Vessel hydrodynamic model tuning by Discrete Bayesian updating using simulated onboard sensor data

Vessel and wave hydrodynamics are fundamental for vessel motion prediction. Improving hydrodynamic model accuracy without compromising computational efficiency has always been of high interest for safe and cost-effective marine operations. With continuous development of sensor technology and computational capacity, an improved digital twin concept for vessel motion prediction can be realized based on an onboard online adaptive hydrodynamic model. This article proposes and demonstrates a practical approach for tuning of important vessel hydrodynamic model parameters based on simulated onboard sensor data of vessel motion response. The algorithm relies fundamentally on spectral analysis, probabilistic modelling and the discrete Bayesian updating formula. All case studies show promising and reasonable tuning results. Sensitivities of the approach with respect to its key parameters were also studied. Sensor noise has been considered. The algorithm is found to be computationally efficient, robust and stable when tuning the values of hydrodynamic parameters and updating their uncertainties, within reasonable sensor noise levels.

Automated summary

This article proposes a practical method for adjusting important vessel hydrodynamic model parameters based on simulated onboard sensor data of vessel motion response. The algorithm relies on spectral analysis, probabilistic modeling, and the discrete Bayesian updating formula. Experimental results show promising tuning results, high sensitivity to key parameters, and computational efficiency in handling sensor noise.