A Coupled SVM-NODE Model for Efficient Prediction of Ship Roll Motion

Traditional analyses of ship roll damping and added moment of inertia rely on free roll decay and forced roll tests, but acquiring linear (small angles) and nonlinear (large angles) relationships demands extensive computational cases and parameter fitting, limiting efficiency. To address this, this study couples Support Vector Machine (SVM) and Neural Ordinary Differential Equation (NODE) networks: SVM solves for added moment of inertia, linear damping, and nonlinear damping, while NODE constructs a complete model for the roll motion equation. Using the DTMB5415 hull form, Computational Fluid Dynamics (CFD) simulations of forced roll build a “time-angle-moment” sample space, and the coupled model learns and predicts free roll decay under different initial angles. The results show that SVM effectively determines roll damping and added moment of inertia from constant-amplitude variable-frequency and constant-frequency variable-amplitude data, reducing required cases significantly. NODE’s simulation of free roll decay validates coefficient accuracy. Within a certain angle range, the SVM-NODE model meets rapid roll motion analysis needs, providing an innovative method for ship roll research and engineering.