Friday, April 6 at 12:00pm
Frank H.T. Rhodes Hall, 253
Optimization of multidisciplinary systems is critical as slight performance improvements can provide significant benefits over the system's life. However, optimization of multidisciplinary systems is often plagued by computationally expensive simulations, inaccurate sensitivity information, and the need to iteratively solve a complex coupling-relationship between subsystems. These challenges are typically severe enough as to prohibit formal system optimization. A solution is to use multifidelity optimization, where other lower-fidelity simulations may be used to approximate the behavior of the higher-fidelity simulation. Low-fidelity simulations are common in practice, for instance, simplifying the numerical simulations with additional physical assumptions or coarser discretizations, or creating direct metamodels such as response surfaces or reduced order models. This talk offers solutions to two challenges in multidisciplinary system design optimization: developing optimization methods that use the high-fidelity analysis as little as possible but ensure convergence to a high-fidelity optimal design, and developing methods that exploit multifidelity information in order to parallelize the optimization of a system and reduce the time needed to find an optimal design.