PFI-RP: Magnet Topology Optimization for Electric Machine Design

这一创新-研究伙伴关系(PFI-RP)项目的更广泛的影响/商业潜力是改进电机设计。拟议的软件套装预计将是一个插件模块，可作为若干现有计算机辅助工程(CAE)工具的附加组件安装。采用稀土(RE)或非稀土材料的磁体的高效设计可以开发高质量和高通量的发电机和电机，用于许多应用，包括电动汽车、风力发电和电动飞机。该项目将对参与项目的学生进行NSF I-Corps培训，并开展扩大科学、技术、工程和数学参与度的推广活动。拟议的项目旨在开发一个基于水平集的、多物理、多材料的电机设计拓扑优化软件。概念设计被认为是产品生命周期中最重要的阶段，而拓扑优化是概念设计最先进的工具。拟议的拓扑优化工具将用于发电机/电机开发的初始阶段，并可使设计工程师在缩短交付期的情况下实现更好的设计。提出的磁铁拓扑优化工具的关键区别在于基于水平集的参数拓扑优化框架。与基于单元的方法如均匀化方法和SIMP方法相比，基于层次集的拓扑优化方法能够处理具有设计相关边界条件的多物理、多材料的磁体拓扑优化问题。拓扑优化将与基于模型的电机设计协同设计工具相集成，以同时实现电机零部件和系统级的优化设计。建议的研究包括：(1)发电机的多物理建模，(2)多物理和多材料磁体的参数水平集拓扑优化，(3)拓扑优化与基于模型的协同设计的集成，以及(4)发电机优化磁体的先进制造和性能验证。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.