.

The product and part names and numbers are inputted into the user interface ofthe provided EXCEL file, as well as the input variables and output responses tobe tagged into the optimisation software. The different boxes in the provided fileindicate where the parameters must be defined. The four different scripts perform, inorder, the following actions: geometry modification and generation of acoustic partmodel, extraction of substrate poles coordinates, screen capture of the geometry,and automatic acoustic simulation. As results, the TL plot is extracted, and all TLvalues saved within a table with a denomination specific to each CAD geometry.

Figure 39: Discretised simplified one-box EATS (without extrusions) - in COMSOLMultiphysics

Figure 39 shows the discretised geometry of the simplified one-box EATS within thesimulation software. The extrusions have been removed for visual purposes. Thesimulation is run for a temperature of 380 [◦C] over a range of 0 to 800 [Hz ]. Usinga batch sweep, the frequency range is divided by eight and simulations are run inparallel for the new intervals. This allows an important reduction in computationalcosts. An example of a TL plot for the simplified silencer can be seen in Figure 40below. Note that the number of batches is represented by the variable G.

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Figure 40: TL plot of a simplified one-box EATS - in COMSOL Multiphysics

The different parameters involved in the optimisation for the simplified one-boxsilencer are listed in Table 11. The same variables are created within HEEDS MDOand the values are "tagged" in the EXCEL node. Only one response was included forthe optimisation loop. This value represents the average TL over the entire frequencyrange. A maximising objective function is applied to this response.

Table 11: List of parameters of the EATS utilised in acoustic optimisation loop

Name Description RangeInlet length Distance from end to end of the Inlet 17 < 20 < 25 [mm]Inlet radius First extrusion radius 20 < 22 < 25 [mm]Inlet second radius Doughnut-shaped sweep 22 < 25 < 27 [mm]Pipe length Distance from the Inlet to the End plate 40 < 50 < 60 [mm]Pipe radius Pipe exit radius 35 < 40 < 45 [mm]Pipe angle Angle between Inlet and End plate (w.r.t. y axis) 30 < 45 < 60 [◦C]End plate length Distance from Pipe to Tube 8 < 10 < 15 [mm]End plate radius Outer wall radius 75 < 80 < 85 [mm]End plate second radius Extrusion wall radius 82 < 85 < 90 [mm]Tube length Distance from End plate to second Tube 80 < 100 < 140 [mm]Tube inner radius Inner wall radius 75 < 80 < 82 [mm]

Note that the value between the upper and lower bound of each parameterrepresents its baseline value.

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E Sixth Appendix - ObjectDenomination

Table 12: Object denomination of first box of the two-box case study EATS

Geometrical Set Object DescriptionExtrusions_1.1 Cross.section.HAR12 Cross-sections surface first microphone from inlet

Extrusions_1.2 Cross.section.HAR11 Cross-sections surface first microphone from inletCross.section.HAR22 Cross-sections surface second microphone from inlet

Extrusions_1.3 Cross.section.HAR21 Cross-sections surface second microphone from inletCross.section.HAR32 Cross-sections surface third microphone from inlet

Extrusions_1.4 Cross.section.HAR31 Cross-sections surface third microphone from inletFirst.inlet Cross-sections surface furthest from inlet

Extrusions_2.1 Cross.section.HBR11 Cross-sections surface first microphone from outlet

Extrusions_2.2 Cross.section.HBR12 Cross-sections surface first microphone from outletCross.section.HBR21 Cross-sections surface second microphone from outlet

Extrusions_2.3 Cross.section.HBR22 Cross-sections surface second microphone from outletCross.section.HBR31 Cross-sections surface third microphone from outlet

Extrusions_2.4 Cross.section.HBR32 Cross-sections surface third microphone from inletLast.Outlet Cross-sections surface furthest from outlet

SCR_ASCSCR.1.Out_RD Outlet surface of SCRASC.1.In_RD Inlet surface of ASCSCR.12.Body Fictive volume between SCR and ASC

ASC_DPFASC.1.Out_RD Outlet surface of ASCDPF.1.In_RD Inlet surface of DPFASC.DPF.Body Fictive volume between ASC and DPF

Points_Set_1

Srv_1 Source vertex SCR NorthSrv_2 Source vertex SCR WestSrv_3 Source vertex SCR SouthSrv_4 Source vertex SCR East

Points_Set_2

Dsv_1 Destination vertex SCR NorthDsv_2 Destination vertex SCR WestDsv_3 Destination vertex SCR SouthDsv_4 Destination vertex SCR EastSrv_1 Source vertex ASC NorthSrv_2 Source vertex ASC WestSrv_3 Source vertex ASC SouthSrv_4 Source vertex ASC East

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Geometrical Set Object Description

Points_Set_3

Dsv_1 Destination vertex ASC NorthDsv_2 Destination vertex ASC WestDsv_3 Destination vertex ASC SouthDsv_4 Destination vertex ASC EastSrv_1 Source vertex DPF NorthSrv_2 Source vertex DPF WestSrv_3 Source vertex DPF SouthSrv_4 Source vertex DPF East

Points_Set_4

Dsv_1 Source vertex DPF NorthDsv_2 Source vertex DPF WestDsv_3 Source vertex DPF SouthDsv_4 Source vertex DPF East

Inlet_and_Endplate

End.plate.body Connector - End plate body surfaceSCR.Around_RD SCR - End plate surface connectionSCR.1.In_RD Inlet surface of SCRPipe.Inlet Cross-section surface inlet surface

Points_Cylinder Points_cyl_1 Centre radius Stage 2

Outer_turnDPF.Around_RD DPF - Baffle plate surfaceExtract.200_Hard_boundary Baffle plate surfaceEdge.1 Cylinder 1 origin

Inner_Insulation_Stage_2 Perforated_plate_1 Flow - Outer turn contact surfaceHealing.57_Hard_boundary Insulation - Outer volume contact

Mixer_Hard_boundary All surfaces Flow - urea mixer

Guide_plate

Extract.579_Hard_boundary_RD Guide plate outer surfaceExtract.584_Hard_boundary_RD Guide plate outer surfaceExtract.586_Hard_boundary_RD Guide plate outer surfaceHealing.36_Hard_boundary_RD Guide plate outer surfaceHealing.37_Hard_boundary_RD Guide plate outer surfaceHealing.38_Hard_boundary_RD Guide plate outer surfaceVolcano_RD Evaporator perforated surface

Note that only the objects with specific denomination in the acoustic model for thestudy case EATS have been listed.

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