Probabilistic Design of Multidisciplinary Systems

Abstract

Modern aerospace systems are increasingly complex and multidisciplinary, and are required to achieve dramatic improvements in performance, cost effectiveness, and reliability. For this reason, program requirements must include reliability standards and design processes must incorporate methods for assessing and engineering to these requirements. Probabilistic analysis methods have been well developed as a means to assess reliability by propagating uncertainties in the form of stochastic variables through performance analysis models. In addition, reliability-based design optimization (RBDO) has been given considerable attention in recent years as a means to make design decisions to ensure reliability goals are met. However, for multidisciplinary systems, these tools exacerbate the already intensive computational effort required for design optimization. This dissertation develops efficient methods for the probabilistic (or reliability-based) design of multidisciplinary systems, considering system integration from two fronts. The first front is the integration of disciplinary analyses at a single level, for which methods are presented to improve the efficiency of reliability analysis and optimization. These methods are applied in the design of the power supply system for an unmanned aerial vehicle. On the second front, two alternative strategies are developed for the synthesis of reliability-based design across two design levels (as distinguished by the scope, detail, and fidelity of the performance analysis). In the second of these strategies, model form error uncertainty is presented as a valuable metric representing the fidelity of disciplinary analysis models, allowing probabilistic methods to determine the sensitivity of the system design to model fidelity as a basis for model selection. These concepts are applied to the design of conceptual reusable launch vehicle geometry and that of a component liquid hydrogen tank.