Multidisciplinary Design Optimization

Multidisciplinary design optimization (MDO) is a field of engineering that focuses on use of numerical optimization to perform the design of systems that involve a number of disciplines or subsystems. The main motivation for using MDO is that the best design of a multidisciplinary system can only be found when the interactions between the system's disciplines are fully considered. Considering these interactions in the design process cannot be done in an arbitrary way and requires a sound mathematical formulation. By solving the MDO problem early in the design process and taking advantage of advanced computational analysis tools, designers can simultaneously improve the design and reduce the time and cost of the design cycle. who extended their experience in structural optimization to include other disciplines. One of the first applications of MDO was aircraft wing design, where aerodynamics, structures, and controls are three strongly coupled disciplines [54, 55, 103, 104]. Since then, the application of MDO has been extended to complete aircraft [92, 106] and a wide range of other engineering systems, such One of the most important considerations when implementing MDO is how to organize the disciplinary analysis models, approximation models (if any), and optimization software in concert with the problem formulation so that an optimal design is achieved. Such a combination of problem formulation and organizational strategy is referred to as an MDO architecture. The MDO architecture defines both how the different models are coupled and how the overall optimization problem is solved. The architecture can be either monolithic or distributed. In monolithic approaches, a single optimization problem is solved. In a distributed approach, the same single problem is partitioned into multiple subproblems containing small subsets of the variables and constraints. While many different architectures can be used to solve a given optimal design problem — and just as many algorithms may be used to solve a given optimization problem — choosing the most appropriate architecture for the problem can significantly reduce the solution time. These time savings come from the selected methods for solving each discipline, the coupling scheme used in the architecture, and the degree to which operations are carried out in parallel. The latter consideration becomes especially important as the design becomes more detailed and the number of variables and/or constraints increases. Note that in the MDO literature, there are several terms used to describe what we mean by " ar