Dynamic analysis of vessel/riser/equipment system for deep-sea mining with RBF neural network approximations

Deep-sea mining (DSM) is an advanced technology. This article is focused on the dynamic analysis of a coupled vessel/riser/equipment system of a DSM based on radial basis function (RBF) neural network approximations while considering vessel dynamic positioning (DP) and active heave compensation (AHC). A coupled model including the production support vessel (PSV), lifting riser, and slurry pump is established containing simulated DP and AHC models. Furthermore, dynamic simulations are implemented to obtain the results of the vessel motions, thruster forces, pump motions and riser tensions. Using optimal Latin hypercube sampling, an RBF neural network approximation model is established, the input includes environmental factors and the output includes the dynamic responses of the pump motion and riser tension. Calculations are performed using RBF network approximations instead of a coupled model. The obtained results show that the PSV wave frequency (WF) motions have significant influence on the dynamic responses of the subsea system. Moreover, the current load affects the compensation effect. The RBF network approximation model can be used to reduce the required calculation time.