Abstract
Artificial intelligence and deep learning have been widely used in recent years to explore the possibility of accelerating computation. However, it has very few applications in magnetic field calculation. This article employs a conditional generative adversarial network (cGAN) to approximate the magnetic field of a coaxial magnetic gear and calculate the magnetic torque through postprocessing. The working principle of magnetic field approximation using cGAN and the training process is introduced in this study. We adopted conditional image-to-image translation technology in cGAN and compared different loss functions and residual structures combinations. Then, we found the best combination, which can accelerate convergence, reduce errors, and improve the generator's performance. Numerical experiments have verified the effectiveness of the proposed cGAN, and the average numerical error can be as small as 1%. At the same time, its speedup ratio is as high as 200 compared to the finite element method.
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