Overview: Modern aerospace systems are increasingly complex and multidisciplinary, and are required to achieve dramatic improvements in performance, cost effectiveness, and reliability. The field of multidisciplinary optimization (MDO) provides many tools for design optimization within the framework of decomposed and re-combined systems. There is also a suite of probabilistic methods that may be used to assess the reliability of general systems. Synthesizing these tools in an integrated design process, however, exacerbates the already difficult challenges regarding computational complexity and poor convergence. This research therefore seeks to formulate an efficient probabilistic multidisciplinary optimization strategy, which combines MDO techniques, reliability analysis, and reliability-based optimization methods. The optimization and reliability analysis iterations are decoupled (compared to earlier nested methods) to achieve computational efficiency. In addition, a systematic methodology is sought that uses model error sensitivity as a basis for selecting the fidelity level of disciplinary models. The methodology is to be demonstrated for the integrated hypersonic aeromechanics tool (IHAT), which has been developed to aid in designing hypersonic missile systems for the U.S. Navy. The research is conducted in close collaboration with researchers at NASA Langley Research Center.
The multidisciplinary probabilistic optimization methodology is extended to integrate system-level and component-level design requirements. Large aerospace systems are too complex to study on a single level, so a top-down design hierarchy is needed. In this approach the system is first designed with minimal detail using with low-fidelity analyses; during subsequent design stages, components are designed with higher fidelity analyses. However, if the design is to be most efficient, the system-level design must be updated considering additional detail provided from component design. The coupling of the two design levels is modeled through an efficient multidisciplinary optimization formulation.
Example Applications: The proposed methods are being demonstrated on multidisciplinary analysis models for aerospace design applications.
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Reusable Launch Vehicle Concept
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Air-launched low volume ramjet
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Potential Applications: The incorporation of probabilistic information in multidisciplinary design is a valuable tool for numerous applications across all engineering disciplines. In fact, as systems become more complex and continue to incorporate new technologies, limitations on funding and time may necessitate this modeling and simulation-based approach.