基于BP神经网络的超空泡射弹优化设计方法

巩世龙; 党建军; 李少星; 黄闯

doi:10.12382/bgxb.2024.0496

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基于BP神经网络的超空泡射弹优化设计方法

更新时间：2025-09-05

- 基于BP神经网络的超空泡射弹优化设计方法
- Optimization Design Method of the Supercavitating Projectile Based on BP Neural Network
- 兵工学报 2025年46卷第5期页码：240496
- 作者机构：
  
  1. 西北工业大学航海学院, 陕西西安 710072
  2. 陆军装备部驻西安地区军事代表局, 陕西西安 710032
- 作者简介：
  
  * 邮箱: huangchuang@nwpu.edu.cn
- 基金信息：
  
  特色学科基础研究项目(G2024WD0134)
- DOI：10.12382/bgxb.2024.0496
  中图分类号：
- 收稿：2024-06-24，
  
  网络出版：2025-05-07，
  
  纸质出版：2025-05-31
- 稿件说明：
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巩世龙, 党建军, 李少星, 等. 基于BP神经网络的超空泡射弹优化设计方法[J]. 兵工学报, 2025,46(5):240496.

Shilong GONG, Jianjun DANG, Shaoxing LI, et al. Optimization Design Method of the Supercavitating Projectile Based on BP Neural Network[J]. Acta Armamentarii, 2025, 46(5): 240496.
巩世龙, 党建军, 李少星, 等. 基于BP神经网络的超空泡射弹优化设计方法[J]. 兵工学报, 2025,46(5):240496. DOI： 10.12382/bgxb.2024.0496.

Shilong GONG, Jianjun DANG, Shaoxing LI, et al. Optimization Design Method of the Supercavitating Projectile Based on BP Neural Network[J]. Acta Armamentarii, 2025, 46(5): 240496. DOI： 10.12382/bgxb.2024.0496.

摘要

有效射程是超空泡射弹最重要的性能指标之一

受到外形和衡重参数的耦合影响。为了增加超空泡射弹的有效射程

建立计算超空泡射弹有效射程的数值模型

根据正交试验设计原则设计四因素五水平工况组合

通过仿真计算获得外形及衡重参数影响下的超空泡射弹有效射程数据集

结合反向传播 (Back Propagation

BP)神经网络方法和遗传算法

建立超空泡射弹设计参数优化方法

获得全域最大有效射程及其对应的外形和衡重参数设计结果。研究结果表明:超空泡射弹的水下弹道具有稳定的尾拍特性

通过极差分析

质量对有效射程的影响最大;在没有精确数学模型的情况下

运用BP神经网络

基于有限个数据点训练出的有效射程预测模型精度高

平均误差为0.735%;通过遗传算法获得了四因素耦合影响下的全域最优射程

较数据集中的最好结果提高了5.01%

较正交优化结果提升了1.95%。所得研究结果可为超空泡射弹总体设计工作提供参考。

Abstract

Effective range is one of the most important performance indexes of supercavitating projectiles

which is influenced by the coupling of shape and weight parameters. In order to increase the effective range of supercavitating projectile

a numerical model for calculating the effective range of supercavitating projectiles is established

and a combination of four factors and five levels is designed according to the principle of orthogonal test design. The effective range data set of supercavitating projectiles under the influence of shape and weight parameters is obtained by simulation calculation

and an optimization method of design parameters of supercavitating projectiles is established by using BP(back propagation) neural network method and genetic algorithm

and the maximum effective range of supercavitating projectile and its corresponding shape and weight parameters are obtained. The results show that the underwater trajectory of supercavitating projectile has a stable tail beat characteristic. The mass has the greatest impact on the effective range through range analysis In the absence of precise mathematical model

the accuracy of the effective range prediction model trained by BP neural network based on limited data points is high with the average error of 0.735%. The optimal range of the whole domain under the influence of four-factors coupling is obtained by genetic algorithm. The range is improved by 5.01% compared with the best result of data set

and by 1.95% compared with the result of orthogonal optimization. The research results can providereference for the overall design of supercavitating projectile.

关键词

Keywords

references

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