PFI-RP: Magnet Topology Optimization for Electric Machine Design

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The broader impact/commercial potential of this Partnerships for Innovation - Research Partnerships (PFI-RP) project is to improve electric machine designs. The proposed software suite is expected to be a plug-in module that can be installed as an add-on to a number of existing computer-aided engineering (CAE) tools. The efficient design of magnets with Rare Earth (RE) or non-RE materials may enable the development of high-quality and high-throughput generators and electric motors for many applications, including electric vehicles, wind power, and electric aircraft. This project will implement NSF I-Corps training for participating students and conduct outreach activities for broadening participation in science, technology, enegineering, and mathematics.The proposed project aims to develop a level-set-based, multi-physics and multi-material topology optimization software for electric machine design. Conceptual design is considered the most important stage in the product life cycle, and topology optimization is the most advanced tool for conceptual design. The proposed topology optimization tool will be used in the initial stages of the electric generator/motor development and may enable the design engineers to achieve a better design with reduced lead time. The key differentiator of the proposed magnet topology optimization tool lies in the level-set-based parametric topology optimization framework. Compared with element-based approaches such as the Homogenization method and SIMP methods, the lev-el-set-based topology optimization approach which can handle multi-physics and multi-material magnet topology optimization problems with design-dependent boundary conditions. The topology optimization will be integrated with model-based co-design tools for electric-machine design to simultaneously achieve optimal designs at the component and system levels of an electric machine. The proposed research includes: (1) multiphysics modeling of electric generators, (2) a parametric level set topology optimization of multi-physics and multi-material magnets, (3) integration of topology optimization with model-based co-design, and (4) advanced manufacturing and performance validation of optimized magnets for electric generators.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.