小口径超空泡子弹头部外形的优化设计

在水下高速运动时，小口径射弹周围的水会发生空化现象，阻力系数最优的弹头几何外形对应着射弹被空泡全包裹的超空泡状态。针对一种小口径射弹，可以利用计算流体力学（CFD）数值方法模拟含空化现象的气液两相流动，探究空泡形态和阻力系数与射弹头部几何外形的关系。选取三段锥形为基本射弹头形，采用分步优化方式对射弹头部外形进行了优化。同时，结合神经网络与序列二次规划（SQP）算法减少优化过程中的计算量，缩短了优化工作所需的总时间。优化后的射弹阻力系数比优化前的减小约30%，且能够形成包裹全弹体的超空泡。

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

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.