Multifidelity Optimization for Engineering Design: Space Application

The design and optimization of space engineering systems requires the implementation of costly high-fidelity models capable to accurately represent complex physical phenomena. Examples are computational fluid dynamic models for the numerical solution of partial differential equations that permit to capture the aerothermodynamic interaction during the re-entry of a space vehicle from an interplanetary transfer. However, simulation-based optimization usually requires a large number of model evaluations during the search for the optimal design, which makes the use of high-fidelity models unfeasible due to their high computational cost. To address this challenge, we discuss a multifidelity strategy for the design and optimization of complex systems capable to combine multiple models at different levels of fidelity in order to contain the computational cost and achieve a better design solution. We propose a strategy for multifidelity active learning that leverages low fidelity models to explore design configurations and refines the quality of the design solution through the principled query of the high-fidelity model. The active learning scheme is formulated to merge data-driven and domain-aware sources of information and is implemented for the multidisciplinary design optimization of an Orion re-entry capsule. The optimization goals are the minimization of the propellant mass burned during the re entry, the minimization of the structural mass of the thermal protection system and the minimization of the temperature reached by the heat shield, all referred to the baseline design configuration. The results illustrate that our multifidelity framework leads to a design improvement of the 15% with respect to the baseline solution with a fraction (7%) of the overall computational cost that would be required by a single-fidelity optimization based on high-fidelity models only.