Bültmann & Gerriets
An Introduction to Structural Optimization
von Peter W. Christensen, A. Klarbring
Verlag: Springer Netherlands
Reihe: Solid Mechanics and Its Applications Nr. 153
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ISBN: 978-1-4020-8666-3
Auflage: 2009
Erschienen am 14.10.2008
Sprache: Englisch
Umfang: 214 Seiten

Preis: 58,84 €

Inhaltsverzeichnis
Klappentext

Examples of Optimization of Discrete Parameter Systems.- Basics of Convex Programming.- Sequential Explicit, Convex Approximations.- Sizing Stiffness Optimization of a Truss.- Sensitivity Analysis.- Two-Dimensional Shape Optimization.- Stiffness Optimization of Distributed Parameter Systems.- Topology Optimization of Distributed Parameter Systems.



This book has grown out of lectures and courses given at Linköping University, Sweden, over a period of 15 years. It gives an introductory treatment of problems and methods of structural optimization. The three basic classes of geometrical - timization problems of mechanical structures, i. e. , size, shape and topology op- mization, are treated. The focus is on concrete numerical solution methods for d- crete and (?nite element) discretized linear elastic structures. The style is explicit and practical: mathematical proofs are provided when arguments can be kept e- mentary but are otherwise only cited, while implementation details are frequently provided. Moreover, since the text has an emphasis on geometrical design problems, where the design is represented by continuously varying-frequently very many- variables, so-called ?rst order methods are central to the treatment. These methods are based on sensitivity analysis, i. e. , on establishing ?rst order derivatives for - jectives and constraints. The classical ?rst order methods that we emphasize are CONLIN and MMA, which are based on explicit, convex and separable appro- mations. It should be remarked that the classical and frequently used so-called op- mality criteria method is also of this kind. It may also be noted in this context that zero order methods such as response surface methods, surrogate models, neural n- works, genetic algorithms, etc. , essentially apply to different types of problems than the ones treated here and should be presented elsewhere.


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