Distributed Multidisciplinary Design and Collaborative Optimization

1. Summary These notes describe some recent ideas for distributed design and their application to large-scale aerospace systems. In this type of multidisciplinary optimization, design tasks are decomposed into domain-specific subproblems, and coordinated to achieve an optimal system. Focusing on collaborative optimization, one form of design decomposition, the notes detail the methods, summarize recent results, and suggest new variants of these approaches that improve performance. Initial applications of multidisciplinary optimization (MDO) involved the direct integration of multiple disciplinary analyses and an optimizer. For small problems, wiring together such a system is quite feasible and usually leads to a efficient, but sometimes hard to explain, procedure. As computational capabilities grew, engineers scaled this approach to larger problems and its limitations became apparent. The need for analysis and data management became better recognized and a second generation of MDO methods came into use. Distributed analysis systems could utilize multiple computers, increasing the practical scale of MDO problems (figure 1). Database management and modular analysis coordination improved efficiency and maintainability. Figure 1. Initial architectures for multidisciplinary design involved integrated analysis and optimization. This was followed by application integration frameworks. Despite these improvements, the reliance on a central optimizer as decision maker on all matters, is not a practical approach to enterprise-level system design. This deficiency has led to the development of what may be considered the third generation of MDO methods: strategies for distributed design optimization. This concept, illustrated in figure 2, involves decomposition of the design process itself into more manageable pieces.