Stochastic annealing optimization of uncertain aeroelastic system

Dealing with optimization when uncertainty is introduced in the objective function through probabilistic models is a difficult problem. When the gradient of the objective function is not available, there exist several extensions of classical heuristics used for deterministic problems. This paper addresses the case of the annealing algorithm. We adapt a discrete stochastic optimization algorithm to the problem of uncertain aeroelastic optimization. The stochastic annealing algorithm used here optimizes the sum of the expected value of the objective function and a penalized term which takes into account the error bandwidth of the estimator used for evaluating the expected value of the objective function. The approach is tested on a simple wing mockup described by a fish bone finite element model. Uncertainties are introduced in the stiffness of the beams. The purpose of the optimization is to find the best mass configuration which maximizes the critical pressure value of the wing. We show the applicability of this approach in the aeroelasticity context.