Skip to main content

2009 | Buch

An Introduction to Structural Optimization

verfasst von: Peter W. Christensen, Anders Klarbring

Verlag: Springer Netherlands

Buchreihe : Solid Mechanics and Its Applications

insite
SUCHEN

Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
This chapter introduces basic ideas and terminology of structural optimization. The role of mathematical design optimization in the product design process is discussed. Nested and simultaneous formulations of structural optimization, as well as the three basic geometric design parameterizations—size, shape and topology, are defined.
Peter W. Christensen, Anders Klarbring
Chapter 2. Examples of Optimization of Discrete Parameter Systems
The following chapter gives some examples of the general optimization problem (SO) introduced in the previous chapter. They all concern the problem of finding the cross-sectional areas of bars or beams, i.e. they are sizing problems. The list of such examples is the following:
1.
Minimization of the weight of a two-bar truss subject to stress constraints.
 
2.
Minimization of the weight of a two-bar truss subject to stress and instability constraints.
 
3.
Minimization of the weight of a two-bar truss subject to stress and displacement constraints.
 
4.
Minimization of the weight of a two-beam cantilever subject to a displacement constraint.
 
5.
Minimization of the weight of a three-bar truss subject to stress constraints.
 
6.
Minimization of the weight of a three-bar truss subject to a stiffness constraint.
 
A simple example of combined shape and sizing optimization of a two-bar truss is given in Exercise 2.5. Despite their simplicity, it turns out that these problems display several general features of structural optimization problems.
Peter W. Christensen, Anders Klarbring
Chapter 3. Basics of Convex Programming
The solution procedure of the previous chapter relies crucially on the ability to easily identify what constraints are active at the solution of the optimization problem under study. This works fine for problems with only two design variables, but when trying to solve real-life problems, where the number of design variables may vary from the order of 10 to the order of 100 000 or more, one needs more systematic solution methods. In this and the following chapter we will study methods from the field of mathematical programming that are applicable for large-scale problems. We begin by reviewing some fundamental results of mathematical programming, with focus on convex programming. Actually, most problems of structural optimization are in fact nonconvex, but this does not imply that convex programming is of little importance in structural optimization: we will see in Chap. 4 that convex approximations play a very important role in the solution algorithms for nonconvex problems. All theorems are presented without proofs; these may be found in any good book on nonlinear mathematical programming such as Bazaraa, Sherali and Shetty (Nonlinear Programming—Theory and Algorithms, Wiley, 1993) or Bertsekas (Nonlinear Programming, Athena Scientific, 1995).
Peter W. Christensen, Anders Klarbring
Chapter 4. Sequential Explicit, Convex Approximations
In the previous two chapters we were able to formulate a number of structural optimization problems where both the objective function and all of the constraints were written as explicit functions of the design variables only. For larger problems, however, it is in general practically impossible to obtain such explicit functions. Our remedy to be able to solve large-scale problems is to generate a sequence of explicit subproblems that are approximations of the original problem and solve these subproblems instead.
As already mentioned, most problems in structural optimization are nonconvex. Because of the intrinsic difficulties with solving nonconvex problems, we will choose approximations that are convex. In this chapter, a number of explicit, convex approximations will be described. The main focus will be on approximations that take into account specific characteristics of certain structural optimization problems.
Peter W. Christensen, Anders Klarbring
Chapter 5. Sizing Stiffness Optimization of a Truss
In this chapter we will describe in detail how sequential explicit approximations can be used to solve a particular large-scale structural optimization problem, namely that of determining the cross-sectional areas of the bars in a two-dimensional truss with fixed locations of the nodes so that its stiffness is maximized.
Peter W. Christensen, Anders Klarbring
Chapter 6. Sensitivity Analysis
When solving nested structural optimization problems by generating a sequence of explicit first order approximations, such as MMA, one needs to differentiate the objective function and all constraint functions with respect to the design variables. The procedure to obtain these derivatives, or sensitivities, is called sensitivity analysis. In the previous chapter we determined the sensitivity of the compliance of a truss with respect to the cross-sectional area of the bars. In this chapter we will go further and describe how to perform a sensitivity analysis for arbitrary functions and design variables. There are two main groups of methods: numerical methods, which are all approximate, and analytical methods, which are exact. One may also consider hybrids of methods from these two groups: so-called semianalytical methods.
Peter W. Christensen, Anders Klarbring
Chapter 7. Two-Dimensional Shape Optimization
In order to optimize the shape of a structure, one naturally has to be able to control the shape of its boundary using some design variables. In Sect. 6.3.2, the sensitivity analysis for shape optimization of sheets was described. It was concluded that the nodal sensitivities, i.e. the partial derivatives of the nodal positions with respect to the design variables were needed. These sensitivities will depend on how the shape is represented and also on how the finite element mesh is generated. In this chapter we will discuss how the nodal sensitivities may be calculated for two-dimensional structures such as plane sheets or axisymmetric bodies.
Peter W. Christensen, Anders Klarbring
Chapter 8. Stiffness Optimization of Distributed Parameter Systems
In previous chapters we have been concerned with the solution of discrete structural optimization problems: either the structures have been naturally discrete, like trusses, or we have made them discrete by a finite element discretization. In this chapter, on the other hand, we will look at some techniques of mathematics, from an area usually referred to as calculus of variations, that can handle some continuous optimization problems such as those of distributed parameter systems, without the need for a discrete approximation. Basic facts from this area will be applied to two types of optimization problems. Firstly, we will discuss linear elastic systems without introducing any design variables. It will be shown that the state variables of such systems are minimizers of the potential energy of the systems. Next, we look at design problems of a particular type: the design variable enters linearly in the potential energy and we seek to make the structure as stiff as possible in the sense previously considered in Chap. 5. It is shown that optimal structures of this type have the property that a particular specific strain energy is constant throughout the structure, which is to be compared to the fully stressed designs of Sect. 5.2.2. We treat mainly simple problems of beams and bars, but the general structure of this stiffness optimization problem will be used in the next chapter that treats topology optimization problems.
Peter W. Christensen, Anders Klarbring
Chapter 9. Topology Optimization of Distributed Parameter Systems
This chapter gives a brief introduction to formulations and solution techniques for topology optimization of elastic structures. As a starting point we formulate the problem of optimizing stiffness of a sheet by finding an optimal thickness distribution, which is basically a special case of the general stiffness optimization problem of the previous chapter and which relates closely to the truss problem of Chap. 5. The classical optimality criteria method has shown to be very efficient and is widely used for problems of this type. We show that this method can be seen as a special case of the sequential convex approximation method of Chap. 4. Formulations and solution techniques for topology optimization are next introduced as a modification of the variable thickness sheet problem where penalization is introduced to favor discrete-valued thickness distributions. We discuss the occurrence of ill-posedness of formulations and numerical instabilities, and possible cures of these difficulties based on restriction or relaxation. As a standard reference for structural topology optimization we mention Bendsøe and Sigmund (Topology Optimization: Theory, Methods and Applications, 2003, Springer).
Peter W. Christensen, Anders Klarbring
Backmatter
Metadaten
Titel
An Introduction to Structural Optimization
verfasst von
Peter W. Christensen
Anders Klarbring
Copyright-Jahr
2009
Verlag
Springer Netherlands
Electronic ISBN
978-1-4020-8666-3
Print ISBN
978-1-4020-8665-6
DOI
https://doi.org/10.1007/978-1-4020-8666-3