A. James Clark School of Engineering
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item ROBUST MULTI-OBJECTIVE OPTIMIZATION OF HYPERSONIC VEHICLES UNDER ASYMMETRIC ROUGHNESS-INDUCED BOUNDARY-LAYER TRANSITION(2014) Ryan, Kevin Michael; Lewis, Mark J; Yu, Kenneth H; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)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 L2-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.Item Robust Optimization and Sensitivity Analysis with Multi-Objective Genetic Algorithms: Single- and Multi-Disciplinary Applications(2007-11-21) Li, Mian; Azarm, Shapour; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Uncertainty is inevitable in engineering design optimization and can significantly degrade the performance of an optimized design solution and/or even change feasibility by making a feasible solution infeasible. The problem with uncertainty can be exacerbated in multi-disciplinary optimization whereby the models for several disciplines are coupled and the propagation of uncertainty has to be accounted for within and across disciplines. It is important to determine which ranges of parameter uncertainty are most important or how to best allocate investments to partially or fully reduce uncertainty under a limited budget. To address these issues, this dissertation concentrates on a new robust optimization approach and a new sensitivity analysis approach for multi-objective and multi-disciplinary design optimization problems that have parameters with interval uncertainty. The dissertation presents models and approaches under four research thrusts. In the first thrust, an approach is presented to obtain robustly optimal solutions which are as best as possible, in a multi-objective sense, and at the same time their sensitivity of objective and/or constraint functions is within an acceptable range. In the second thrust, the robust optimization approach in the first thrust is extended to design optimization problems which are decomposed into multiple subproblems, each with multiple objectives and constraints. In the third thrust, a new approach for multi-objective sensitivity analysis and uncertainty reduction is presented. And in the final research thrust, a metamodel embedded Multi-Objective Genetic Algorithm (MOGA) for solution of design optimization problems is presented. Numerous numerical and engineering examples are used to explore and demonstrate the applicability and performance of the robust optimization, sensitivity analysis and MOGA techniques developed in this dissertation. It is shown that the obtained robust optimal solutions for the test examples are conservative compared to their corresponding optimal solutions in the deterministic case. For the sensitivity analysis, it is demonstrated that the proposed method identifies parameters whose uncertainty reduction or elimination produces the largest payoffs for any given investment. Finally, it is shown that the new MOGA requires a significantly fewer number of simulation calls, when used to solve multi-objective design optimization problems, compared to previously developed MOGA methods while obtaining comparable solutions.