Monte Carlo Simulation in Reliability Based Optimization Applied to Multidisciplinary System Design

Abstract

The work presented in the paper deals with the incorporation of Monte Carlo Simulation (MCS) techniques in Reliability Based Optimization (RBO). The main criteria used to select the suitable MCS techniques are the ease of obtaining analytical sensitivities and the smoothness of the probability of failure estimates with changes in design. The MCS techniques that best suit these criteria are Conditional Expectation MCS techniques that consist of the Directional Simulation and Axis-Orthogonal Simulation techniques. Details of obtaining probability of failure estimates and its sensitivities using these simulation techniques for component reliability and series system reliability are presented. A strategy for performing RBO using the axis-orthogonal simulation is presented. This strategy was applied to two multidisciplinary test problems. The first test problem is a simple analytic problem that is used for demonstration purposes. The second test problem is a control-augmented structure problem that has been used in various Multidisciplinary Design Optimization (MDO) studies. The MCS based RBO converged for both test problems and substantial improvements from initial designs, obtained using First Order Reliability Method based RBO, were observed for both * Graduate Research Assistant, Student Member AIAA. † Mechanical Engineer, Member AIAA. ‡ Professor, Associate Fellow AIAA.

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