Multiphysics performance of surrogate models on skewed pole surface-mounted permanent magnet motors
Purpose Evaluating the multiphysics performance of an electric motor can be a computationally intensive process, especially where several complex subsystems of the motor are coupled together. For example, evaluating acoustic noise requires the coupling of the electromagnetic, structural and acoustic...
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Published in | Compel Vol. 43; no. 3; pp. 418 - 426 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Bradford
Emerald Publishing Limited
17.07.2024
Emerald Group Publishing Limited |
Subjects | |
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Abstract | Purpose
Evaluating the multiphysics performance of an electric motor can be a computationally intensive process, especially where several complex subsystems of the motor are coupled together. For example, evaluating acoustic noise requires the coupling of the electromagnetic, structural and acoustic models of the electric motor. Where skewed poles are considered in the design, the problem becomes a purely three-dimensional (3D) multiphysics problem, which could increase the computational burden astronomically. This study, therefore, aims to introduce surrogate models in the design process to reduce the computational cost associated with solving such 3D-coupled multiphysics problems.
Design/methodology/approach
The procedure involves using the finite element (FE) method to generate a database of several skewed rotor pole surface-mounted permanent magnet synchronous motors and their corresponding electromagnetic, structural and acoustic performances. Then, a surrogate model is fitted to the data to generate mapping functions that could be used in place of the time-consuming FE simulations.
Findings
It was established that the surrogate models showed promising results in predicting the multiphysics performance of skewed pole surface-mounted permanent magnet motors. As such, such models could be used to handle the skewing aspects, which has always been a major design challenge due to the scarcity of simulation tools with stepwise skewing capability.
Originality/value
The main contribution involves the use of surrogate models to replace FE simulations during the design cycle of skewed pole surface-mounted permanent magnet motors without compromising the integrity of the electromagnetic, structural, and acoustic results of the motor. |
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AbstractList | Purpose
Evaluating the multiphysics performance of an electric motor can be a computationally intensive process, especially where several complex subsystems of the motor are coupled together. For example, evaluating acoustic noise requires the coupling of the electromagnetic, structural and acoustic models of the electric motor. Where skewed poles are considered in the design, the problem becomes a purely three-dimensional (3D) multiphysics problem, which could increase the computational burden astronomically. This study, therefore, aims to introduce surrogate models in the design process to reduce the computational cost associated with solving such 3D-coupled multiphysics problems.
Design/methodology/approach
The procedure involves using the finite element (FE) method to generate a database of several skewed rotor pole surface-mounted permanent magnet synchronous motors and their corresponding electromagnetic, structural and acoustic performances. Then, a surrogate model is fitted to the data to generate mapping functions that could be used in place of the time-consuming FE simulations.
Findings
It was established that the surrogate models showed promising results in predicting the multiphysics performance of skewed pole surface-mounted permanent magnet motors. As such, such models could be used to handle the skewing aspects, which has always been a major design challenge due to the scarcity of simulation tools with stepwise skewing capability.
Originality/value
The main contribution involves the use of surrogate models to replace FE simulations during the design cycle of skewed pole surface-mounted permanent magnet motors without compromising the integrity of the electromagnetic, structural, and acoustic results of the motor. Purpose Evaluating the multiphysics performance of an electric motor can be a computationally intensive process, especially where several complex subsystems of the motor are coupled together. For example, evaluating acoustic noise requires the coupling of the electromagnetic, structural and acoustic models of the electric motor. Where skewed poles are considered in the design, the problem becomes a purely three-dimensional (3D) multiphysics problem, which could increase the computational burden astronomically. This study, therefore, aims to introduce surrogate models in the design process to reduce the computational cost associated with solving such 3D-coupled multiphysics problems. Design/methodology/approach The procedure involves using the finite element (FE) method to generate a database of several skewed rotor pole surface-mounted permanent magnet synchronous motors and their corresponding electromagnetic, structural and acoustic performances. Then, a surrogate model is fitted to the data to generate mapping functions that could be used in place of the time-consuming FE simulations. Findings It was established that the surrogate models showed promising results in predicting the multiphysics performance of skewed pole surface-mounted permanent magnet motors. As such, such models could be used to handle the skewing aspects, which has always been a major design challenge due to the scarcity of simulation tools with stepwise skewing capability. Originality/value The main contribution involves the use of surrogate models to replace FE simulations during the design cycle of skewed pole surface-mounted permanent magnet motors without compromising the integrity of the electromagnetic, structural, and acoustic results of the motor. PurposeEvaluating the multiphysics performance of an electric motor can be a computationally intensive process, especially where several complex subsystems of the motor are coupled together. For example, evaluating acoustic noise requires the coupling of the electromagnetic, structural and acoustic models of the electric motor. Where skewed poles are considered in the design, the problem becomes a purely three-dimensional (3D) multiphysics problem, which could increase the computational burden astronomically. This study, therefore, aims to introduce surrogate models in the design process to reduce the computational cost associated with solving such 3D-coupled multiphysics problems.Design/methodology/approachThe procedure involves using the finite element (FE) method to generate a database of several skewed rotor pole surface-mounted permanent magnet synchronous motors and their corresponding electromagnetic, structural and acoustic performances. Then, a surrogate model is fitted to the data to generate mapping functions that could be used in place of the time-consuming FE simulations.FindingsIt was established that the surrogate models showed promising results in predicting the multiphysics performance of skewed pole surface-mounted permanent magnet motors. As such, such models could be used to handle the skewing aspects, which has always been a major design challenge due to the scarcity of simulation tools with stepwise skewing capability.Originality/valueThe main contribution involves the use of surrogate models to replace FE simulations during the design cycle of skewed pole surface-mounted permanent magnet motors without compromising the integrity of the electromagnetic, structural, and acoustic results of the motor. |
Author | Ibrahim, Issah Lowther, David |
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Keywords | Rotor skewing Electric motor Surrogate model Acoustic noise Finite element |
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References_xml | – volume: 57 start-page: 1288 issue: 4 year: 2010 ident: key2024071511444136600_ref001 article-title: Characterization and reduction of audible magnetic noise due to PWM supply in induction machines publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2009.2029529 – ident: key2024071511444136600_ref012 – year: 2013 ident: key2024071511444136600_ref005 article-title: Electroacoustics—sound level meters contributor: fullname: IEC Std. 61 672 – volume-title: 2021 International Conference on Electrical Drives and Power Electronics (EDPE) year: 2021 ident: key2024071511444136600_ref003 article-title: The application of neural network metamodels interior permanent magnet machine performance prediction – ident: key2024071511444136600_ref013 – volume: 54 issue: 3 year: 2018 ident: key2024071511444136600_ref009 article-title: Effect of acoustic noise on optimal SynRM design regions publication-title: IEEE Transactions on Magnetics doi: 10.1109/TMAG.2017.2760859 – volume: 46 start-page: 2346 issue: 6 year: 2010 ident: key2024071511444136600_ref006 article-title: Analytical model for predicting noise and vibration in permanent-magnet synchronous motors publication-title: IEEE Transactions on Industry Applications doi: 10.1109/TIA.2010.2070473 – volume: 55 start-page: 1 issue: 6 year: 2019 ident: key2024071511444136600_ref007 article-title: Deep learning for magnetic field estimation publication-title: IEEE Transactions on Magnetics – volume-title: Proc. IEEE Conf. Electromagn. Field Comput. (CEFC), Denver, CO, USA year: 2022 ident: key2024071511444136600_ref004 article-title: A study of the relationship between acoustic noise and torque pulsation in permanent magnet synchronous motors doi: 10.1109/CEFC55061.2022.9940912 – volume-title: Noise of Polyphase Electric Motors year: 2005 ident: key2024071511444136600_ref002 – volume: 31 start-page: 145 issue: 2 year: 1980 ident: key2024071511444136600_ref011 article-title: Models in physics publication-title: The British Journal for the Philosophy of Science doi: 10.1093/bjps/31.2.145 – start-page: 744 volume-title: 2021 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE) year: 2021 ident: key2024071511444136600_ref008 article-title: Application of neural network in parameters optimization of permanent magnet synchronous motor model predictive control doi: 10.1109/PRECEDE51386.2021.9681029 – volume: 12 start-page: 2825 year: 2011 ident: key2024071511444136600_ref010 article-title: Scikit-learn: machine learning in python publication-title: J. Mach. Learn. Res – volume-title: Noise and Vibration Control Engineering: Principles and Applications year: 2006 ident: key2024071511444136600_ref014 |
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Evaluating the multiphysics performance of an electric motor can be a computationally intensive process, especially where several complex subsystems of... Purpose Evaluating the multiphysics performance of an electric motor can be a computationally intensive process, especially where several complex subsystems of... PurposeEvaluating the multiphysics performance of an electric motor can be a computationally intensive process, especially where several complex subsystems of... |
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SubjectTerms | Acoustic noise Acoustics Automation Computing costs Design of experiments Design specifications Electric motors Fourier transforms Neural networks Performance evaluation Performance prediction Permanent magnets Random access memory Simulation Subsystems Synchronous motors |
Title | Multiphysics performance of surrogate models on skewed pole surface-mounted permanent magnet motors |
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