Structural optimization of an electromagnetic packer based on genetic algorithm and neural network

To address the low efficiency and high cost associated with traditional design optimization methods for downhole electromagnetic packers, a multi-objective optimization model for packer structural design is established in this study. An optimization approach that integrates genetic algorithms (GAs) and neural networks is proposed. Orthogonal experimental design and COMSOL Multiphysics simulation are employed to construct the training dataset. The sensitivity of structural parameters to packer performance and manufacturing cost is evaluated through Sobol sensitivity analysis. A GA-optimized neural network is then used to establish a nonlinear mapping between structural design parameters and performance metrics, while the GA is further used to perform multi-objective optimization of the packer structure. COMSOL simulation results demonstrate that the optimized electromagnetic packer achieves enhanced performance and reduced cost, confirming that the proposed method can accurately determine optimal structural configurations.