MACHINE LEARNING AND NUMERICAL INVESTIGATION ON DRAG REDUCTION OF UNDERWATER SERIAL MULTI-PROJECTILES

Abstract

The quest to achieve high launching frequency and higher speed is one of the key goals for researchers in the field of underwater projectiles. Many methods have been proposed for reducing the drag force to reach higher speed, such as superhydrophobic surface, compliant surface, and so on. One of the most effective methods is supercavitating drag reduction technology, which can be used to achieve a significant reduction of hydrodynamic drag by wrapping a large, continuous cavity around the underwater projectile. 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. However, the matching relationships of serial multiprojectiles between different conditions (cavitation number, distance between projectiles, etc.) and the drag characteristics have not been investigated, because obtaining the relationship between drag characteristics and different conditions requires simulation time and extensive calculations. In this paper, computational fluid dynamics (CFD) method and machine learning method were coupled to investigate the natural supercavitation phenomenon of the serial projectiles to reduce the computational cost and improve the simulation accuracy. 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. The results predicted by ANN are in good agreement with the data obtained by CFD. The findings provide helpful guidance for the research on the drag reduction characteristics of underwater serial projectiles. The main conclusions of the study are as follows: (1) The cavity will develop, fuse and collapse in the water and natural supercavitation phenomenon is observed when underwater serial projectiles move at a high speed. The serial multi-projectile structure can reduce the drag of the second projectile and achieve the goal of drag reduction. (2) The length and aspect ratio of supercavity around serial projectiles decrease with the decreasing of the cavitation number, and the drag coefficient of the second projectile decreases. The length and aspect ratio of supercavity decrease with the 3 decreasing of the distance between serial projectiles, and the drag coefficient of the second projectile decreases. (3) The drag coefficient of the second projectile predicted from the ANN model has a high accuracy, where the regression R2 values of training, validation, and testing samples are above 0.99. Machine learning has an obvious advantage in simplifying computational complexity and increasing computational efficiency. Furthermore, the discussion of different cases is based on static projectiles. However, in real-world applications, the movement of serial projectiles should be considered. This is the future direction for this work.