Shaonan Guo

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.

TEAM Problem 35, a benchmark problem in the optimization of electrical equipment, is a multi-objective, multi-variable optimization problem involving uniform field optimization. This paper proposes a novel guided optimization algorithm to address this problem and achieve optimal magnetic field regulation. First, the deep learning method is used to predict the magnetic field of the solenoid; second, the generated magnetic field cloud map is screened to extract the magnetic field information of the more optimal solution with uniform magnetic field distribution by calculating the variance of the generated magnetic field map; finally, the magnetic field features are combined with the particle swarm optimization algorithm; specifically, the variance of the calculated magnetic field map is added to the objective function of the optimization algorithm to strengthen the optimization process. This integration improves the convergence speed of the optimization process and ensures the continuity and stability of the obtained Pareto solutions. To optimize the solenoid, this paper considers the uniformity of magnetic field distribution and power loss as the primary optimization objectives. The radius of the coil is chosen as the design variable. The optimization results are compared with those obtained by conventional optimization algorithms, demonstrating superior optimization performance.