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EHTC 2008 in Strasbourg

Duct Design OptimizationDuct Design Optimizationof an of an HVACHVAC* System* System

Using Fast CFD SolverUsing Fast CFD SolverSC/TetraSC/Tetra

Figures Courtesy of Denso Co.

**HHeating, eating, VVentilating and entilating and AAirir--CConditioningonditioning


1. Company Information


Software Cradle Co., Ltd.Software Cradle Co., Ltd.

Company Information

44


Cartesian MeshCartesian Mesh

Data Cleaner and TranslatorData Cleaner and Translator

Unstructured MeshUnstructured MeshMultiMulti--purpose CFDpurpose CFD

Cartesian MeshCartesian Mesh

MultiMulti--purpose CFDpurpose CFD

Construction / Architectures Civil engineering / ChemicalElectrical Appliances / Electronics

Automotive / Machinery Electrical AppliancesConstruction / Bio-chemicals

•Data Translation•Data Cleaning•Data Modification

MAIN INDUSTRIES:MAIN INDUSTRIES:MAIN INDUSTRIES:

MAIN INDUSTRIES:MAIN INDUSTRIES:MAIN INDUSTRIES:

Company Information


SC/Tetra37%

STREAM36%

Others21%

Chemistry6%

Engineering16%

Construction4%

Electricity16%

Machinery12%

Automobile25%

Company Information


2. Introduction to SC/Tetra


CFD as a ShopCFD as a Shop--floor Technologyfloor Technology

Introduction to SC/Tetra

Design engineers can use SC/Tetra as a tool to establish a hypothesis on how things happen

With:

- User-friendly Interface and Usabilityfor those who don’t have time to read through manuals

- Powerful Automatic Mesherfor those who don’t use CFD very often

- State-of-the-art Postprocessorfor understanding thermo-fluid behavior intuitively

And more,

- Outstanding computational speed and accuracy

- Low memory consumption


User Friendly Interface and Usability -- NavigationNavigation



Powerful Automatic Mesher– Powerful Powerful MesherMesher

Better Accuracy!



解適合解析

The 3rd and 4th results are almost the same.

This shows that about 420,000 mesh elements with resolution like the 3rd

one is required forcertain accuracy.

Powerful Automatic Mesher–– Automatic Mesh RefinementAutomatic Mesh Refinement


# of mesh elements: # of mesh elements: 600K600K

# of calc.: 4 times# of calc.: 4 times


Condition settingCondition setting

SCT preSCT pre

Powerful Automatic Mesher–– Automatic Mesh RefinementAutomatic Mesh Refinement

ImportImport

Targeted # of mesh elements

# of calculation

Targeted # of mesh elements

# of calculation

Mesh generationMesh generation CalculationCalculation

Post processingPost processing

New Octreefrom previous

computation result

New Octreefrom previous

computation resultSCT solverSCT solver

SCT postSCT post11

22

33 444


EHTC 2008 in StrasbourgIntroduction to SC/Tetra


3. Analysis of an HVAC* System

**HHeating, eating, VVentilating and entilating and AAirir--CConditioningonditioning


Cross-flow fan

Evaporator

1. Analysis prior to optimization

Analysis of an HVAC System


Existing Existing model model (staged)(staged)

Primitive Primitive model model (straight)(straight)

Analysis casesAnalysis casesAnalysis cases

Reason for staging:To obtain uniform

velocity distribution in the evaporator



Primitive Primitive model model (straight)(straight)


Analysis meshesAnalysis meshesAnalysis meshes

1.7 million mesh elements


Cross-flow fan

Evaporator

Moving mesh @2235rpmMoving mesh @2235rpm

Pressure lossPressure loss

Flow rateFlow rate

Pressure: 0 PaPressure: 0 Pa

Transient analysis: Time step 1 ms up to 0.5s


Analysis conditionsAnalysis conditionsAnalysis conditions


Results: Velocity distribution in EvaporatorResults: Velocity distribution in Evaporator

Non-uniformUniform


Existing Existing model (staged)model (staged) Primitive Primitive model (straight)model (straight)


Quantification of “Uniformity”Standard deviation of exit velocity

Quantification of “Loss”Difference in area-averaged pressurebetween inlet and outlet

Uniform velocity distribution in

Evaporator

MultiMulti--purposepurpose

Reduction of loss

2. Setting the 2. Setting the objectivesobjectives of optimizationof optimization



Bernoulli's theoryContracted

Small cross-section

Large velocity

Small pressure

Small velocity in Evaporator

3. Setting of optimization design parameters

Basic idea for Uniform velocity distribution

Basic idea for Basic idea for Uniform velocity distributionUniform velocity distribution

Magnitude of pressure

ContractContract



速度小

Confirmation of basic idea using CFD (1)Confirmation of basic idea using CFDConfirmation of basic idea using CFD (1)(1)



ContractSmall

velocity

Confirmation of basic idea using CFD (2)Confirmation of basic idea using CFD (2)Confirmation of basic idea using CFD (2)



4. Optimization


Optimization methodsWorkflow structuring

Fast, robust calculation High quality mesh morphing

Optimization Engine

CFD SolverGeometry modification

tool

Optimization


Optimization Engine

Optimization Engine

Geometry modification

tool

Geometry modification

tool SC/TetraSC/TetraDEF

BAT

SL

PREMDFMDFMDFMDFMDF

BAT

parameters Results

Morphing parameters

Base meshMorphing definitions

Analysis conditions

Morphed mesh

EvaluationResult

Legend:System

FileControlI/O

Optimization


Control points forMorphing

Control points forControl points forMorphingMorphing

Optimization

MorphingMorphing


Design parameter:Range of morphing parameters

Sampling no.: 40

Design parameter:Range of morphing parameters

Sampling no.: 40

Information for sampling

Time limit for optimization

5 min.

Table of parametersTable of

parametersCase/ZoneCase/Zone G1G1 G2G2 G3G3 G4G4

1 0.017692 0.025385 0.015000 0.001846: : : : :: : : : :

40 : : : :

4.1 Sampling by 4.1 Sampling by Design of Experiments methodDesign of Experiments method

n Sampling no.2 63 104 155 21

Design of Experiments methodOptimized Latin Hypercube Sampling

Design of Experiments methodOptimized Latin Hypercube Sampling

Optimization


CaseCase G1G1 G2G2 G3G3 G4G4 S. D.S. D. Ave. PAve. P1 0.017692 0.025385 0.015000 0.001846 0.239995 -222.449: : : : : : :: : : : : : :

40 : : : : : :

Mesh for primitive model

MorphingMorphing

CFDCFD

4.2 4.2 CFD calculationCFD calculation of samplesof samples

Optimization


Design parameters Response

4.3 Approximation model4.3 Approximation model

Approximation model by RBF Neural NetworkApproximation model by RBF Neural Network

Optimization

CaseCase G1G1 G2G2 G3G3 G4G4 S. D.S. D. Ave. PAve. P1 0.017692 0.025385 0.015000 0.001846 0.239995 -222.449: : : : : : :: : : : : : :

40 : : : : : :


Determination coefficient R2

(Multiple correlation coefficient)

Standard deviation: 0.95633

Area-averaged pressure: 0.97671

Determination coefficient R2

(Multiple correlation coefficient)

Standard deviation: 0.95633

Area-averaged pressure: 0.97671

Desirable results with R2 > 0.95

Optimization

EHTC 2008 in StrasbourgOptimization

Examples of visualization of response: Standard deviation


Examples of visualization of response: Area-averaged pressure

Optimization


Primitive model (straight)

Std. Deviation of exit velocity: 0.267268

Area-averaged P of inlet: -254.155

Existing model (staged)

Std. Deviation of exit velocity: 0.

Area-averaged P of inlet : -2

Better

Better

Multi-purpose GA

Multi-purpose GA A0

B0

C0

Std.

Dev

.

Ave. P

5. Multi5. Multi--purpose optimizationpurpose optimization

Pareto solutionPareto solution

No. of Samples (1st gen.): 20No. of generations : 50

No. of Samples (1st gen.): 20No. of generations : 50

RBF Neural NetworkRBF Neural Network

Optimization


Change of design parameters on Pareto solution Blue: Pareto solutionGray: Other

G2: Can be fixed at minimum (no morphing)

G4: Can be fixed at maximum (max morphing)

Engineering Data Mining

G1: Close to maximum (max morphing)

G3: Can be utilized for design variations

Optimization


Better

Advances Pareto frontAdvances Pareto front

G1 & G4 atnear maximum

G1 & G4 atnear maximum

G1 & G4Increased amount of

morphing

G1 & G4Increased amount of

morphing

Re-optimization in approximation modelRe-optimization in

approximation model

G1 0.030 -> 0.040G4 0.006 -> 0.008

A1

B1

C1

OptimizationPrimitive model (straight)


Area-averaged P of inlet: -254.155

Existing model (staged)


Area-averaged P of inlet : -241.58


Example of Pareto SolutionS.D. (approximate) Ave. P (approximate)

A0 0.228692 -252.273B0 0.209740 -247.556C0 0.196647 -241.784A1 0.230152 -254.240B1 0.202036 -248.779C1 0.175985 -242.050

Existing model 0.230355 -241.587

Optimization


S.D.(approximate)

S.D.(CFD)

Ave. P(approximate)

Ave. P(CFD)

A0 0.228692 0.224107 -252.273 -250.307

B0 0.209740 0.204853 -247.556 -247.096

C0 0.196647 0.211522 -241.784 -241.448

A1 0.230152 0.223554 -254.240 -249.539

B1 0.202036 0.201163 -248.779 -245.823

C1 0.175985 0.195815 -242.050 -239.319

Existing model 0.230355 -241.587

Some discrepancySome discrepancy

6. Confirmation of optimized model6. Confirmation of optimized model

Optimization


Existing model (staged)Existing model (staged)Existing model (staged)

Primitive model (straight)Primitive Primitive model (straight)model (straight)

Optimized modelOptimized Optimized modelmodel

Velocity distribution for EvaporatorVelocity distribution for Evaporator

C1

Optimization


Item Application Hours CPU

Meshing (existing) SC/Tetra 3h

Developing UDF 5h

CFD Calculation (existing) SC/Tetra 0.5h 6h

Meshing (primitive) SC/Tetra 0.5h

CFD Calculation (primitive) SC/Tetra 7.5h

Determining morphing control points

Geometry modification tool 1h

Defining morphing, developing batch Geo. mod. tool 3h

Structuring work-flow Opt. Engine 1h

Design of experiments (40 cases)

Opt. EngineGeo. mod. tool

SC/Tetra1h 300h*

Optimization Opt. Engine １h

Confirmation SC/Tetra 1h 7.5h

Total 17ｈ 321ｈ

Wor

kW

ork --

flow

flow

* * 7.5h X 40 cases (up to 16 jobs simultaneous7.5h X 40 cases (up to 16 jobs simultaneous for for 22.5 hour22.5 hour))

Optimization


5. Experimental Results

To be presented on site …

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