Physics-Informed Generative Adversarial Network-Based Modeling and Simulation of Linear Electric Machines Article Swipe
Huihuan Wu
,
Shuangxia Niu
,
Yunpeng Zhang
,
W. N. Fu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app122010426
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app122010426
The demand for fast magnetic field approximation for the optimal design of electromagnetic devices is urgent nowadays. However, due to the lack of a publicly available dataset and the unclear definition of each parameter in the magnetic field dataset, the expansion of data-driven magnetic field approximation is severely limited. This study presents a physics-informed generative adversarial network (PIGAN), as well as a permanent magnet linear synchronous motor (PMLSM)-based magnetic field dataset, for fast magnetic field approximation. It includes the current density, material distribution, electromagnetic material properties, and other parameters of the electric machine. Physics-informed loss functions are utilized in the training process, making the output governed by Maxwell’s equation. Different slot-pole combinations of the PMLSM are involved in the dataset to extend the generalization of PIGAN. Some indicators for the further evaluation of magnetic approximation performance, including image-based metrics and calculation methods for the performance of electric motors, are presented in this study. Some challenges of magnetic field approximation using PIGAN are also discussed. The effectiveness of the physics-informed method is verified by comparing the magnetic field approximation results and the performance analysis results of the PMLSM with FEM, and the speed of PIGAN is approximately 40 times faster than that of FEM, while the accuracy is similar.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app122010426
- https://www.mdpi.com/2076-3417/12/20/10426/pdf?version=1665908718
- OA Status
- gold
- Cited By
- 7
- References
- 34
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://doi.org/10.3390/app122010426Digital Object Identifier
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Physics-Informed Generative Adversarial Network-Based Modeling and Simulation of Linear Electric MachinesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-16Full publication date if available
- Authors
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Huihuan Wu, Shuangxia Niu, Yunpeng Zhang, W. N. FuList of authors in order
- Landing page
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https://doi.org/10.3390/app122010426Publisher landing page
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https://www.mdpi.com/2076-3417/12/20/10426/pdf?version=1665908718Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/12/20/10426/pdf?version=1665908718Direct OA link when available
- Concepts
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Magnetic field, Finite element method, Computer science, Magnet, Field (mathematics), Electromagnetic field, Physics, Applied mathematics, Mathematics, Quantum mechanics, Thermodynamics, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 3, 2024: 1, 2023: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | Applied Sciences |
| primary_location.landing_page_url | https://doi.org/10.3390/app122010426 |
| publication_date | 2022-10-16 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W3130490277, https://openalex.org/W3120268848, https://openalex.org/W3199238561, https://openalex.org/W3125889496, https://openalex.org/W3036063661, https://openalex.org/W2610747134, https://openalex.org/W2999580839, https://openalex.org/W3112436251, https://openalex.org/W3090815367, https://openalex.org/W3046552531, https://openalex.org/W4285239155, https://openalex.org/W2135100707, https://openalex.org/W2921710869, https://openalex.org/W3129637124, https://openalex.org/W3000983008, https://openalex.org/W2777417212, https://openalex.org/W2898392193, https://openalex.org/W3161200675, https://openalex.org/W6790024873, https://openalex.org/W3164220104, https://openalex.org/W2922327436, https://openalex.org/W3028129150, https://openalex.org/W3096831136, https://openalex.org/W2963073614, https://openalex.org/W2593729559, https://openalex.org/W2083968915, https://openalex.org/W6713134421, https://openalex.org/W6631190155, https://openalex.org/W3098056110, https://openalex.org/W2064076387, https://openalex.org/W4321438589, https://openalex.org/W3098172061, https://openalex.org/W3157843951, https://openalex.org/W3126220035 |
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