Acta Armamentarii ›› 2025, Vol. 46 ›› Issue (5): 240496-.doi: 10.12382/bgxb.2024.0496
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GONG Shilong1, DANG Jianjun1, LI Shaoxing2, HUANG Chuang1,*()
Received:
2024-06-24
Online:
2025-05-07
Contact:
HUANG Chuang
CLC Number:
GONG Shilong, DANG Jianjun, LI Shaoxing, HUANG Chuang. Optimization Design Method of the Supercavitating Projectile Based on BP Neural Network[J]. Acta Armamentarii, 2025, 46(5): 240496-.
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水平 | Dn/mm | Lt/mm | G/kg | Jz/(kg·mm2) |
---|---|---|---|---|
1 | 5.00 | 30 | 0.753 | 3364.1 |
2 | 5.25 | 27 | 0.828 | 3700.5 |
3 | 5.50 | 33 | 0.903 | 4036.9 |
4 | 5.75 | 36 | 0.668 | 3027.7 |
5 | 6.00 | 39 | 0.593 | 2691.3 |
Table 1 Projectile parameters and level
水平 | Dn/mm | Lt/mm | G/kg | Jz/(kg·mm2) |
---|---|---|---|---|
1 | 5.00 | 30 | 0.753 | 3364.1 |
2 | 5.25 | 27 | 0.828 | 3700.5 |
3 | 5.50 | 33 | 0.903 | 4036.9 |
4 | 5.75 | 36 | 0.668 | 3027.7 |
5 | 6.00 | 39 | 0.593 | 2691.3 |
编号 | 序列 | 初始速度 v0/(m·s-1) | 剩余速度 vr/(m·s-1) | 有效射程 Lr/m |
---|---|---|---|---|
1 | A1B1C1D1 | 320 | 200 | 49.73 |
2 | A1B2C2D2 | 305 | 191 | 51.27 |
3 | A1B3C3D3 | 292 | 182 | 53.30 |
4 | A1B4C4D4 | 340 | 212 | 47.23 |
5 | A1B5C5D5 | 360 | 225 | 44.19 |
6 | A2B1C2D3 | 305 | 191 | 52.99 |
7 | A2B2C3D4 | 292 | 182 | 51.67 |
8 | A2B3C4D5 | 340 | 212 | 45.49 |
9 | A2B4C5D1 | 360 | 225 | 44.88 |
10 | A2B5C1D2 | 320 | 200 | 53.75 |
11 | A3B1C3D5 | 292 | 182 | 54.26 |
12 | A3B2C4D1 | 340 | 212 | 45.38 |
13 | A3B3C5D2 | 360 | 225 | 44.35 |
14 | A3B4C1D3 | 320 | 200 | 48.26 |
15 | A3B5C2D4 | 305 | 191 | 51.64 |
16 | A4B1C4D2 | 340 | 212 | 44.86 |
17 | A4B2C5D3 | 360 | 225 | 40.96 |
18 | A4B3C1D4 | 320 | 200 | 47.05 |
19 | A4B4C2D5 | 305 | 191 | 49.68 |
20 | A4B5C3D1 | 292 | 182 | 53.62 |
21 | A5B1C5D4 | 360 | 225 | 37.49 |
22 | A5B2C1D5 | 320 | 200 | 43.25 |
23 | A5B3C2D1 | 305 | 191 | 49.59 |
24 | A5B4C3D2 | 292 | 182 | 52.33 |
25 | A5B5C4D3 | 340 | 212 | 45.38 |
Table 2 Orthogonal test results
编号 | 序列 | 初始速度 v0/(m·s-1) | 剩余速度 vr/(m·s-1) | 有效射程 Lr/m |
---|---|---|---|---|
1 | A1B1C1D1 | 320 | 200 | 49.73 |
2 | A1B2C2D2 | 305 | 191 | 51.27 |
3 | A1B3C3D3 | 292 | 182 | 53.30 |
4 | A1B4C4D4 | 340 | 212 | 47.23 |
5 | A1B5C5D5 | 360 | 225 | 44.19 |
6 | A2B1C2D3 | 305 | 191 | 52.99 |
7 | A2B2C3D4 | 292 | 182 | 51.67 |
8 | A2B3C4D5 | 340 | 212 | 45.49 |
9 | A2B4C5D1 | 360 | 225 | 44.88 |
10 | A2B5C1D2 | 320 | 200 | 53.75 |
11 | A3B1C3D5 | 292 | 182 | 54.26 |
12 | A3B2C4D1 | 340 | 212 | 45.38 |
13 | A3B3C5D2 | 360 | 225 | 44.35 |
14 | A3B4C1D3 | 320 | 200 | 48.26 |
15 | A3B5C2D4 | 305 | 191 | 51.64 |
16 | A4B1C4D2 | 340 | 212 | 44.86 |
17 | A4B2C5D3 | 360 | 225 | 40.96 |
18 | A4B3C1D4 | 320 | 200 | 47.05 |
19 | A4B4C2D5 | 305 | 191 | 49.68 |
20 | A4B5C3D1 | 292 | 182 | 53.62 |
21 | A5B1C5D4 | 360 | 225 | 37.49 |
22 | A5B2C1D5 | 320 | 200 | 43.25 |
23 | A5B3C2D1 | 305 | 191 | 49.59 |
24 | A5B4C3D2 | 292 | 182 | 52.33 |
25 | A5B5C4D3 | 340 | 212 | 45.38 |
影响参数 | Ki1 | Ki2 | Ki3 | Ki4 | Ki5 | Ri |
---|---|---|---|---|---|---|
Dn | 49.14 | 49.56 | 49.50 | 47.64 | 46.01 | 3.55 |
Lt | 46.47 | 46.31 | 47.96 | 50.80 | 50.32 | 4.49 |
G | 48.21 | 51.23 | 53.16 | 45.67 | 42.37 | 10.79 |
Jz | 49.00 | 49.71 | 50.10 | 47.02 | 45.97 | 4.13 |
Table 3 Analysis of orthogonal test results
影响参数 | Ki1 | Ki2 | Ki3 | Ki4 | Ki5 | Ri |
---|---|---|---|---|---|---|
Dn | 49.14 | 49.56 | 49.50 | 47.64 | 46.01 | 3.55 |
Lt | 46.47 | 46.31 | 47.96 | 50.80 | 50.32 | 4.49 |
G | 48.21 | 51.23 | 53.16 | 45.67 | 42.37 | 10.79 |
Jz | 49.00 | 49.71 | 50.10 | 47.02 | 45.97 | 4.13 |
隐含层节点数 | 平均错误率/% | 隐含层节点数 | 平均错误率/% |
---|---|---|---|
2 | 3.554 | 8 | 0.761 |
3 | 1.916 | 9 | 0.795 |
4 | 1.275 | 10 | 0.823 |
5 | 0.872 | 11 | 0.816 |
6 | 0.745 | 12 | 0.779 |
7 | 0.758 | 13 | 0.805 |
Table 4 The average error rate corresponding to the number of nodes in different hidden layers
隐含层节点数 | 平均错误率/% | 隐含层节点数 | 平均错误率/% |
---|---|---|---|
2 | 3.554 | 8 | 0.761 |
3 | 1.916 | 9 | 0.795 |
4 | 1.275 | 10 | 0.823 |
5 | 0.872 | 11 | 0.816 |
6 | 0.745 | 12 | 0.779 |
7 | 0.758 | 13 | 0.805 |
结果及误差 | 正交优化射弹 | 遗传优化射弹 |
---|---|---|
数值计算结果/m | 55.89 | 56.98 |
BP神经网络结果/m | 55.63 | 57.21 |
误差/% | 0.48 | 0.40 |
Table 5 Comparative validation of predictive models
结果及误差 | 正交优化射弹 | 遗传优化射弹 |
---|---|---|
数值计算结果/m | 55.89 | 56.98 |
BP神经网络结果/m | 55.63 | 57.21 |
误差/% | 0.48 | 0.40 |
[1] |
刘立栋, 张宇文, 滕鹏桦. 水下高速射弹超空泡运动建模与仿真[J]. 应用力学学报, 2011, 28(5): 470-474, 553.
|
|
|
[2] |
|
[3] |
魏英杰, 何乾坤, 王聪, 等. 超空泡射弹尾拍问题研究进展[J]. 舰船科学技术, 2013, 35(1):7-15.
|
|
|
[4] |
|
[5] |
古鉴霄, 党建军, 黄闯, 等. 衡重参数对超空泡射弹有效射程的影响[J]. 兵工学报, 2022, 43(6): 1376-1386.
doi: 10.12382/bgxb.2021.0319 |
|
|
[6] |
赵成功, 王聪, 魏英杰, 等. 质心位置对超空泡射弹尾拍运动影响分析[J]. 北京航空航天大学学报, 2014, 40(12): 1754-1760.
|
|
|
[7] |
邹多艺佳, 朱墨, 蔡希文, 等. 空化器锥角对超空泡射弹阻力与弹道影响数值研究[J]. 兵器装备工程学报, 2023, 44(12):54-62.
|
|
|
[8] |
|
[9] |
郭晓宇. 空化器对通气超空泡流影响的数值研究[D]. 哈尔滨: 哈尔滨工程大学, 2023.
|
|
|
[10] |
刘如石, 郭则庆, 张辉. 尾部形状对超空泡射弹尾拍运动影响的数值研究[J]. 兵工学报, 2023, 44(10): 2984-2994.
doi: 10.12382/bgxb.2022.0689 |
|
|
[11] |
陈伟善. 高速超空泡射弹尾拍运动特性数值研究[D]. 南京: 南京理工大学, 2020.
|
|
|
[12] |
黄闯. 跨声速超空泡射弹的弹道特性研究[D]. 西安: 西北工业大学, 2017.
|
|
|
[13] |
王颖. 桩锚支护深基坑变形的有限元分析与神经网络预测[D]. 广州: 广东工业大学, 2014.
|
|
|
[14] |
李金晟, 常思江, 陈升富. 基于神经网络算法的弹丸阻力系数辨识[J]. 弹道学报, 2018, 30(4):38-43.
doi: 10.12115/j.issn.1004-499X(2018)04-007 |
|
|
[15] |
吴朝峰, 杨臻, 曹文辉, 等. 基于GA-BP算法的外弹道落点误差预测[J]. 兵器装备工程学报, 2019, 40(12):67-71.
|
|
|
[16] |
郝博, 刘力维, 谷继明. 基于麻雀搜索算法优化BP神经网络的弹丸射程预测研究[J]. 火炮发射与控制学报, 2024, 45(1):10-15.
|
|
|
[17] |
胥涯杰, 鲜勇, 李邦杰. 基于BP神经网络改进遗传算法的导弹总体参数快速优化方法[J]. 电光与控制, 2022, 29(2):20-24.
|
|
|
[18] |
|
[19] |
|
[20] |
黄宝珠, 李代金, 黄闯, 等. 材料密度对超空泡射弹尾拍特性的影响[J]. 水下无人系统学报, 2023, 31(2):211-220.
|
|
|
[21] |
祝许皓, 李杰. 超空泡射弹六自由度尾拍运动数值模拟研究[J]. 水动力学研究与进展A辑, 2022, 37(4):474-482.
|
|
|
[22] |
|
[23] |
梁超, 张熊, 米高阳, 等. 基于神经网络与遗传算法的铝合金激光摆动焊工艺参数优化[J]. 电焊机, 2022, 52(8):43-49,64.
|
|
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