ROBUST MULTI-OBJECTIVE OPTIMIZATION OF HYPERSONIC VEHICLES UNDER ASYMMETRIC ROUGHNESS-INDUCED BOUNDARY-LAYER TRANSITION
Ryan, Kevin Michael
Lewis, Mark J
Yu, Kenneth H
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The effects of aerodynamic asymmetries on hypersonic vehicle controllability and performance were investigated for a wide range of geometries. Asymmetric conditions were introduced by an isolated surface roughness that forces boundary-layer transition resulting in a turbulence wedge downstream of the disturbance. The disturbance simulates the effects of physical deformations that may exist on a vehicle surface or leading edge, such as protruding edges of thermal protection system tiles or non-uniform surface roughness. Both multi-objective and robust multi-objective optimization studies were performed. Traditional multi-objective optimization methods were used to identify vehicle designs that are best suited to withstand spanwise asymmetric boundary-layer transition while retaining its performance and payload requirements. Trade-offs between vehicle controllability and performance were analyzed. A novel multi-objective based robust optimization method to solve single-objective optimization problems with environmental parameter uncertainty was proposed and tested. Unlike commonly used robust optimization methods, the multi-objective method formulates an optimization problem such that post-optimality data handling techniques can identify multiple robust designs from a single solution set. This allows for comparisons to be made between different types of robust designs, thus providing more information about the design space. Comparisons were made between the robust multi-objective optimization formulation and conventional robust regularization- and aggregation-based methods. The results, performance, and philosophies of each method are discussed. Design trends were identified for classifying the optimum and robust optimum designs of hypersonic vehicle shapes under boundary-layer transition uncertainties. Traditional multi-objective optimization results show that two types of vehicle shapes bound the set of Pareto-optimal solutions: wedge-like and cone-like. The L<super>2</super>-norm optimum design, representing a compromise between the competing shapes, was a hybrid wedge-cone shape. The robust optimization results show that a flat wedge-like vehicle design is best for a worst-case scenario, while a pyramidal shaped vehicle design minimizes the expected detrimental effects on vehicle controllability. The analyses prove that the novel robust optimization method can provide a range of robust optimum results, while also capturing trade-offs within the design space, providing capabilities not available in state-of-the-art robust optimization methods.