Machine learning and numerical investigation on drag reduction of underwater serial multi-projectiles

Abstract

To increase launching frequency and decrease drag force of underwater projectiles, a serial multi-projectiles structure based on the principle of supercavitation is proposed in this paper. The drag reduction and supercavitation characteristics of the underwater serial multi-projectiles are studied with computational fluid dynamics (CFD) and machine learning. Firstly, the numerical simulation model for the underwater supercavitating projectile is established and verified by experimental data. Then the evolution of the supercavitation for the serial multi-projectiles is described. In addition, the effects of different cavitation numbers and different distances between projectiles are investigated to demonstrate the supercavitation and drag reduction performance. Finally, the artificial neural network (ANN) model is established to predict the evolution of drag coefficient based on the data obtained by CFD, and the results predicted by ANN are in good agreement with the data obtained by CFD. The finding provides a useful guidance for the research of drag reduction characteristics of underwater serial projectiles.

Keywords

• Drag reduction
• Serial multi-projectiles
• Machine learning
• Artificial neural network (ANN)
• Numerical simulation