Optimizing the Formation of Hollow Annular Shaped Charges Through a Novel Charge Compensation Method: External Experiment Validation, Simulation, and Predictive Model

Abstract

The design optimization of Annular Shaped Charge (ASC) is highly complex and nonlinear. Traditional ASC optimization focuses on liner structure using empirical methods, while optimization for annular charges is scarce because slight charge variations significantly alter the annular penetrator morphology. To address this, we propose a predictive model for Optimal Charge Compensation Amount (OCCA) of Hollow Annular Shaped Charge (HASC) by integrating Finite Element Method with Multilayer Perceptron (FEM-MLP). Through dimensional analysis and theoretical calculations, we identified four input parameters. Using these, 1431 data points were generated to train and test the MLP. Compared with SVR, Random Forest, and Linear Regression using 5-fold cross-validation, the MLP showed superior prediction accuracy and generalization. The trained MLP predicted OCCA for random and experimental structures, and numerical simulations confirmed high accuracy and generalization. The charge compensation method is broadly applicable for similar HASC structures. The optimized annular jet exhibits no deviation and delayed fracture, providing insights for annular jet penetration into targets.