Comparing Filtering Multifidelity Optimization Strategies With a Simulation-Based Multidisciplinary Aircraft Model

Abstract

Abstract Multifidelity optimization leverages the fast run times of low-fidelity models with the accuracy of high-fidelity models (HFMs), in order to conserve computing resources while still reaching optimal solutions. This work focuses on the multifidelity multidisciplinary optimization of an aircraft system model with finite element analysis and computational fluid dynamics simulations in the loop. A two-step filtering method is used where a lower fidelity model is optimized, and then the solution is used as a starting point for a higher-fidelity optimization routine. By starting the high-fidelity routine at a nearly optimal region of the design space, the computing resources required for optimization are expected to decrease when using local algorithms. Results show that, when using surrogates for the lower fidelity models, the multifidelity workflows save statistically significant amounts of time over optimizing the original HFM alone. However, the impact on solution quality varies depending on the model behavior and optimization algorithm.