Robust and Reliability-Based Design Optimization Framework for Wing Design

Abstract

This paper outlines an architecture for simultaneous analysis, robustness, and reliability calculations in aircraft wing design optimization. Robust design optimization and reliability-based design optimization are unified in a mixed formulation, which streamlines the setup of optimization problems and aims at preventing foreseeable implementation issues in uncertainty-based design while ensuring that the performance hit of robustness/reliability assessments is kept to a minimum. To avoid the extra computation time that would be the result of a direct evaluation approach to nondeterministic optimization, Kriging surrogate models are employed, and an alternative implementation of the reliability subproblem is also proposed. The sigma point method is used to compute statistical moments in the robust objective function. The computational effort of reliability analysis is further reduced through the implementation of a coordinate change in the respective optimization subproblem to solve for the distance from the current iterate to the most probable point of failure. Robustness and reliability-based optimization is tested on both simple analytic problems and more complex wing design problems, across a range of statistical variation, revealing that performance benefits can still be achieved while obeying precise probabilistic constraints.

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