Whole Trajectory Design Optimization of Self-Propelled Artillery Based on Adaptive Genetic Algorithm

Abstract:

An optimization design method of whole trajectory for a self-propelled artillery, including interior, exterior and terminal ballistic trajectory, is proposed. The structural parameters of the projectile, ballistic parameters, charge weight and explosive weight are selected as the design variables. The optimization objectives are the largest lethal area, the longest range at 45° and the highest utilization of propellant energy. Constraining the maximum bore pressure, muzzle pressure, packing density and the relative position of propellant burning end, the whole trajectory optimization model of the self-propelled is established. The model is calculated using both genetic algorithm and adaptive genetic algorithm. Optimization results show that the effect and convergence rate of the adaptive genetic algorithm are better than those of the genetic algorithm. The method in this paper play an important role in theory reference and engineering application for the whole trajectory design of the self-propelled artillery.

• Periodical: Advanced Materials Research (Volumes 971-973)
• Pages: 1391-1395
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.971-973.1391
• Online since: June 2014
• Authors: Fu Qiang Niu, Cheng Xu*, Xiao Xiao
• Keywords: Adaptive Genetic Algorithm, Design Optimization, Multi-Disciplinary Simulation, Whole Trajectory

References

[1] Pan Changjun, Guo Yingqing. Complete trajectory modeling and simulation of high altitude air-launched AUV[J]. Journal of System Simulation. 2013, 25(5): 925-930.

[2] Pan Guang, Wu Wenhui, Mao Zhaoyong. Key technologies about complete trajectory simulation for high altitude long-range gliding torpedo[J]. Torpedo Technology, 2009, 17(4): 10-15.

[3] Wang Yali, San Bingbing, Zhu Zhaoquan. Multi-objective optimization of airship structures based on genetic algorithm[J]. Sichuan Building Science, 2014, 40(1): 39-42.

[4] Nie Songhui, Zhu Bailin, Liao Shutao. Optimization design of centrifugal pump based on genetic algorithm[J]. Journal of Machine Design, 2013, 30(12): 19-22.

[5] Kuang Hangyu, Jin Jing, Su Yong. Improving Crossover and Mutation for Adaptive Genetic Algorithm[J]. Computer Engineering and Applications, 2006, 12: 93-96.

[6] Li Junmin, Li Hu. Application of control parameters optimization of CNC servo system based on self-adaptive genetic algorithm[J]. Journal of Northwest University (Natural Science Edition), 2011, 41(3): 430-434.

[7] Hong Yajun, Cao Yanfeng, Yin Qiang. Research on integrated optimization method of barrel and recoil mechanisms[J]. China Mechanical Engineering, 2013, 24(1): 15-20.