CAREER: High-Resolution Lumped Parameter Network Multiphysics Models for Electric Machine Topology Optimization

About This Grant

Electric machines are critical to society, converting power between electricity and motion. However, innovations in electric machines are necessary to reduce costs, increase efficiency, and improve their capabilities for challenging applications, such as space exploration. Therefore, this project will develop and integrate fast, flexible, and accurate electromagnetic, structural, and thermal equivalent circuit models of electric machines. These models will be tailored for topology optimization (TO), which yields novel shapes that would not result from conventional optimization. Previously, TO has only been applied to portions of electric machines and has not considered electromagnetic, structural, and thermal performance simultaneously. However, the speed, flexibility, and accuracy of the developed models will enable TO of entire electric machines considering electromagnetic, structural, and thermal performance simultaneously. The resulting new designs will reduce costs and losses for existing applications and enable the use of electric machines in new applications. Other broader impacts of the project include research opportunities for high school, undergraduate, and graduate students and the development of assignment modules teaching students to combine coding and discipline-specific knowledge to develop models for solving engineering problems; these assignment modules will be designed to be incorporated into various engineering classes. This project will develop a new approach for multiphysics analysis of electric machines that is significantly faster than finite element analysis (FEA) with the accuracy and flexibility required for TO. TO, unlike conventional optimization, yields fundamentally new geometries that can take advantage of additive manufacturing. However, true TO of electric machines requires fast and extremely flexible multiphysics analysis. Thus, this project will develop high-resolution lumped parameter magnetic, electrical, structural, and thermal network models. These models will enable multiphysical TO of entire electric machines. Each network will divide the geometry into a grid of many small node cells, which consist of lumped sources (magnetomotive force, electromotive force, mechanical force, or heat) and impedances (reluctances, resistances, springs, or thermal resistances), and solve for the magnetic scalar potential, electrical current, mechanical deflection, or temperature of each node cell. Each network will share the same grid of node cells as the TO algorithm. To perform multiphysics analysis, each network will be solved individually and its results used to update the other networks iteratively until the analysis converges. To test the models’ accuracy, the networks will be applied with TO to design different electric machines, and the performance of the optimal designs will be verified with FEA and experimental testing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.