Data driven computational frameworks for robust design optimization of complex engineering systems

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Simulation-based predictive tools have revolutionized engineering design practice, allowing designers to improve system performance and safety before physical prototypes are built and tested. However, a number of computational challenges remain to be addressed in order to apply high-fidelity simulation tools to design complex engineering systems. The proposed research program will deliver efficient computational methods and a software framework that will enable engineers to optimize the performance and robustness of complex engineering systems on a limited computational budget. The focus of this work will be on the development of novel greedy function approximation strategies for constructing computationally efficient surrogate models of high-fidelity simulation models. This work will also include the formulation of linear and nonlinear dimensionality reduction strategies that will enable the application of greedy surrogate modeling algorithms to high-dimensional, large-scale simulation databases typically encountered in engineering practice. Data-driven optimization frameworks for robust design will be developed leveraging the algorithms developed for surrogate modeling. The computational methods developed during this research project will be tested and validated on aeroengine design and aerodynamic shape optimization problems. The key deliverables from this research project will be novel greedy algorithms for constructing surrogate models, surrogate-assisted optimization strategies for robust design, and a general-purpose software toolkit that will enable significant reductions in the design cost for complex real-world engineering systems.