Cover: Illustration of a continuous metamodel, developed with the estimated guiding samples approach, representing a discontinuous response in two variables.Printed by: LiU-Tryck, Linköping, Sweden, 2017 ISBN 978-91-7685-482-2 ISSN 0345-7524 Distributed by: Linköping University Department of Management and Engineering SE-581 83 Linköping, Sweden Copyright © 2017 Ann-Britt Ryberg unless otherwise notedNo part of this publication may be reproduced, stored in a retrieval system, or be transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without prior permission of the author.iii PrefaceThe work presented in this thesis has been carried out within a PhD programme at the Division of Solid Mechanics, Linköping University. Financial support has been provided by the Swedish government agency for innovation Vinnova/FFI, and it has also been a part of the SFI/ProViking project ProOpt.Some years ago when I began this journey, which I now approach the end of, I could never imagine how different some things would be today. I have learned that research is hard to plan. Setbacks, as rejection of articles, as well as happy events, as the birth of my children, have all made the journey longer than anticipated. I started my research studies as an employee of the car manufacturer Saab Automobile AB. After having spent many years working with different types of safety simulations, the intention was to find suitable methods for multidisciplinary design optimization that I could implement into the development process of the company. However, the financial situation for Saab Automobile AB became untenable and the company went into bankruptcy. I am therefore very grateful to my new employer Combitech AB that allowed me to continue my endeavours. Since Combitech AB is a technical consulting company, my focus has shifted towards improved methods rather than implementation during the latter part of the project. I am now eager to put my newly acquired knowledge into practice for our customers.A number of people have been important for the outcome of the work presented in this thesis. First and foremost, I would like to express my deepest appreciation to my supervisor Professor Larsgunnar Nilsson for his guidance and support during the course of the work. Many thanks also go to my PhD student colleague Rebecka Domeij Bäckryd for our close collaboration and very fruitful discussions during the first years of the work. Additionally, my manager Tomas Sjödin deserves special appreciation for being one of the initiators of the project and for always supporting me. I am also grateful to all my colleagues, friends, and family for their support and interest in my work.Finally, I would like to especially thank my beloved Henrik and our two lovely children Elsa and Gustav for making my life so enjoyable. I hope you will understand what is in this thesis some day and feel proud of me.Ann-Britt Ryberg, Trollhättan, October 2017 iv v Abstract Multidisciplinary design optimization (MDO) can be used...
Multidisciplinary design optimisation (MDO) can be used as an effective tool to improve the design of automotive structures. Large-scale MDO problems typically involve several groups who must work concurrently and autonomously in order to make the solution process efficient. In this article, the formulations of existing MDO methods are compared and their suitability is assessed in relation to the characteristics of automotive structural applications. Both multi-level and single-level optimisation methods are considered. Multi-level optimisation methods distribute the design process but are complex. When optimising automotive structures, metamodels are often required to relieve the computational burden of detailed simulation models. The metamodels can be created by individual groups prior to the optimisation process, and thus offer a way of distributing work. Therefore, it is concluded that a single-level method in combination with metamodels is the most straightforward way of implementing MDO into the development of automotive structures. If the benefits of multi-level optimisation methods, in a special case, are considered to compensate for their drawbacks, analytical target cascading has a number of advantages over collaborative optimisation, but both methods are possible choices.
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