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Multiobjective Optimization in Engineering DesignApplications to Fluid Power Systems
Linkping Studies in Science and Technology. Dissertations. No. 675
Multiobjective Optimization in Engineering DesignApplications to Fluid Power Systems
Division of Fluid and Mechanical Engineering Systems Department of Mechanical Engineering Linkpings universitet SE-581 83 Linkping, Sweden Linkping 2001
On the cover a set of chromosomes is depicted. Chromosomes are essential for evolution as well as for genetic algorithms, which is an optimization algorithm employed within this thesis.
ISBN 91-7219-943-1 ISSN 0345-7524 Copyright 2001 by Johan Andersson Department of Mechanical Engineering Linkpings Universitet SE-581 83 Linkping, Sweden
Printed in Sweden by UniTryck Linkping, 2001.
AbstractHIS THESIS FOCUSES on how to improve design and development of complex engineering systems by employing simulation and optimization techniques. Within the thesis, methods are developed and applied to systems that combine mechanical, hydraulical and electrical subsystems, so-called multi-domain systems. Studied systems include a landing gear system for a civil aircraft, electro-hydrostatic actuation systems for aircraft applications as well as hydraulic actuation systems. The usage of simulation and optimization in engineering design is gaining wider acceptance in all fields of industry as the computational capabilities of computers increase. Therefore, the applications for numerical optimization have increased dramatically. A great part of the design process is and will always be intuitive. Analytical techniques as well as numerical optimization could however be of great value and can permit vast improvements in design. Within the thesis, a framework is presented in which modeling and simulation are employed to predict the performance of a design. Additionally, non-gradient optimization techniques are coupled to the simulation models to automate the search for the best design. Engineering design problems often consist of several conflicting objectives. In many cases, the multiple objectives are aggregated into one single objective function. Optimization is then conducted with one optimal design as the result. The result is then strongly dependent on how the objectives are aggregated. Here a method is presented in which the Design Structure Matrix and the relationship matrix from the House of Quality method are applied to support the formulation of the objective function. Another approach to tackle multiobjective design problems is to employ the concept of Pareto optimality. Within this thesis a new multiobjective genetic algorithm is proposed and applied to support the design of a hydraulic actuation system. The outcome from such a multiobjective optimization is a set of Pareto optimal solutions that visualize the trade-off between the competing objectives. The proposed method is capable of handling a mix of continuous design variables and discrete selections of individual components from catalogs or databases. In real-world situations, system parameters will always include variations to some extent, and this fact is likely to influence the performance of the system. Therefore we need to answer not only the question What is best?, but also What is sufficiently robust? Within this thesis, several approaches to handle these two different questions are presented.
AcknowledgmentsNog finns det ml och mening med vr frd med det r resan som r mdan vrd. Karin Boye, I rrelse.
HERE ARE SEVERAL people who have in one way or another made this thesis possible, and to whom I whish to express my gratitude. First, I would like to thank associate Professor Petter Krus for his great supervision, support and encouragement during this work. I would also like to thank Professor Jan-Ove Palmberg, head of division, for his support and for fruitful discussions during my work to complete this thesis. My gratitude is extended to all members of the division of Fluid and Mechanical Engineering Systems for their help and support. The innumerable discussions, regarding not only research but everything under the sun have created a stimulating and enjoyable atmosphere to work in. I would like to express my special gratitude to Jochen Pohl, coauthor of a number of papers in this thesis. Thanks for the many valuable discussions during the completion of this thesis and for the companionship in traveling the bumpy road towards the Ph.D. degree. I would also like to thank my other co-authors: Dr. Katarina Nilsson at CNS Systems AB (formerly at Saab AB), for her help and support and for being an extraordinary industrial advisor. Professor Steven Eppinger at Massachusetts Institute of Technology (MIT), Boston, for introducing me to a new discipline, and for his kind supervision. During this work, I have had the pleasure to co-operate with Professor David Wallace at the MIT CADlab, for which I am very grateful. The co-operation has been very valuable and given birth to many new ideas. I would like to extend the gratitude to all members of the CADlab and to Dr. Matthew Wall at Oculus Technologies Corporation for his support while using his GAlib genetic algorithm package. Founding for this work was provided by the Swedish Foundation for Strategic Research through the ENDREA research program and is gratefully acknowledged. Last but not least I would like to express the warmest thank you to my family Eva, Rolf and Anna and to my girlfriend, Kia, for always backing me up and encouraging me to struggle on.
Linkping in March 2001
Johan AnderssonBryt upp, bryt upp! Den nya dagen gryr. Ondligt r vrt stora ventyr. Karin Boye, I rrelse.
PapersHE FOLLOWING SEVEN papers are appended and will be referred to by their Roman numerals. The papers are printed in their originally published state except for changes in format and minor errata. [I] ANDERSSON J., POHL J. AND EPPINGER S. D., A Design Process Modeling Approach Incorporating non-Linear Elements, in Proceedings of the ASME Design Theory and Methodology Conference, Atlanta, USA, September 13-16, 1998. ANDERSSON J., POHL J. AND KRUS P., Design of Objective Functions for Optimization of Multidomain Systems, in Proceedings of the ASME Annual Winter meeting, Fluid Power System and Technology, Anaheim, USA, November 1520, 1998. NILSSON K., ANDERSSON J. AND KRUS P., Method for Integrated Systems Design A Study of EHA Systems, in Proceedings of Recent Advances in Aerospace Hydraulics, Toulouse, France, November 24-25, 1998. ANDERSSON J. AND WALLACE D., Pareto Optimization Using the Struggle Genetic Crowding Algorithm, submitted for international publication, 2000. ANDERSSON J., KRUS P. AND WALLACE D., Multiobjective Optimization of Hydraulic Actuation Systems, in Proceedings of ASME Design Automation Conference, Baltimore, USA, September 11-13, 2000. ANDERSSON J. AND KRUS P., Multiobjective Optimization of Mixed Variable Design Problems, in Proceedings of 1st International Conference on Evolutionary Multi Criteria Optimization, Zurich, Switzerland, March 7-9, 2001.
[VII] ANDERSSON J. AND KRUS P., Metamodel Representations for Robustness Assessment in Multiobjective Optimization, accepted publication in Proceedings of the 13th International Conference on Engineering Design, ICED 01, Glasgow, UK August 21-23, 2001.
The following papers are not included in the thesis but constitute an important part of the background. [VIII] ANDERSSON J., POHL J. AND KRUS P., Design of Multi-Domain Systems Using Optimization, in Proceedings of NordDesign 98, Stockholm, Sweden, August 26-28, 1998. [IX] ANDERSSON J., KRUS P. AND NILSSON K., Optimization as a Support for Selection and Design of Aircraft Actuation Systems, in Proceedings of the Seventh AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, St. Louis, USA, September 2-4, 1998. SVENSON H., ANDERSSON J. AND RYDBERG K.-E., Modelling of Losses and Temperature Calculations in Fluid Power Systems, in Proceedings of the Sixth Scandinavian International Conference on Fluid Power, Tampere, Finland, May 26-28, 1999. ANDERSSON J., KRUS P., NILSSON K. AND STORCK, K., Modelling and Simulation of Heat Generation in Electro-Hydrostatic Actuation Systems, in Proceedings of the 4:th JHPS International Symposium on Fluid Power, Tokyo, Japan, November 15-17, 1999.
[XII] PERSSON P., KAMMERLIND P., BERGMAN B. AND ANDERSSON J., A Methodology for Multi-Characteristic System Improvement with Active Expert Involvement, Quality and Reliability Engineering International, vol. 16, 2000, pp. 405416. [XIII] ANDERSSON J., A Survey of Multiobjective Optimization in Engineering Design, Technical report LiTH-IKP-R-1097, Department of Mechanical Engineering, Linkping University, Linkping, Sweden, 2000.
Contents1 Introduction 2 Aims 3 The engineering design process 3.1 Literature 3.2 System design 3.2.1 Modeling and simulation 3.2.2 Optimization 3.3 Designing the design process 3.3.1 Modeling approach 4 Optimization in engineering design 4.1 The concept of value 4.2 The design variables 4.3 The multiobjective optimization problem 4.4 Formulating the objective 4.4.1 No preference articulation 4.4.2 Priori articulation of preference information 4.4.3 Progressive articulation of preference information 4.4.4 Posteriori articulation of preference information 5 Optimization methods 5.1 The Complex method 5.2 Genetic algorithms 5.3 Multiobjective genetic algorithms 5.4 A new multiobjective genetic algorithm 5.4.1 Test Function 6 Applications 6.1 Landing gear system 6.1.1 Methods that support objective function formulation 6.1.2 Objective function formulation 6.2 Multiobjective optimization 6.2.1 Optimization results 6.2.2 Mixed variable design problems 13 15 17