Optimization of Interior Ballistic Performance of Series Multi-chamber Gun via DENN-based NSGA-II Algorithm

Abstract

To improve the interior ballistic performance of series multi-chamber gun,a classical interior ballistic model of series multi-chamber guns was established and numerically simulated according to the launch features of the series multi-chamber gun. The main factors affecting the interior ballistic performance of the series multi-chamber guns were analyzed. Based on this, the traditional second-generation non-dominated sorting genetic algorithm (NSGA-II) was utilized to perform multi-objective optimization on the interior ballistic performance of the series multi-chamber guns. The charging masses of the main charge chamber and the auxiliary charge chamber,the mass of the piston,and the ignition delay on ballistic performance were analyzed preliminarily. Given that the NSGA-II algorithm tends to fall into a local convergence when the design variable dimension is overly high,resulting in an invalid optimization, the present work further considered extra design variables, such as arc thickness of propellants and projectile stroke length, and proposed a combination of machine learning neural network (NN) and differential evolution algorithm(DE),namely DENN-NSGA-II,aiming to accelerate traditional NSGA-II. The proposed method can provide a high-quality optimization of the interior ballistic performance for the series multi-chamber gun by generating the initial population used in the NSGA-II algorithm that is near the Pareto front predicted by the NN. Numerical results show that the initial population of DENN-NSGA-II generated near the predicted Pareto has a faster convergence speed than random generation of DENN,and can obtain more ideal and comprehensive optimization results and achieve more efficient interior ballistic performance optimization.