Multi-fidelity Constrained Optimization Methods Applied to Benchmark Problems

The traditional design process relies upon low-fidelity models for expedience and resource savings. However, the reduced accuracy and reliability of low-fidelity tools often lead to the discovery of design defects or inadequacies late in the design process. These deficiencies result either in costly changes or the acceptance of configurations that do not meet expectations. Multi-fidelity methods attempt to blend the increased accuracy and reliability of high-fidelity models with the reduced cost of low-fidelity models. In this paper, two multi-fidelity constrained optimization approaches are presented and exercised on two analytical benchmark problems as well as a constrained drag minimization problem for the RAE-2822 airfoil. The results show promise in overall computational savings compared to using high-fidelity information alone.