A novel key performance analysis method for permanent magnet coupler using physics-informed neural networks

The non-contact transmission product permanent magnet coupler (PMC) has been widely used in industry due to its advantages such as low noise and vibration, high efficiency, high reliability, and overload protection. Owing to its complex electromagnetic behaviors, the accurate computation of key perf...

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Bibliographic Details
Published in:Engineering with computers 2024-08, Vol.40 (4), p.2259-2277
Main Authors: Pu, Huayan, Tan, Bo, Yi, Jin, Yuan, Shujin, Zhao, Jinglei, Bai, Ruqing, Luo, Jun
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
CAE) and Design
Calculus of Variations and Optimal Control
Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Cost analysis
Couplers
Design optimization
Electromagnetic properties
Low noise
Magnetic vector potentials
Math. Applications in Chemistry
Mathematical analysis
Mathematical and Computational Engineering
Neural networks
Original Article
Permanent magnets
Simulation models
Systems Theory
Transmission efficiency
Vibration analysis
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Summary:The non-contact transmission product permanent magnet coupler (PMC) has been widely used in industry due to its advantages such as low noise and vibration, high efficiency, high reliability, and overload protection. Owing to its complex electromagnetic behaviors, the accurate computation of key performances such as magnetic vector potential and torque is essential for optimizing its transmission efficiency. However, traditional calculation methods are based on analytical or simulation models, either imprecise or computationally intensive, hindering subsequent optimization design modeling. To address the above issue, this paper proposes a novel method based on physical-informed neural networks (PINN) to calculate the PMC performances with high accuracy and low computational cost. PINN integrates prior knowledge into the deep neural network’s loss functions to establish the model and accurately predict the PMC’s performance parameters. Experimental results demonstrate that PINN outperforms traditional calculation methods regarding feasibility, validity, and accuracy. Overall, PINN combines data-driven models with prior knowledge to achieve a data–knowledge dual driven, providing a new approach for optimizing PMC structure design.
ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-023-01914-8