电磁轨道发射器高速下电流密度场预测

Current Density Field Prediction Method for Electromagnetic Rail Launcher at High Speed

通过有限元方法计算发射过程中电流密度场,是实现电磁轨道发射器结构精细化设计的必要条件之一,但存在计算时间长、高速计算困难甚至无法计算的问题。该文首先以激励电流、电枢电导率、轨道结构参数和时间t为输入,建立了基于条件生成对抗网络(CGAN)的电流密度场预测模型;然后,为提高对复杂场分布的预测能力,构建ResUnet-Trans网络作为CGAN的特征生成器;最后进行计算验证。以低速电磁轨道发射器实例验证模型内插预测精度,结果表明,模型在测试集上的平均绝对百分比误差(MAPE)小于1.5%;以高速电磁轨道发射器实例验证模型外推预测能力,结果表明,使用由低电导率向高电导率的迁移学习训练策略可以提高模型外推预测精度和泛化能力,在测试集上外推预测MAPE小于2.5%。该文提出的预测方法可实现电流密度场的秒级计算,为高速电磁轨道发射器的优化设计和数字化提供了一种新的思路。

During the electromagnetic rail launcher's launch process,the current distribution on the rail cross-section and armature-rail contact surfaces exhibits significant inhomogeneity,with current density approaching the material's bearing limit.Calculating the current density field during the launching process is one of the necessary conditions to realize the fine design of its structure.However,due to the complexity of the problem involving multi-field coupling and nonlinearity,refining the modeling and calculations using the finite element method(FEM)is time-consuming,often spanning hours or even days.Affected by the velocity skinning effect,the electromagnetic field control equation is a convection-diffusion equation,which is difficult or even impossible to compute at high speeds.Considering that the deep neural network has a powerful feature extraction ability and can learn the time-space change law of the field from numerical simulation data,this paper proposes a current density field prediction method based on ResUnet-Trans-CGAN at high speed.The dataset is obtained using FEM,and the high-precision interpolation prediction of the current density field and the extrapolation prediction of the difficult-to-stabilize computation stage at high speed can be realized after the training is completed,and the single computation time is within seconds.First,to realize the direct generation of the current density field,this paper uses the generative adversarial network(GAN),which is currently one of the most widely used image generation models.The conditional generative adversarial network(CGAN)addresses GAN's uncontrollable image generation issue by introducing a conditional variable y to the generator and discriminator.In order to realize the one-to-one mapping from input parameters to current density field,this paper removes the random noise z in the input,and directly inputs the excitation current,armature conductivity,rail structure parameters and time t into the neural network as the conditional variables,and establishes the current density field prediction model based on CGAN.Then,to improve the feature extraction capability for complex current density field distributions,the ResUnet-Trans network is constructed as the feature generator of CGAN.The U-Net structure is used to realize the fusion of different levels of features,short Residuals are introduced to inhibit the deep neural network degradation,and the Transformer is introduced to enhance the model's ability to extract global features.Second,dropout and a cosine annealing algorithm for adaptive learning rate adjustment are introduced in the model to curb overfitting and improve prediction accuracy.Further,a transfer learning training strategy,transitioning from low to high conductivity,is applied during training to improve the model's extrapolation prediction accuracy and generalization ability.Finally,the accuracy of model interpolation and extrapolation prediction is verified with the examples of low-speed and high-speed electromagnetic rail launcher,respectively.The results show that for interpolation prediction,the ResUnet-Trans-CGAN proposed in this paper has the highest accuracy with the mean absolute percentage error(MAPE)value of current density field prediction less than 1.5% compared to CGAN,Unet-CGAN and ResUnet-Trans.After using the transfer learning training strategy from low to high conductivity,the MAPE is reduced by 9.59% and 19.0% at the rail cross-section and at the armature-rail contact surface,respectively.When extrapolating the prediction at high speeds,the MAPE of the current density field at the rail cross-section is reduced from 1.83% to 1.52% and the training speed is improved by 17.0%,and that at the armature-rail contact surface is reduced from 3.47% to 2.43%,and the training speed is improved by 22.1%,after using the Transfer Learning training strategy of moving from low to high conductivity on the test set.The model can generate the current density field directly at the stage where it is difficult to perform stable numerical simulation at high speed.