Xuan (Melody) Yi with Prof. Kiruba Haran

Figure 1: Multi-Physics optimization process

The current aviation industry demands environmentally friendly and fuel-efficient aircraft to be sustainable. It has been theoretically proved that superconducting machines can power electric airplanes to carry hundreds of people. These machines apply superconducting technology to produce dramatically high flux density and high current density, and thus can significantly reduce weight/volume. Conventional machines for aircraft application must reach a power density of 8 hp/lb (four times greater than the existing state-of-art) to produce similar results. In this project funded by NASA and the Grainger Foundation, a non-cryogenic, high-frequency, high-pole-count and high-speed air-core permanent magnet (PM) motor is proposed to meet this requirement. In addition, an efficient multi-physics optimization method that can accurately predict electric and thermal performance of such a machine is developed and verified with finite element analysis. Optimum machine sizing of the machine is obtained by using a genetic algorithm coupled with an established multi-physics model requirement.


Figure 2: Pareto front for speed

Currently, multi-objective optimization can run smoothly with developed multi-physics modeling. The pareto front relative to efficiency and specific power can be obtained for various design parameters, including pole number, speed, electric loading, magnetic loading, etc. Figure 1 illustrates the multi-physics optimization process. Designs selected based on a genetic algorithm are fed into the developed multi-physics model and corresponding machine feasibilities are evaluated. Figure 2 presents the pareto front for speed. It indicates that higher-speed designs result in higher power density but lower efficiency.