Abstract
Online tuning of vessel models based on onboard measurement data can reduce the uncertainties of vessel motion prediction, and therefore potentially increase the safety and cost efficiency for marine operations. Among the uncertain vessel parameters, the roll damping coefficient is very important and highly nonlinear. In reality, roll damping depends on the sea state and vessel condition. This paper proposes two different procedures for tuning the sea state dependent roll damping coefficient together with other uncertain vessel parameters, i.e., 1-step tuning and 2-step tuning procedures. In addition, a roll damping prediction model based on Gaussian process regression is also proposed to predict the roll damping for future sea states based on historical data. The tuning procedure together with the proposed prediction model form an iterative closed loop of continuously improving the knowledge about the roll damping online, also estimating the model uncertainty based on prior knowledge, sampling uncertainties, and the applied kernel. Case studies are presented to demonstrate the procedures.
Highlights
Reliable vessel motion prediction plays a key role for the safety and optimization of maritime and offshore activities
The present paper describes the algorithm for tuning of sea state dependent roll damping coefficient together with other vessel parameters
Gaussian process regression (GPR) is found to be a very promising solution for roll damping modelling and prediction, because (1) it does not require to decide the format of the roll damping function; (2) the tuned values of roll damping for the previous sea states and vessel conditions can reasonably influence the prediction of roll damping for future sea states and vessel conditions, through the covariance function; (3) it indicates the estimation uncertainty based on the prior knowledge, the available samples, and the selected kernel function
Summary
Reliable vessel motion prediction plays a key role for the safety and optimization of maritime and offshore activities. Tuning of the uncertain vessel parameters in a probabilistic approach can improve the knowledge about the real-time vessel condition and reduce the model uncertainties quantitatively, based on onboard vessel motion measurements and wave information such as Hs, Tp, βW , directional spreading, and spectral shape. The present paper describes the algorithm for tuning of sea state dependent roll damping coefficient together with other vessel parameters It is even more important for this paper to establish an algorithm which prescribes how to model the roll damping as sea state dependent and predict it for the unobserved future sea states. The algorithm should be able to predict the roll damping for the unobserved sea states with improved accuracy based on prior knowledge and historical tuning results for different sea states and vessel conditions.
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