Aerospace vehicle design with Bayesian collaborative optimization

Complex engineering design requires solving large optimization problems involving several disciplines. Collaborative Optimization is a two-level design optimization architecture that solves multidisciplinary problems as a series of single-disciplinary problems with little infrastructure overhead. It can be critical when practitioners do not have a fully coupled multidisciplinary optimizer but have access to multiple single-disciplinary optimizers. However, it comes at a steep computational cost because the limited information sharing between disciplines slows downs the progress of the optimization. Our work shows that this issue can be mitigated by exploiting the full extent of the available information. In the Collaborative Optimization framework, disciplines are repeatedly given a design point and tasked with finding the closest feasible design. These iterations are computationally expensive as they require each discipline to solve an optimization problem and report the result. To combine the information collected by all iterations, we propose to learn a predictive model of the distance to the feasible set of each discipline using datasets of feasible and infeasible design points. This requires modeling the signed distance function of each set, which is a challenging machine-learning task. Therefore, we introduce Householder networks: a new, lightweight neural network architecture that can learn distance functions more efficiently than conventional architectures. We then introduce our new method, called Bayesian Collaborative Optimization, which uses ensembles of Householder networks to represent probabilistic models of the disciplinary feasible sets. Following the Bayesian Optimization framework, these models are iteratively refined and used to find values of the design variables that improve the objective function while remaining feasible for every discipline. This method is shown to outperform previous Collaborative Optimization approaches on simple test problems. Finally, we introduce a new multi-disciplinary aircraft design problem. We optimize the airframe, propulsion system, and trajectory of an unmanned fixed-wing vehicle tasked with completing a half-marathon with a fixed battery. This problem tries to balance out realism, by including a diverse set of modeling and optimization approaches, with simplicity and low computational cost. The importance of trajectory optimization, which is efficiently solved by itself but hard to solve coupled with other disciplines, makes this problem different from those previously available. We hope that this problem will be useful to the research community as a test for multidisciplinary design optimization architectures. We have open-sourced the code that generated the results presented in this document. It can be accessed at the following links: https://github.com/jdebecdelievre/HouseholderNets.jl , https://github.com/jdebecdelievre/BayesianCollaborativeOptimization.jl , and https://github.com/jdebecdelievre/ModelAirRaces.jl.

Keywords: Engineering.
Aeronautics and Astronautics.
Advisor: Kroo Ilan
Alonso Juan José
Kochenderfer Mykel J.