Asef, Pedram;
Silva, Marcelo D;
Badewa, Oluwaseun A;
Alden, Rosemary E;
Ionel, Dan M;
Eriksson, Sandra;
(2025)
A Physics-Informed Gaussian Process Regression-Based Meta Model for Rapid Characterization of Permanent Magnet Synchronous Machines.
In:
2025 International Electric Machines and Drives Conference (IEMDC).
IEEE
(In press).
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Abstract
Optimizing an Interior Permanent Magnet Synchronous Machine (IPM) requires evaluating multiple working points for each candidate design. In case the design domain has many dimensions, multiple working points evaluations would require an impractical number of finite element method (FEM)-based simulations. This study proposes a novel strategy to build a meta-model to reduce the number of FEM-based simulations for a given optimization process. The study proposes a novel, physicsinformed meta-model based on Gaussian Process Regression (GPR) aiming for rapid characterization of any given machine design. The meta-model uses an adapted version of Posterior Standard Deviation (PSD) to allow for an exact and detailed adaptive sampling strategy. The results show that the proposed meta-model presents a data-efficient approach capable of computing performance parameters with low error. Additionally, the characterization from the proposed meta-model agrees with the experimental data.
| Type: | Proceedings paper |
|---|---|
| Title: | A Physics-Informed Gaussian Process Regression-Based Meta Model for Rapid Characterization of Permanent Magnet Synchronous Machines |
| Event: | International Electric Machines and Drives Conference (IEMDC) |
| Location: | Houston, TX, USA |
| Dates: | 18 May 2025 - 21 May 2025 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://ieeexplore.ieee.org/Xplore/home.jsp |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Gaussian Process Regression, Meta-Model, IPM, Experimental Verification, Physics-Informed Characterization, Design Optimization, Drive Cycle |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10209513 |
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