Machine Learning Enabled Fast Multi-Objective Optimization for Electrified Aviation Power System Design

With the rise of more electric and all-electric aviation power systems, engineering efforts of system optimization shift to the electrical domain. A substantial amount of time and resources are dedicated to finding the best system architecture and design specifications to meet energy efficiency goals and physical constraints. Current processes utilize models of power system components to determine the optimal designs. However, such modeling is computationally expensive as numerous iterations are required to settle on an optimal design. This paper proposes a machine learning (ML) enabled constrained multi-objective optimization solver to drastically reduce the amount of design iterations required for Pareto set discovery for power systems. The process contributes significantly to design automation. A heavy-duty vertical-takeoff-landing (VTOL) unmanned aerial vehicle (UAV) power system is selected to demonstrate the efficacy and limitation of ML enabled optimization. Two extreme trials were run: 1) a search throughout the entire design space with only 9% valid designs within constraints; 2) a search throughout the valid design space. While Trial 1 was unsuccessful in discovering the Pareto front, Trial 2 uncovered all Pareto optimal designs with a 99% reduction of iterations compared to a brute force method.