Multi‐Objective Optimization of Interior Ballistic Performance Using NSGA‐II

Abstract

This paper investigates an interior ballistic design with equal and unequal web thicknesses of seven-perforation propellant grains using optimization methods. In order to reveal the influence of the web thickness of the propellant grains on the overall interior ballistic performance, burning seven-perforation propellant grains with both equal and unequal web thickness is modeled. A currently popular evolution algorithm (EA) is used to compare two charge shapes, and to seek which one could achieve the optimal ballistic performance. Complete optimization of the interior ballistic performance is a complex process in view of the conflicting objectives to be achieved and a solution to such problems is sought by converting them into a single composite objective and using many tedious measurements. In this paper, a true multi-objective optimization of the interior ballistic charging design is carried out by considering three objectives simultaneously. The non-dominated sorting genetic algorithm version II (NSGA-II) is used to solve this multi-objective optimization problem (MOP). In order to check its implementation, both the conventional optimization algorithm-hill climbing method (HCM) and NSGA-II are used to solve the same single objective problem. The NSGA-II used to capture the full Pareto-optimal front is capable of identifying the trade-off among the conflicting objectives thereby providing alternative useful designs for a designer. Furthermore, for seven-perforation propellant grains, the results of using equal web thickness are compared with those of unequal web thickness, and it is shown that the two charge shapes produce no distinct difference in the interior ballistic performance.

Interior ballistic charge design based on a modified particle swarm optimizer

“…The designer needs to weight the advantages and disadvantages between different objectives. As described in the paper of our group which has been published in PEP (Li and Zhang 2011), the multiobjective optimization problem for interior ballistic charge design can be generally stated as…”

“…Considering the characteristic of guided projectile, the structural parameters of gun are pre-determined and only propellant parameters can be changed for interior ballistic design. In addition, according to the previous experience of interior ballistic charge design (Li and Zhang 2011;Zhang 1998), the web thickness of propellant 2e 1 and the charge density , which are the main characteristic parameters of the propellant are chosen to be the design variables and other parameters are treated as the initial conditions for interior ballistic simulation.…”

“…The objective function can be calculated by a lumped parameter model for interior ballistic process. The detailed mathematical model of interior ballistic process is similar with the model in paper by Li and Zhang (2011). In this single-objective study, the optimization results for interior ballistic charge design are obtained by GA, PSO, CPSO (Alatas et al 2009) and SPSO.…”

“…According to the limitations above, our group used to implement genetic algorithm (GA) which is one of the currently popular intelligent optimizations to optimize the interior ballistic problem. The relevant research results have been published in PEP (Li and Zhang 2011). Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.…”

Modeling and Simulation of Interior Ballistics Based on Actual Combustion Characteristics of Propellants

“…An accurate modeling can promote gun and propellant charge design more efficiently [1][2][3][4][5][6][7]. In the classical interior ballistic model, the burn rate exponent (n) and burn rate coefficient (u 1 ) of the propellant were used to describe the burning property and to simulate the ballistic behaviors [8,9]. In propellant charge design, the ballistic efficiency can be improved by using a progressive surface propellant [10].…”