A Multiphysics Dataset Generation Procedure for the Data-Driven Modeling of Traction Electric Motors
This paper presents the work done to address two main challenges in the simulation and design of electric machines for traction applications. On one hand, the modeling process is becoming increasingly complex as the demand for higher efficiency, high power density, and low cost pushes the speed and...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10937701/ |
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| author | Simone Ferrari Luigi Solimene Riccardo Torchio Costanza Anerdi Fabio Freschi Luca Giaccone Gianmarco Lorenti Francesco Lucchini Piergiorgio Alotto Gianmario Pellegrino Maurizio Repetto |
| author_facet | Simone Ferrari Luigi Solimene Riccardo Torchio Costanza Anerdi Fabio Freschi Luca Giaccone Gianmarco Lorenti Francesco Lucchini Piergiorgio Alotto Gianmario Pellegrino Maurizio Repetto |
| author_sort | Simone Ferrari |
| collection | DOAJ |
| description | This paper presents the work done to address two main challenges in the simulation and design of electric machines for traction applications. On one hand, the modeling process is becoming increasingly complex as the demand for higher efficiency, high power density, and low cost pushes the speed and compactness of the motor to high levels. As a result, the interactions between multiple physical domains (e.g., electromagnetic, thermal, structural, etc.) can no longer be neglected, even in preliminary designs. Consequently, research into new modeling solutions in this area is currently active and widespread. On the other hand, new computational methodologies based on data-driven machine learning are becoming increasingly widespread as the computational power available for this task increases. However, to assess their performance and realize their potential in surrogate and meta-modeling electrical machines, a standardized benchmark for comparing these new approaches is needed. To address these challenges, the paper presents an open-source dataset that provides a reliable foundation for the multi-physical analysis of electric motors used in traction applications. One of the main novelties of this approach is that geometrical and physical data of the motor configuration are shared among different analysis codes. Attention is focused on tailoring the numerical discretization so that the same mesh can be used in different domains, avoiding data conversions and possible numerical inaccuracies. The paper thoroughly explains the workflow developed to create the database, detailing the methodological aspects. Ultimately, the resulting database is made available as an open resource for other researchers in the field. The resulting dataset represents a tool for benchmarking advanced computational methodologies and promoting reproducibility in research. |
| format | Article |
| id | doaj-art-8f0b233cfcc246c8b6f8c158c1f0883f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8f0b233cfcc246c8b6f8c158c1f0883f2025-08-20T03:04:07ZengIEEEIEEE Access2169-35362025-01-0113545345454610.1109/ACCESS.2025.355414710937701A Multiphysics Dataset Generation Procedure for the Data-Driven Modeling of Traction Electric MotorsSimone Ferrari0https://orcid.org/0000-0002-4100-1590Luigi Solimene1https://orcid.org/0000-0001-5464-1231Riccardo Torchio2https://orcid.org/0000-0002-2916-8793Costanza Anerdi3https://orcid.org/0000-0002-2322-9743Fabio Freschi4https://orcid.org/0000-0002-8183-3228Luca Giaccone5https://orcid.org/0000-0002-9766-3411Gianmarco Lorenti6https://orcid.org/0000-0002-5142-6998Francesco Lucchini7https://orcid.org/0000-0002-6791-9340Piergiorgio Alotto8https://orcid.org/0000-0003-3589-0325Gianmario Pellegrino9https://orcid.org/0000-0003-4264-1917Maurizio Repetto10https://orcid.org/0000-0001-6693-4146Department of Energy “Galileo Ferraris” (DENERG), Politecnico di Torino, Turin, ItalyDepartment of Energy “Galileo Ferraris” (DENERG), Politecnico di Torino, Turin, ItalyDepartment of Industrial Engineering, University of Padova, Padua, ItalyDepartment of Energy “Galileo Ferraris” (DENERG), Politecnico di Torino, Turin, ItalyDepartment of Energy “Galileo Ferraris” (DENERG), Politecnico di Torino, Turin, ItalyDepartment of Energy “Galileo Ferraris” (DENERG), Politecnico di Torino, Turin, ItalyDepartment of Energy “Galileo Ferraris” (DENERG), Politecnico di Torino, Turin, ItalyDepartment of Industrial Engineering, University of Padova, Padua, ItalyDepartment of Industrial Engineering, University of Padova, Padua, ItalyDepartment of Energy “Galileo Ferraris” (DENERG), Politecnico di Torino, Turin, ItalyDepartment of Energy “Galileo Ferraris” (DENERG), Politecnico di Torino, Turin, ItalyThis paper presents the work done to address two main challenges in the simulation and design of electric machines for traction applications. On one hand, the modeling process is becoming increasingly complex as the demand for higher efficiency, high power density, and low cost pushes the speed and compactness of the motor to high levels. As a result, the interactions between multiple physical domains (e.g., electromagnetic, thermal, structural, etc.) can no longer be neglected, even in preliminary designs. Consequently, research into new modeling solutions in this area is currently active and widespread. On the other hand, new computational methodologies based on data-driven machine learning are becoming increasingly widespread as the computational power available for this task increases. However, to assess their performance and realize their potential in surrogate and meta-modeling electrical machines, a standardized benchmark for comparing these new approaches is needed. To address these challenges, the paper presents an open-source dataset that provides a reliable foundation for the multi-physical analysis of electric motors used in traction applications. One of the main novelties of this approach is that geometrical and physical data of the motor configuration are shared among different analysis codes. Attention is focused on tailoring the numerical discretization so that the same mesh can be used in different domains, avoiding data conversions and possible numerical inaccuracies. The paper thoroughly explains the workflow developed to create the database, detailing the methodological aspects. Ultimately, the resulting database is made available as an open resource for other researchers in the field. The resulting dataset represents a tool for benchmarking advanced computational methodologies and promoting reproducibility in research.https://ieeexplore.ieee.org/document/10937701/Data-driven techniquesmachine learningmulti-physics analysisopen-source datasetstraction electric motors |
| spellingShingle | Simone Ferrari Luigi Solimene Riccardo Torchio Costanza Anerdi Fabio Freschi Luca Giaccone Gianmarco Lorenti Francesco Lucchini Piergiorgio Alotto Gianmario Pellegrino Maurizio Repetto A Multiphysics Dataset Generation Procedure for the Data-Driven Modeling of Traction Electric Motors IEEE Access Data-driven techniques machine learning multi-physics analysis open-source datasets traction electric motors |
| title | A Multiphysics Dataset Generation Procedure for the Data-Driven Modeling of Traction Electric Motors |
| title_full | A Multiphysics Dataset Generation Procedure for the Data-Driven Modeling of Traction Electric Motors |
| title_fullStr | A Multiphysics Dataset Generation Procedure for the Data-Driven Modeling of Traction Electric Motors |
| title_full_unstemmed | A Multiphysics Dataset Generation Procedure for the Data-Driven Modeling of Traction Electric Motors |
| title_short | A Multiphysics Dataset Generation Procedure for the Data-Driven Modeling of Traction Electric Motors |
| title_sort | multiphysics dataset generation procedure for the data driven modeling of traction electric motors |
| topic | Data-driven techniques machine learning multi-physics analysis open-source datasets traction electric motors |
| url | https://ieeexplore.ieee.org/document/10937701/ |
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