GOALI: A time-efficient analytical framework for optimal electromagnetic, thermal and structural design of switched reluctance motor

GOALI：用于开关磁阻电机最佳电磁、热和结构设计的高效分析框架

• 批准号: 1927432
• 负责人: Mahesh Krishnamurthy
• 金额: $ 39.67万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927432&HistoricalAwards=false
• 关键词: GOALI; time; efficient; analytical; framework

Switched reluctance motors (SRMs) have recently gained attention as a low-cost replacement for permanent magnet (PM) machines, and as a high-efficiency replacement for induction motors. These motors can operate in four quadrants and wide speed-constant power range, which makes them a good candidate for applications ranging from electrical vehicles (EVs) to HVAC systems to domestic appliances. SRMs have concentrated windings on the stator and do not have permanent magnets or windings on the rotor, which makes them an inherently low-cost and good candidate for high-efficiency applications. This motor also has double saliency, high starting torque and low inertia, which can be achieved without large inrush currents. They are also inherently fault tolerant machines since the phase windings are isolated from each other. Despite these advantages, critical problems hindering the wider adoption of SRMs include high acoustic noise, vibration and torque ripple, low efficiency, and power density. In response to these challenges, this research proposes a new modeling paradigm for simultaneous optimization of coupled electromagnetic and structural performance in electric machines. It will also allow modeling of non-homogeneous conditions and 3-D phenomena including acoustic noise and vibration. Successful completion of both these objectives will lead to design techniques that are faster than conventional numerical approaches. This method can be used to implement real-time training of the machine model towards the development of high efficiency, quiet switched reluctance motors, which can easily be extended to other motor types.Conventional approaches for prediction of acoustic noise and vibration in machine design are usually based upon a deterministic approach using finite element analysis (FEA), where the motor is modeled using finite elements solving the partial differential equations (PDEs). Although FEA can be used for problems defined on complicated domains, it requires full discretization of the entire computational domain, which is not numerically efficient. For example, to design a new machine, one must start with a base shape and then tune parameters iteratively to meet the desired performance requirements. Computationally, the domain needs to be discretized several times and PDEs are solved iteratively to identify a relationship between the input parameters and output performance, which makes it computationally intensive. The proposed research will use a kernel free boundary integral method (KF-BIM) approach to develop a comprehensive time and computationally efficient design approach for electric machine design. The kernel-free boundary integral method is a generalization of the classical boundary integral method. It allows the formulation of variable coefficient elliptic PDEs in irregular domain into boundary integrals, without requiring the analytical expression of the Green's function. This approach will be used as a fundamental framework to develop a fast approach to accurately model non-homogenous conditions and 3D phenomena such as noise, vibration and harshness (NVH) and thermal behavior of the machine. This is an inter-disciplinary effort with contributions from Engineering and Applied Mathematics towards a cutting-edge integrated motor drive design framework. Research outcomes will be disseminated through active student engagement in the classroom and research lab, industry collaboration and research presentations. The team will continue to work with undergraduate students from Electrical and Mechanical Engineering in the EDEC laboratory on hands-on tasks and industry-grade benchmarking. Participation by female students and under-represented minority groups will be enhanced through undergraduate Inter-professional Project (IPRO) courses, summer research immersion projects and presentations to the City Colleges of Chicago, Society of Women Engineers and Society of Hispanic Engineers.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.

开关磁阻电机（SRM）作为永磁电机的低成本替代品和感应电机的高效率替代品，近年来受到关注。这些电机可以在四象限和宽速度恒定功率范围内运行，这使它们成为从电动汽车（EV）到HVAC系统到家用电器的应用的良好候选者。SRM在定子上具有集中绕组，并且在转子上没有永磁体或绕组，这使得它们成为高效率应用的固有低成本和良好候选者。该电机还具有双凸极、高起动转矩和低惯性，可以在没有大的浪涌电流的情况下实现。它们也是固有的容错机器，因为相绕组彼此隔离。尽管有这些优点，但阻碍SRM更广泛采用的关键问题包括高噪声、振动和转矩涟漪、低效率和功率密度。为了应对这些挑战，本研究提出了一种新的建模范式，同时优化耦合的电磁和结构性能的电机。它还将允许对非均匀条件和3-D现象（包括声学噪声和振动）进行建模。这两个目标的成功完成将导致设计技术，比传统的数值方法更快。这种方法可以用来实现机器模型的实时训练，以开发高效率、低噪音的开关磁阻电机，这可以很容易地扩展到其他电机类型。传统的机器设计中的噪声和振动预测方法通常基于使用有限元分析（FEA）的确定性方法，其中使用求解偏微分方程（PDE）的有限元对电动机建模。虽然有限元分析可以用于定义在复杂域上的问题，但它需要对整个计算域进行完全离散化，这在数值上不是有效的。例如，要设计新机器，必须从基本形状开始，然后迭代地调整参数以满足所需的性能要求。在计算上，域需要被离散化多次，并且迭代地求解偏微分方程以识别输入参数和输出性能之间的关系，这使得其计算密集。拟议的研究将使用核自由边界积分法（KF-BIM）的方法来开发一个全面的时间和计算效率的电机设计方法。无核边界积分方法是经典边界积分方法的推广。它允许在不规则区域中的变系数椭圆型偏微分方程的公式化为边界积分，而不需要绿色函数的解析表达式。这种方法将被用作一个基本框架，以开发一种快速的方法来准确地建模非均匀的条件和3D现象，如噪音，振动和粗糙度（NVH）和机器的热行为。这是一个跨学科的努力，从工程和应用数学的贡献对一个尖端的集成电机驱动器设计框架。研究成果将通过积极的学生参与课堂和研究实验室，行业合作和研究演示来传播。该团队将继续与EDEC实验室的电气和机械工程本科生合作，进行实践任务和行业级基准测试。将通过本科跨专业项目课程、暑期沉浸式研究项目和在芝加哥城市学院的演讲，加强女学生和代表性不足的少数群体的参与，该奖项反映了NSF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进行评估，被认为值得支持的搜索.

项目成果

Time-Efficient Behavioral Modeling of Switched Reluctance Machines

开关磁阻电机的省时行为建模

• DOI: 10.1109/itec51675.2021.9490084
• 发表时间: 2021
• 期刊: 2021 IEEE Transportation Electrification Conference & Expo (ITEC
• 作者: Jin, Zichao;Zhang, Ziyan;Chen, Chengxiu;Yaman, Selin;Krishnamurthy, Mahesh
• 通讯作者: Krishnamurthy, Mahesh

A kernel-free boundary integral method for elliptic PDEs on a doubly connected domain

• DOI: 10.1007/s10665-022-10233-8
• 发表时间: 2022-08
• 期刊: Journal of Engineering Mathematics
• 影响因子: 1.3
• 作者: Yue Cao;Yaning Xie;M. Krishnamurthy;Shuwang Li;W. Ying

A Hybrid Time-Efficient Modeling Approach for Acoustic Noise Prediction in SRMs

用于 SRM 中声学噪声预测的混合高效建模方法

• DOI: 10.1109/itec51675.2021.9490075
• 发表时间: 2021
• 作者: Zhang, Ziyan;Jin, Zichao;Chen, Chengxiu;Yaman, Selin;Krishnamurthy, Mahesh
• 通讯作者: Krishnamurthy, Mahesh

A Truncated Fourier Based Analytical Model for SRMs with Higher Number of Rotor Poles

具有更多转子极数的 SRM 的基于截断傅里叶的分析模型

• DOI: 10.1109/itec48692.2020.9161708
• 发表时间: 2020
• 期刊: 2020 IEEE Transportation Electrification Conference & Expo (ITEC
• 作者: Jin, Zichao;Jia, Yijiang;Salameh, Mohamad;Bilgin, Berker;Li, Shuwang;Krishnamurthy, Mahesh
• 通讯作者: Krishnamurthy, Mahesh

Surrogate Vibration Modeling Approach for Design Optimization of Electric Machines

• DOI: 10.1109/tte.2020.3017232
• 发表时间: 2020-09-01
• 期刊: IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
• 影响因子: 7
• 作者: Salameh, Mohamad;Singh, Suryadev;Krishnamurthy, Mahesh
• 通讯作者: Krishnamurthy, Mahesh