Lisa Kusch, SciComp
Robust Design in the Context of Deterministic Multi-Objective Optimization
Realistic engineering design involves the optimization of different competing objectives. Here, the aim is to find a set of solutions that fulfill the concept of Pareto optimality. A further significant step to realistic multiobjective designs is to take into account uncertainties for finding robust optimal solutions. So far, multi-objective robust design is mainly treated in an evolutionary context. There exist different methods to propagate uncertainties in the model. As the costs of a multi-objective optimization are already very high, it is important to use efficient approaches.
The aim is to find robust designs in multi-objective airfoil design. We apply a nonintrusive polynomial chaos approach for uncertainty quantification and the deterministic Epsilon-Constraint method for solving the multi-objective optimization problem. The concept of the Epsilon-Constraint Method is to optimize one objective function while imposing inequality constraints on the remaining competing objective functions. The constraints as well as the objective function to be optimized is varied to find different Pareto optimal solutions that are evenly distributed. For solving the sequence of these constrained single-objective
optimization problems we will make use of deterministic optimization strategies using algorithmic differentiation (AD) for the computation of derivatives.
The first significant steps towards multi-objective robust airfoil design are presented in the seminar talk.