Gaussian process metamodels for floating offshore wind turbine platforms. (1st January 2023) - Rajiv, Gayathry; Verma, Mohit; Subbulakshmi, A.
Abstract: The dynamics of floating offshore wind turbines (FOWT) are quite complex as it involves interactions in multiple domains. The dynamic response of FOWT is numerically evaluated using coupled aero-hydro-servo-elastic analysis. In the design stages, the dynamic analysis of FOWT is carried out for different environmental conditions. These analyses can be time-consuming and computationally expensive. This paper proposes the use of metamodels based on the Gaussian process for representing the hydrodynamics and structural dynamics of the floating platform. The input to Gaussian process metamodels is taken as wave elevation, forces and moments at the interface of the tower and platform. The output of the metamodels is the displacements (surge, sway and heave) and the rotations (roll, pitch and yaw) of the platform. Three different Gaussian process metamodels are considered in this paper — Standard Gaussian Process, Variational Heteroscedastic Gaussian Process and Sparse Spectrum Gaussian Process. The construction of the metamodels is demonstrated for an NREL 5MW turbine supported on an ITI barge platform. The performance of the Gaussian metamodels is compared in terms of evaluation indices, convergence, residuals and computational efficiency. The sensitivity analysis of the metamodels is carried out to verify that the interdependence between the variables is captured accurately. All three Gaussian process metamodels are found to be effective in emulating the response of the platform. These models can be effectively used in situations where simple and computationally inexpensive models are required. Highlights: The dynamics of the floating platform are emulated using Gaussian process metamodels. Three different metamodels are considered. The construction of the metamodels is demonstrated for ITI barge platform. Performance assessment of metamodels is carried out to compare their prediction ability. Gaussian metamodels are found to be accurate and computationally efficient.