Abstract
Reducing the carbon emissions of ships and increasing the utilization of marine renewable energy are the important ways to achieve the goal of carbon neutrality in ocean engineering. Establishing an accurate mathematical model is the foundation of simulating the motion of marine vehicles and structures, and it is the basis of operation energy efficiency optimization and prediction of power generation. System identification from observed input–output data is a practical and powerful method. However, for modeling objects with different characteristics and known information, a single modeling framework can hardly meet the requirements of model establishment. Moreover, there are some challenges in system identification, such as parameter drift and overfitting. In this work, three robust methods are proposed for generating ocean hydrodynamic models based on Bayesian regression. Two Bayesian techniques, semi-conjugate linear regression and noisy input Gaussian process regression are used for parametric and nonparametric gray-box modeling and black-box modeling. The experimental free-running tests of the KRISO very large crude oil carrier (KVLCC2) ship model and a multi-freedom wave energy converter (WEC) are used to validate the proposed Bayesian models. The results demonstrate that the proposed schemes for system identification of the ship and WEC have good generalization ability and robustness. Finally, the developed modeling methods are evaluated considering the aspects required conditions, operating characteristics, and prediction accuracy.
Highlights
Introduction published maps and institutional affilThe demand for mitigating anthropogenic CO2 emissions increasingly focuses on the transportation system and energy system
The results show that the proposed scheme is more robust than ordinary SVM and has the potential to be further applied to other marine equipment
The experimental data used in this article are not from experiments specially designed for system identification, so the excitation signal of the training data is not enough
Summary
The classical kinematic model in naval architecture is motivated by Newton’s second law, and the rigid-body kinemics equations can be expressed in vector form as [48]. Where MRB is the rigid-body inertia matrix; CRB (V ) is a matrix of rigid-body Coriolis and centripetal terms; and τRB is a vector of generalized forces containing hydrodynamic water resistance, τh , environmental forces, τenv , and control forces, τcontrol. V denotes the generalized velocity in 6 DOF. The marine dynamic model is essentially a nonlinear autoregressive model with an exogenous input (NARX) system, and the predictions are based on the previous measurements of the input signals and output signals [43].
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