Identification of Hydrodynamic Coefficients of the SUBOFF Submarine Using the Bayesian Ridge Regression Model

Abstract

Maneuverability is one of the submarine’s most important features, and is closely related to hydrodynamic coefficients. The submarine standard motion equation is the most commonly used hydrodynamic mathematical model, estimating more than 100 hydrodynamic coefficients. This paper applies the Bayesian ridge regression model to identify a submarine’s hydrodynamic coefficients. Specifically, the proposed approach combines the URANS equation with a six-degree-of-freedom motion model and a body force propeller model, and uses overset grid technology to simulate the underwater six-degree-of-freedom navigation motion of the SUBOFF submarine. The submarine’s required hydrodynamic coefficients are obtained by collecting relevant velocity and angular velocity data and applying the Bayesian ridge regression model for data identification. Meanwhile, CFD simulation of the restraint model test for the SUBOFF submarine is conducted to obtain the related hydrodynamic coefficients. Through comparative experiments, we validate that the proposed Bayesian ridge regression model identification method is effective and reliable; most of the errors in the hydrodynamic coefficients were within 10%, with a maximum error of −43.26%, providing more comprehensive and timely hydrodynamic coefficients than traditional CFD restraint model tests. Furthermore, the hydrodynamic coefficient identification results were used to invert the submarine’s spatial motion, and we demonstrated that the resulting trajectory, velocity, and angular velocity curves all fit well.