A simulation-based comparison of multidisciplinary design optimization solution strategies using CASCADE

Abstract

The design of multidisciplinary systems (such as aircraft, automobiles, and others) often requires an iterative cycle that includes a design initialization, a system analysis, a sensitivity analysis, and design optimization. This design cycle is standard in the field of Multidisciplinary Design Optimization (MDO) and has often been referred to in the literature as the “Multiple-Discipline-Feasible” (MDF) approach. The name stems from the fact that complete multidisciplinary feasibility is maintained in each and every design cycle. The drawback of MDF is that it can be a timely and a costly procedure. Numerous researchers have developed alternate means for posing and subsequently solving the multidisciplinary design problem. One such solution procedure has been referred to both as “Simultaneous Analysis and Design” (SAND) and “All-at-once” (AAO), and treats the entire multidisciplinary design cycle as one large optimization problem. Another alternate solution procedure has been referred to as “Individual-Discipline-Feasible” (IDF); this procedure exhibits characteristics which lie in between the two extremes exemplified by MDF and AAO. IDF assured that each individual discipline is feasible on every design cycle, while driving the entire system (all disciplines) towards multidisciplinary feasibility. The present work will present a rigorous numerical comparison of these solution strategies over a wide variety of problem sizes and complexities. The purpose of this comparison is for the eventual development of heuristics which will govern the appropriateness of a given solution strategy for a given set of system characteristics. The multidisciplinary design test problems that are used for these comparisons are generated by a robust simulation tool called CASCADE.