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A practical model is developed to investigate the performance of a hybrid system with semi-submersible floating offshore wind turbines (FOWT) coupled to an array of point-absorbing wave energy converters (WECs). In this study, the boundary value problem is solved by applying the matching-method of eigenfunctions to solve a complex-shaped hybrid system and the velocity potential can be decomposed into radiation and diffraction problems. For each component in the structure, we consider it consists of three coaxial cylinders of different dimensions, making our mathematical model applicable to many marine structures. Within the framework of a linearized theory, We develop the coupled equations of motion to model the stiffness and damping constraints and to evaluate the effect of coupled motion between the floating platform and vertical truncated cylinders, taking into account wind forces, mooring lines, power take-off (PTO) systems and viscous effects on the hybrid system. For such a system, the combination of the OC4-DeepCwind platform with an array of point-absorbing WECs is investigated in this study. After running the convergence analysis and model validation, the present model is employed to perform a multiparameter effect analysis. Case studies are presented to clarify the effects of WEC parameters (i.e. radius, draft, PTO damping and layout), base column submergence, wave heading and frequency on the motion response of wind platform and mean capture width ratio of the WECs array. Our results provide insights into the relationship between the variables analyzed and the performance of the hybrid system. Moreover, the theoretical model developed in this study accurately calculates the hydrodynamic coefficients and motion performance of some marine structures. • A practical model was presented to accurately calculate the hydrodynamic coefficients. • A coupled motion equation was developed to model the rigidity and damping constraints in a hybrid system. • The study examined the synergistic effects between a wind platform and PTO damping. • Added WECs significantly reduce the pitch motion response of the wind platform. • Increasing the submergence depth of the base column has a negative impact on the hybrid system. [ABSTRACT FROM AUTHOR]

Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, obtaining precise load statistics often involves computationally intensive simulations. Furthermore, the estimation of statistics using current practices is subject to ongoing discussion due to the inherent uncertainty involved. To address these challenges, we present a novel machine learning framework that leverages data‐driven surrogate modeling to predict hydrodynamic loads on monopile foundations while reducing reliance on costly simulations and facilitate the load statistics reconstruction. The primary advantage of our approach is the significant reduction in evaluation time compared to traditional modeling methods. The novelty of our framework lies in its efficient construction of the surrogate model, utilizing the Gaussian process regression machine learning technique and a Bayesian active learning method to sequentially sample wave episodes that contribute to accurate predictions of extreme hydrodynamic forces. Additionally, a spectrum transfer technique combines computational fluid dynamics (CFD) results from both quiescent and extreme waves, further reducing data requirements. This study focuses on reducing the dimensionality of stochastic irregular wave episodes and their associated hydrodynamic force time series. Although the dimensionality reduction is linear, Gaussian process regression successfully captures high‐order correlations. Furthermore, our framework incorporates built‐in uncertainty quantification capabilities, facilitating efficient parameter sampling using traditional CFD tools. This paper provides comprehensive implementation details and demonstrates the effectiveness of our approach in delivering reliable statistics for hydrodynamic loads while overcoming the computational cost constraints associated with classical modeling methods. [ABSTRACT FROM AUTHOR]