Abstract

The design of reliable structures requires robust tools that allow the analysis of the behavior of the system subject to variability in its resistance and applied loads. For this, there are several formulations and computational algorithms that enable to model the structural behavior under uncertainties. Under these requirements, the most popular and reliable optimum design methodology is the reliability based optimization (RBO), which should be implemented through the combination of high fidelity mathematical or computational models, e.g. finite element models, efficient and accurate reliability estimation methods, and efficient and effective engineering optimization algorithms. Most RBO applications for structural optimization has the latest developments in efficient computational techniques for simulation and reliability calculations, however, although a variety of optimization methods exist, they generally do not perform a selection of the optimization algorithm more appropriate for each application. In this context, the main contribution of this article is the performing of a comparative study of the computational performance of optimization algorithms applied in structural optimization by RBO. The study compared the numerical performance of optimization algorithms in three problems. Compared algorithms correspond to derivative based algorithms, direct search algorithms, and bioinspired algorithms; including the most representative algorithms of each category. The results of the comparative study point out advantages and disadvantages of the use of the different types of algorithms and allow to conclude on the criteria that must be considered for the choice of an algorithm that favors the computational performance.

Abstract

Abstract

Abstract

Self-deployable composite structures are attractive for space applications as they provide lightweight, low cost and simple solutions that can replace mechanical systems. These structures can be folded and use the stored strain energy to promote a self-deployment event. However, these structures present design, manufacture and optimize challenges.

This article describes the process of designing and optimizing an elastic-hinge, for a telecommunication satellite considering both structural and frequency requirements using finite element analysis, manufacturing and experimental testing. Furthermore, the paper details the sequence of optimization algorithms that were used to produce a solution capable of meeting the original design specifications.

The structural assessment consists of the elastic-hinge numerical modelling during the folding process, by means of an explicit formulation. This model correlates well to the experimental test data and was used to evaluate the damage installed in the hinge using a pletfora of damage criteria.

The frequency analysis considers the numerical modelling of a system constituted by the elastic-hinge and a telecommunication satellite antenna. This model was used to estimate the natural frequency of the system.

Results show that a design methodology which uses a genetic algorithm (GA) for a global search, followed by a particle swarm optimization (PSO) method for a local search provides a good approach to the optimization problem. It is also shown that the efficiency of these algorithms can be maximized by tailoring their architecture and internal parameters.