TY - JOUR

T1 - 小口径超空泡子弹头部外形的优化设计

AU - Ma, Wenxuan

AU - Yu, Yong

AU - Hu, Jun

N1 - Publisher Copyright:
© 2022, Editorial Staff of EXPLOSION AND SHOCK WAVES. All right reserved.

PY - 2022/3/5

Y1 - 2022/3/5

N2 - When a small-caliber projectile is moving underwater at a high speed, the water around the projectile will cavitate. The cavitation effect can greatly reduce the resistance of the moving vehicle, and the geometric shape of the warhead with the best drag coefficient corresponds to the supercavitating state where the projectile is completely enveloped by cavitation. Aiming at a small-caliber projectile, the computational fluid dynamics method is used to numerically simulate the gas-liquid two-phase flow with cavitation phenomenon, while the relationships of the cavitation shape and the drag coefficient with the geometry of the projectile's head shape are explored. The three-segment cone type is selected as the basic projectile type, and the shape of the projectile is optimized by step optimization method. First, seven parameters are used to describe the three-segment cone shape of the projectile, and then the projectile is optimized in the order of the first section cone, the second and the third section cone. This method is used because the seven parameters are not independent of each other, and it is difficult to quantitatively determine the relationship between an individual parameter and the performance of the projectile. At the same time, the neural network is employed to perform nonlinear fitting with a large number of CFD numerical simulation results as learning samples, and the approximate calculation model of the shape parameters-drag coefficient of the projectile is established by neural network. Finally, the sequential quadratic programming (SQP) algorithm is introduced to find the optimal solution of the approximate calculation model. The use of neural network and SQP algorithm reduces the amount of calculation in the optimization process and the total time required for optimization work. After two rounds of optimization, the optimized projectile has a better ability to form supercavitation, and its drag coefficient has also been significantly improved compared to the original projectile, with a reduction about 30% compared to the projectile before optimization.

AB - When a small-caliber projectile is moving underwater at a high speed, the water around the projectile will cavitate. The cavitation effect can greatly reduce the resistance of the moving vehicle, and the geometric shape of the warhead with the best drag coefficient corresponds to the supercavitating state where the projectile is completely enveloped by cavitation. Aiming at a small-caliber projectile, the computational fluid dynamics method is used to numerically simulate the gas-liquid two-phase flow with cavitation phenomenon, while the relationships of the cavitation shape and the drag coefficient with the geometry of the projectile's head shape are explored. The three-segment cone type is selected as the basic projectile type, and the shape of the projectile is optimized by step optimization method. First, seven parameters are used to describe the three-segment cone shape of the projectile, and then the projectile is optimized in the order of the first section cone, the second and the third section cone. This method is used because the seven parameters are not independent of each other, and it is difficult to quantitatively determine the relationship between an individual parameter and the performance of the projectile. At the same time, the neural network is employed to perform nonlinear fitting with a large number of CFD numerical simulation results as learning samples, and the approximate calculation model of the shape parameters-drag coefficient of the projectile is established by neural network. Finally, the sequential quadratic programming (SQP) algorithm is introduced to find the optimal solution of the approximate calculation model. The use of neural network and SQP algorithm reduces the amount of calculation in the optimization process and the total time required for optimization work. After two rounds of optimization, the optimized projectile has a better ability to form supercavitation, and its drag coefficient has also been significantly improved compared to the original projectile, with a reduction about 30% compared to the projectile before optimization.

KW - Drag coefficient

KW - Multi-step optimization

KW - Neural network

KW - SQP algorithm

KW - Supercavitating projectile

UR - http://www.scopus.com/inward/record.url?scp=85127745636&partnerID=8YFLogxK

U2 - 10.11883/bzycj-2021-0092

DO - 10.11883/bzycj-2021-0092

M3 - 文章

AN - SCOPUS:85127745636

SN - 1001-1455

VL - 42

JO - Baozha Yu Chongji/Expolosion and Shock Waves

JF - Baozha Yu Chongji/Expolosion and Shock Waves

IS - 3

M1 - 033305

ER -