A conditional adversarial operator network prediction method for current fields in armature-rail contact surface

• Published in: Journal of computational design and engineering 2024-10, Vol.11 (5), p.284-302
• Main Authors: Jin, Liang, Guo, Shaonan, Su, Haozhan, Song, Juheng, Jia, Yufang
• Format: Article
• Language: English
• ISSN: 2288-5048; 2288-5048
• DOI: 10.1093/jcde/qwae082

Linear propulsion electromagnetic energy equipment can convert electromagnetic energy to kinetic energy instantaneously and has many advantages, such as high kinetic energy, efficiency, precision, and strong controllability. It surpasses traditional mechanical and chemical energy methods, significantly impacting various fields. Aiming at the numerical simulation method, which has problems with large computation volume and a long time of physical field simulation, the current field prediction method of linear propulsion electromagnetic energy equipment based on a Deep Generation Adversarial Operator Network is proposed. Firstly, deep operator network is combined with conditional generative adversarial network to obtain the knowledge-embedded conditional adversarial operator network (CGAONet) model. Then, Res-Transformer-Unet (RTUnet) is used as a branch network of CGAONet to establish the RTUnet-CGAONet model, and the current field method using a deep adversarial operator network is proposed. Finally, the finite element simulation model of the US public linear propulsion electromagnetic energy equipment calculation example is established to construct the simulation dataset from 1D excitation current value and time data to a 2D current field. The trained RTUnet-CGAONet model predicts the mean absolute percentage error of 2.94% in the 2D current field, and the model is minimally affected by the number of samples in the dataset. The results of this paper can achieve the second-level calculation of the current field under different excitation currents, which provides a new way of thinking for the analysis of dynamic characteristics of linear propulsion electromagnetic energy equipment.