Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression
In this paper, a multi-physics case study belonging to the class of induction heating problem is considered. Finite Element Analysis is used to evaluate the temperature along a line on a graphite disk heated by two power inductors. In order to build a surrogate field model of the device, i.e., to co...
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2025-01-01
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| author | Paolo Di Barba Arash Ghafoorinejad Maria Evelina Mognaschi Fabrizio Dughiero Michele Forzan Elisabetta Sieni |
| author_facet | Paolo Di Barba Arash Ghafoorinejad Maria Evelina Mognaschi Fabrizio Dughiero Michele Forzan Elisabetta Sieni |
| author_sort | Paolo Di Barba |
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| description | In this paper, a multi-physics case study belonging to the class of induction heating problem is considered. Finite Element Analysis is used to evaluate the temperature along a line on a graphite disk heated by two power inductors. In order to build a surrogate field model of the device, i.e., to compute the temperature profile on the disk, given the amplitudes and frequencies of the supply currents, three methods have been used (Support Vector Regression (SVR), fully connected Neural Network (NN) and Gaussian Process Regression (GPR)). In turn, to solve the inverse problem, i.e., to identify frequencies and currents of the two coils, given a prescribed temperature profile, two approaches have been implemented. The former is an optimization approach based on a multi-objective formulation, solved by means of the NSGA-II algorithm; the latter is a two-step procedure, based on fully connected Deep Neural Networks (DNNs), solving an optimal design problem first and, subsequently, an optimal control problem. |
| format | Article |
| id | doaj-art-61302be661ed4f96a274732a81d5cd91 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-61302be661ed4f96a274732a81d5cd912025-01-24T13:17:27ZengMDPI AGAlgorithms1999-48932025-01-011811010.3390/a18010010Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process RegressionPaolo Di Barba0Arash Ghafoorinejad1Maria Evelina Mognaschi2Fabrizio Dughiero3Michele Forzan4Elisabetta Sieni5Department of Electrical Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, ItalyDepartment of Electrical Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, ItalyDepartment of Electrical Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, ItalyDepartment of Industrial Engineering, University of Padova, 35131 Padova, ItalyDepartment of Industrial Engineering, University of Padova, 35131 Padova, ItalyDepartment of Theoretical and Applied Sciences, University of Insubria, 21100 Varese, ItalyIn this paper, a multi-physics case study belonging to the class of induction heating problem is considered. Finite Element Analysis is used to evaluate the temperature along a line on a graphite disk heated by two power inductors. In order to build a surrogate field model of the device, i.e., to compute the temperature profile on the disk, given the amplitudes and frequencies of the supply currents, three methods have been used (Support Vector Regression (SVR), fully connected Neural Network (NN) and Gaussian Process Regression (GPR)). In turn, to solve the inverse problem, i.e., to identify frequencies and currents of the two coils, given a prescribed temperature profile, two approaches have been implemented. The former is an optimization approach based on a multi-objective formulation, solved by means of the NSGA-II algorithm; the latter is a two-step procedure, based on fully connected Deep Neural Networks (DNNs), solving an optimal design problem first and, subsequently, an optimal control problem.https://www.mdpi.com/1999-4893/18/1/10induction heatingmulti-physics domainfinite element analysisdeep neural networksmulti-objective optimization |
| spellingShingle | Paolo Di Barba Arash Ghafoorinejad Maria Evelina Mognaschi Fabrizio Dughiero Michele Forzan Elisabetta Sieni Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression Algorithms induction heating multi-physics domain finite element analysis deep neural networks multi-objective optimization |
| title | Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression |
| title_full | Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression |
| title_fullStr | Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression |
| title_full_unstemmed | Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression |
| title_short | Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression |
| title_sort | optimal multi physics synthesis of a dual frequency power inductor using deep neural networks and gaussian process regression |
| topic | induction heating multi-physics domain finite element analysis deep neural networks multi-objective optimization |
| url | https://www.mdpi.com/1999-4893/18/1/10 |
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