Charles D. McAllister

TL;DR: In this paper, a multidisciplinary robust design optimization formulation is introduced to evaluate uncertainty encountered in the design process, which is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem.

TL;DR: The novel integration of linear physical programming within the collaborative optimization framework is described, which enables designers to formulate multiple system-level objectives in terms of physically meaningful parameters.

TL;DR: In this article, the authors describe the integration of linear physical programming within the Collaborative Optimization framework to enable designers to formulate multiple system-level objectives in terms of physically meaningful parameters, which is demonstrated using two MDO applications: (1) the design of a Formula 1 racecar and (2) the configuration of an autonomous underwater vehicle.

TL;DR: While the bi-level goal programming approach is computationally more expensive, it does improve overall system performance by automating subsystem synthesis and arbitration during optimization.

TL;DR: In this article, the design of an autonomous underwater vehicle with the intent of conveying this testbed to the multidisciplinary design optimization research community for exploration of new and existing modeling and optimization methodologies is introduced.