An asymmetric suboptimization method for performing multidisciplinary design optimization is introduced. The objective of the proposed method is to improve the overall efficiency of aerostructural optimization, by simplifying the system-level problem, and thereby reducing the number of calls to a potentially costly aerodynamics solver. To guide a gradient-based optimization algorithm, an extension of the coupled sensitivity equations is developed to include post-optimality information from the structural suboptimization. The optimization of an aircraft wing is performed using linear aerodynamic and structural analyses, and a thorough performance comparison is made between the new approach and the conventional multidisciplinary feasible method. The asymmetric suboptimization method is found to be the more efficient approach when it adequately simplifies the system-level problem, or when there is a large enough discrepancy between disciplinary solution times.

The multi-level, multidisciplinary and multi-fidelity optimization framework developed at Bombardier Aviation has shown great results to explore efficient and competitive aircraft configurations. This optimization framework has been developed within the Isight software, the latter offers a set of ready-to-use optimizers. Unfortunately, the computational effort required by the Isight optimizers can be prohibitive with respect to the requirements of an industrial context. In this paper, a constrained Bayesian optimization optimizer, namely the super efficient global optimization with mixture of experts, is used to reduce the optimization computational effort. The obtained results showed significant improvements compared to two of the popular Isight optimizers. The capabilities of the tested constrained Bayesian optimization solver are demonstrated on Bombardier research aircraft configuration study cases.

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A new subspace optimization method for performing aero-structural design is introduced. The method relies on a semi-analytic approach to the sensitivity analysis that includes post-optimality sensitivity information from the structural optimization subproblem. The resulting coupled post-optimality sensitivity (CPOS) approach is used to guide a gradient-based optimization algorithm. The new approach simplifies the system-level problem, thereby reducing the number of calls to the costly aerodynamics solver. The aero-structural optimization of an aircraft wing is carried out, and it is shown that the proposed method results in a problem equivalent to the conventional approach. The new method is also shown to reduce both the computational time required by the aerodynamic discipline, and the total time required by higher-fidelity optimizations.