Different Approaches to Taking Joule Heat into Induction Heating of Graphite Crucible

Brazhnik, Dmitriy S.; Bolotin, Kirill

doi:10.1109/eiconrus49466.2020.9039247

2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) 2020

DOI: 10.1109/eiconrus49466.2020.9039247

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Different Approaches to Taking Joule Heat into Induction Heating of Graphite Crucible

Dmitriy S. Brazhnik

Kirill Bolotin

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2025

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Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression

Di Barba,

Ghafoorinejad,

Mognaschi

et al. 2025

Algorithms

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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.

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Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression

Di Barba,

Ghafoorinejad,

Mognaschi

et al. 2025

Algorithms

View full text Add to dashboard Cite

show abstract

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Different Approaches to Taking Joule Heat into Induction Heating of Graphite Crucible

Cited by 1 publication

References 3 publications

Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression

Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression

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