This paper proposes an effective inertia coefficient (EIC) in the Morison equation for better wave-force calculations. The OC4 semi-submersible floating offshore wind turbine (FOWT) platform was considered to test the feasibility. Large diffraction at large Keulegan– Carpenter (KC) numbers and the interaction between columns can result in errors in estimating the wave force using the Morison equation with a theoretical inertia coefficient, which can be corrected by the EIC as a function of the wave period and direction. The horizontal and vertical wave forces were calculated using the Morison equation and potential theory at each column, wave period, and wave direction. The EICs of each column were then obtained, resulting in a minimal difference between the Morison inertia force and the wave excitation force by the potential theory. The EICs, wave forces, phase angles, and dynamic motions were compared to confirm the feasibility of an EIC concept under regular and random waves.

This study introduces a data-driven and machine learning (ML)-based methodology for converting the encounter wave frequency spectrum to the original wave spectrum, a critical process for navigating vessels with forward speed in various control and adjustment missions. The spectral conversion from the encounter- to original-frequency domain faces challenges under certain wave conditions due to the non-uniqueness of the inverse problem. To resolve these challenges, the authors developed an artificial neural network (ANN) model that transforms the encounter-frequency spectrum into the original wave spectrum at a given vessel speed and wave direction. The model was trained and validated using a large dataset mapped from various JONSWAP wave spectra to the corresponding encounter-frequency spectra for various vessel speeds and wave parameters. The hyperparameters of the ANN model were subsequently tested and optimized. The results demonstrate that the ANN model can effectively predict the original wave spectrum with high accuracy, as evidenced by a favorable R2 value and error distribution analysis. This approach not only enhances the reliability of wave spectrum estimation during maritime navigation but also broadens the capability of real-time operational controls and adjustments.