Abstract: The chain-structured underwater vehicle is composed of several autonomous underwater vehicles in series. It has advantages in terms of navigation efficiency, stability, and carrying capacity. The motion can be better controlled and the power can be better organized if its direct route drag can be predicted accurately. Aiming at the problem that the drag prediction of chain-structured underwater vehicle cannot be carried out quickly and accurately because of the complex coupling relationship among the units and the long time-consuming of computational fluid dynamics (CFD) analysis, the research on the direct route drag prediction carried out. A large number of input (number of units, velocity and spacing between units) and output (direct route drag) sample data are obtained by using CFD. BP neural network is used to establish the relationship between input and output. Particle swarm optimization is used to optimize the initial weights and biases of the neural network to improve the problem that BP neural network is easy to fall into local extreme points and over-fitting. The prediction results of a large number of test samples show that the BP neural network algorithm based on particle swarm optimization is more accurate than the traditional BP neural network algorithm, and the mean square error is reduced by 2.04×10^-5 and 7.4×10^-6 respectively in the tests of given different velocities and spacing. The average relative error of BP neural network model optimized by particle swarm optimization is 0.42% during the uniform acceleration of the 5-unit chain-structured underwater vehicle with an acceleration of 0.25 m/s². The accuracy of prediction results is high. The experimental results show that the proposed method is feasible and effective.

Key words: chain-structured underwater vehicle, neural network, particle swarm optimization, drag prediction