Optimal design of the head shape of a small-caliber supercavitating projectile

MA Wenxuan; YU Yong; HU Jun

doi:10.11883/bzycj-2021-0092

Volume 42 Issue 3

Apr. 2022

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MA Wenxuan, YU Yong, HU Jun. Optimal design of the head shape of a small-caliber supercavitating projectile[J]. Explosion And Shock Waves, 2022, 42(3): 033305. doi: 10.11883/bzycj-2021-0092

Citation:

MA Wenxuan, YU Yong, HU Jun. Optimal design of the head shape of a small-caliber supercavitating projectile[J]. Explosion And Shock Waves, 2022, 42(3): 033305. doi: 10.11883/bzycj-2021-0092

MA Wenxuan, YU Yong, HU Jun. Optimal design of the head shape of a small-caliber supercavitating projectile[J]. Explosion And Shock Waves, 2022, 42(3): 033305. doi: 10.11883/bzycj-2021-0092

Citation:

MA Wenxuan, YU Yong, HU Jun. Optimal design of the head shape of a small-caliber supercavitating projectile[J]. Explosion And Shock Waves, 2022, 42(3): 033305. doi: 10.11883/bzycj-2021-0092

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Optimal design of the head shape of a small-caliber supercavitating projectile

doi: 10.11883/bzycj-2021-0092

School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China

Received Date: 2021-03-18
Rev Recd Date: 2021-09-17

Available Online: 2022-02-12

Publish Date: 2022-04-07

Abstract

Abstract

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.
- supercavitating projectile,
- drag coefficient,
- multi-step optimization,
- neural network,
- SQP algorithm

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References(23)

References

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