PFI-RP: Magnet Topology Optimization for Electric Machine Design

PFI-RP：用于电机设计的磁体拓扑优化

• 批准号: 2213852
• 负责人: Shikui Chen
• 金额: $ 55万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213852&HistoricalAwards=false
• 关键词: PFI; RP; Magnet; Topology; Optimization

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.

这个创新研究伙伴关系（PFI-RP）项目的更广泛的影响/商业潜力是改进电机设计。拟议的软件套件预计将是一个插件模块，可以作为许多现有计算机辅助工程（CAE）工具的附加组件安装。利用稀土（RE）或非稀土材料高效设计磁体，可以为许多应用开发高质量和高通量的发电机和电动机，包括电动汽车、风力发电和电动飞机。本项目将对参与项目的学生进行NSF I-Corps培训，并开展拓展活动，以扩大学生在科学、技术、工程和数学领域的参与。该项目旨在开发一种基于水平集、多物理场和多材料的电机拓扑优化软件。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。所提出的拓扑优化工具将用于发电机/电机开发的初始阶段，并可能使设计工程师在缩短交货时间的情况下实现更好的设计。所提出的磁体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。与均质化方法和SIMP方法等基于单元的方法相比，基于水平集的磁体拓扑优化方法能够处理具有设计依赖边界条件的多物理场、多材料磁体拓扑优化问题。拓扑优化将与基于模型的协同设计工具集成，用于电机设计，同时实现电机组件和系统级别的优化设计。提出的研究内容包括：(1)发电机多物理场建模；(2)多物理场、多材料磁体的参数化水平集拓扑优化；(3)拓扑优化与基于模型的协同设计相结合；(4)优化后的发电机磁体先进制造与性能验证。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Designing Programmable Ferromagnetic Soft Metastructures for Minimally Invasive Endovascular Therapy

设计用于微创血管内治疗的可编程铁磁软超结构

• 发表时间: 2023
• 期刊: Proceedings of the International Design Engineering Technical Conferences & Computers & Information in Engineering Conference
• 作者: Zhuang, Ran;Tian, Jiawei;Tassiopoulos, Apostolos;Sadasivan, Chander;Gu, Xianfeng;Chen, Shikui
• 通讯作者: Chen, Shikui

Topology optimization of thermal cloaks in euclidean spaces and manifolds using an extended level set method

• DOI: 10.1016/j.ijheatmasstransfer.2022.123720
• 发表时间: 2023-03
• 期刊: International Journal of Heat and Mass Transfer
• 影响因子: 5.2
• 作者: Xiaoqiang Xu;X. Gu;Shikui Chen

Robust Topology Optimization of Synchronous Reluctance Motors using Cardinal Basis Function Based Level Set Method

使用基于基数函数的水平集方法对同步磁阻电机进行鲁棒拓扑优化

• 发表时间: 2023
• 期刊: ASME Design Engineering Technical Conferences
• 作者: Tian, Jiawei;Torrey, David;Luo, Fang;Longtin, Jon;Shikui
• 通讯作者: Shikui