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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.

The potential of two distinct approaches applied to the truss discrete optimization problem is presented in the paper. The sequential discrete optimization method SDO (which is a deterministic procedure, using heuristics based on the idea of fully stressed truss design) and the genetic algorithm GA (a stochastic search method, inspired by the natural evolution model) are compared. The minimum weight design of truss structures subjected to stress and displacement constraints is investigated, including the case of multiple load conditions. The discrete design variables are areas of members, selected from a finite catalogue of available sections. Benchmark 2D and 3D problems are presented in numerical examples. The effectiveness of two approaches is discussed. The improvements of both algorithms and GA integrating the results of SDO method are proposed. They enable us to accelerate the convergence, diminish the number of structural analyses and guide to refined “near optimal” solutions.

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.

The purpose of this paper is to present an overview of the aircraft design problems which can be efficiently solved using a special solid finite‐element model of variable density.

Optimization algorithms based on fully‐stressed design philosophy, sensitivity coefficients, and employing material density as a design variable provide means to generate optimal topology layouts, subject to a wide range of design constraints. A novel non‐dimensional criterion is used for assessment of load‐carrying efficiency of structures and knowledge accumulation.

Variable density model, together with non‐dimensional criterion of structural efficiency, yields a new versatile approach to a structural weight estimation at early design stages. New weight equations are used. The approach is a powerful tool for addressing complex multidisciplinary design optimization (MDO) problems such as aerodynamic load prediction taking aeroelastic deformations into account and aerodynamic‐structural design optimization of unconventional aircraft configurations.

For accurate estimation of wing weight and deflections, the method should be tuned by regression analysis of existing aircraft to properly account for secondary structural weight.

The developed software tools for aeroelastic behaviour prediction and coupled aerodynamic‐structural design optimization are ready for integration into the complex MDO framework.

The variable density model is shown to have broad predictive opportunities for design problems at early stages of a product development.

Demonstrates the simple but effective application of a standard finite element program (PAFEC), and the associated geometric modelling code (PIGS), to the improvement of the design of an engineering component. The technique adopted involves augmenting material around zones of high stress and removing material in zones of low stress. This evolutionary procedure is related to the behaviour of bones in animals. The essentially two‐step procedure involves; finite element analysis of the preliminary component design using PAFEC; and, definition of a new geometry using PIGS, with selected stress contours giving an indication of the new shape. The technique, which proceeds iteratively, was first tested successfully on some classical academic optimisation problems. Its subsequent application to the industrial problem of a twin chamber pressurised extruded aluminium section, the primary component of an air drying system, resulted in material savings of up to 50 per cent and an associated drop in the maximum von Mises stress of 45 per cent. While this method does not determine the optimal structural form, it does generate substantial improvements in terms of material usage and reduced maximum stresses. It has the advantage that it can be used by any competent engineer with a working knowledge of the strength of materials, finite elements and structural form.

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.

Describes development work to combine the basic ESO with the additive evolutionary structural optimisation (AESO) to produce bidirectional ESO whereby material can be added and can be removed. It will be shown that this provides the same results as the traditional ESO. This has two benefits, it validates the whole ESO concept and there is a significant time saving since the structure grows from a small initial one rather than contracting from a sometimes huge initial one where 90 per cent of the material gets removed over many hundreds of finite element analysis (FEA) evolutionary cycles. Presents a brief background to the current state of Structural Optimisation research. This is followed by a discussion of the strategies for the bidirectional ESO (BESO) algorithm and two examples are presented.

Introduces a faster and improved structural optimisation method which combines fixed grid finite element analysis (FG FEA) and evolutionary structural optimisation (ESO). ESO optimises a structure by removing a few elements at every iteration. FG methods allow fast mesh generation, fast solution and fast re‐evaluation of the modified meshes. The implementation of FG into the ESO process eliminates the need for regenerating the mesh and a few arithmetic calculations replace the full regeneration of the stiffness matrix every time the structure is modified. This greatly reduces the solution time, and the examples presented in this paper demonstrate and validate the method.

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.

The purpose of this paper is to introduce an evolutionary heuristic method for simultaneous optimization of topology and material property distribution of functionally graded (FG) structures under a prescribed loading condition.

The proposed procedure is inspired by heuristic nature of bi-directional evolutionary structural optimization (BESO) and genetic algorithm (GA). The optimization algorithm is developed in the context of minimum compliance (maximum stiffness) design problem. The problem is modeled by means of finite element method (FEM). The element-wise material volume fractions and elements’ status (i.e. existence or nonexistence in FE model) are introduced as design variables. After FE analysis, sensitivities are obtain and filtered according to BESO. Having determined sensitivities, by means of a heuristic scheme combined by GA, topology and material property distribution for the next cycle of optimization are determined by updating design variables.

The adopted method has been tested by means of several examples previously reported in literature. The comparison showed the superiority of the proposed method against its rival in terms of relative reduction in compliance, smoother material property distribution and computational cost.

The value of the described method lies in its simple (yet efficient) nature. In contrast with its only rival in literature which more relied on mathematical approach, proposed method uses a series of logic-based heuristic ideas to drive reasonable solutions but with far less computational cost.