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An automated fully stressed design approach based on the Xie and Steven algorithm is presented. With this algorithm a fully stressed design is obtained by a gradual removal of low stressed material. By applying this evolutionary procedure a layout or topology of a structure can be found from an initial block of material. A fully integrated, interactive program is presented which incorporates automatic mesh generation, finite element analysis and the fully stressed design algorithm. The feasibility of the approach is demonstrated using several examples.

This paper deals with structural shape and thickness optimization of axisymmetric shell structures loaded symmetrically. In the finite element stress analysis use is made of newly developed linear, quadratic, and cubic, variable thickness, C(0) elements based on axisymmetric Mindlin‐Reissner shell theory. An integrated approach is used to carry out the whole shape optimization process in a fully automatic manner. A robust, versatile and flexible mesh generator is incorporated with facilities for generating either uniform or graded meshes, with constant, linear, or cubic variation of thickness, pressure etc. The midsurface geometry and thickness variations of the axisymmetric shell structure are defined using cubic splines passing through certain key points. The design variables are chosen as the coordinates and/or the thickness at the key points. Variable linking procedures are also included. Sensitivity analysis is carried out using either a semi‐analytical method or a global finite difference method. The objective of the optimization is the weight minimization of the structure. Several examples are presented illustrating optimal shapes and thickness distributions for various shells. The changes in the bending, membrane and shear strain energies during the optimization process are also monitored.

Shells are widely used structural systems in engineering practice. These structures have been used in the civil, automobile and aerospace industries. Many shells are designed using the finite element analysis through the conventional and costly trial and error scheme. As a more efficient alternative, optimization procedures can be used to design economic and safe structures.

This paper presents developments, integration and applications of reliable and efficient computational tools for the structural optimization of variable thickness plates and free‐form shells. Topology, sizing and shape optimization procedures are considered here. They are applied first as isolated subjects. Then these tools are combined to form a robust and reliable fully integrated design optimization tool to obtain optimum designs. The unique feature is the application of a flexible integrally stiffened plate and shell formulation to the design of stiffened plates and shells.

This work showed the use of different optimization strategies to obtain an optimal design for plates and shells. Both topology optimization (TO) and structural shape optimization procedures were considered. These two optimization applications, as separate procedures produce new designs with a great improvement when compared to the initial designs. However, the combination of stiffening TO and sizing optimization using integrally stiffened shells appears as a more attractive tool to be used. This was illustrated with several examples.

This work represents a novel approach to the design of optimally stiffened shells and overcomes the drawbacks of both topology optimization and structural shape optimization procedures when applied individually. Furthermore, the unique use of integrally stiffened shell elements for optimization, unlike conventional shell‐stiffening optimization techniques, provided a general and extremely flexible tool.

Investigates the efficiency of hybrid solution methods when incorporated into large‐scale topology and shape optimization problems and to demonstrate their influence on the overall performance of the optimization algorithms. Implements three innovative solution methods based on the preconditioned conjugate gradient (PCG) and Lanczos algorithms. The first method is a PCG algorithm with a preconditioner resulted from a complete or an incomplete Cholesky factorization, the second is a PCG algorithm in which a truncated Neumann series expansion is used as preconditioner, and the third is a preconditioned Lanczos algorithm properly modified to treat multiple right‐hand sides. The numerical tests presented demonstrate the computational advantages of the proposed methods which become more pronounced in large‐scale and/or computationally intensive optimization problems.

The purpose of this paper is to propose physically based varying fidelity surrogates to be used in structural design optimization of space trusses. The main aim is to demonstrate its efficiency in reducing the number of high fidelity (HF) runs in the optimization process.

In this work, surrogate models are built for space truss structures. This study uses functional as well as physical surrogates. In the latter, a grid analogy of the space truss is used thereby reducing drastically the analysis cost. Global and local approaches are considered. The latter will require a globalization scheme (sequential approximate optimization (SAO)) to ensure convergence.

Physically based surrogates were proposed. Classical techniques, namely Taylor series and kriging, are also implemented for comparison purposes. A parameter study in kriging is necessary to select the best kriging model to be used as surrogate. A test case was considered for optimization and several surrogates were built. The CPU time is reduced when compared with the HF solution, for all surrogate‐based optimization performed. The best result was achieved combining the proposed physical model with additive corrections in a SAO strategy in which C1 continuity was imposed at each trust region center. Some guidance for other engineering applications was given.

This is the first time that physical‐based surrogates for optimum design of space truss systems are used in the SAO framework. Physical surrogates typically exhibit better generalization properties than other surrogates forms, produce faster solutions, and do not suffer from dimensionality curse when used in approximate optimization strategies.

Most engineering products contain more than one component or structural element, a consideration that needs to be appreciated during the design process and beyond, to manufacturing, transportation, storage and maintenance. The allocation and design of component interconnections (such as bolts, rivets, or springs, spot‐welds, adhesives, others) usually play a crucial role in the design of the entire multi‐component system. This paper extends the evolutionary structural optimization method to the generic design problems of connection topology. The proposed approach consists of a simple cycle of a finite element analysis followed by a rule‐driven element removal process. To make the interconnection elements carry as close to uniform a load as possible, a “fully stressed” design criterion is adopted. To determine the presence and absence of the interconnection elements, the usage efficiencies of fastener elements are estimated in terms of their relative stress levels. This avoids the use of gradient‐based optimization algorithms and allows designers to readily seek an optimization of connection topology, which can be implemented in their familiar CAD/CAE design platforms. To demonstrate the capabilities of the proposed procedure, a number of design examples are presented in this paper.

This paper shows how the evolutionary structural optimization (ESO) algorithm can be used to achieve a multiple criterion design for a structure in a thermal environment. The proposed thermal ESO procedure couples an evolutionary iterative process of a finite element heat conduction solution and a finite element thermoelastic solution. The overall efficiency of material usage is measured in terms of the combination of thermal stress levels and heat flux densities by using a combination strategy with weighting factors. The ESO method then works by eliminating from the structural domain under‐utilized material. In this paper, a practical design example of a printed circuit board substrate is presented to illustrate the capabilities of the ESO algorithm for thermal design optimization in multiple load environments.