This work focuses on the validation of the two-phase Navier-Stokes model interFoam, part of the OpenFOAM finite volume framework, applied to the case of a fixed oscillating water column. The model is validated against experimental results from the Technical University of Denmark. Both twoand one-way absorption, i.e. only the upor downstroke is used to drive the flow through the orifice, are investigated. For the one-way absorption cases a numerical one-way valve is employed. The results show a good fit to the experimental values for the two-way absorption, but for the one-way absorption the pressure, and subsequently power generation, is off. The reason is unresolved problems with the numerical one-way valve. Further, for the two-way absorption a formal solution verification was performed, showing the simulations had less than 7% numerical uncertainty arising from the spatial discretization

MoodyMarine is a weakly nonlinear potential flow model for wave-body and mooring  simulations with a graphical user interface. In this work we present the extension of the model to deal with constrained multi-body dynamics. By combining different translation and rotation constraints most joints can be modelled. As the constraints are imposed through springs and dampers in the explicit time-stepping algorithm, a slight manual tuning is required to make sure the bodies are constrained properly. Nevertheless, this tuning is shown not to influence the final results. In the paper we compare to existing test cases in literature as well as against experimental data. In all test cases there is a good agreement between the target solutions and MoodyMarine .

Wind power plays an increasingly vital role in sustainable energy development. However, accurately simulating wind turbine aerodynamics, particularly in offshore wind farms, remains challenging due to complex environmental factors such as the marine atmospheric boundary layer. This study investigates the integration and assessment of the Actuator Line Model (ALM) within the high-order spectral/hp element framework, Nektar++, for wind turbine aerodynamic simulations. The primary objective is to evaluate the implementation and effectiveness of the ALM by analyzing aerodynamic loads, wake behavior, and computational demands. A three-bladed NREL-5MW turbine is modeled using the ALM in Nektar++, with results compared against established computational fluid dynamics (CFD) tools, including SOWFA and AMR-Wind. The findings demonstrate that Nektar++ effectively captures velocity and vorticity fields in the turbine wake while providing aerodynamic load predictions that closely align with finite-volume CFD models. Furthermore, the spectral/hp element framework exhibits favorable scalability and computational efficiency, indicating that Nektar++ is a promising tool for high-fidelity wind turbine and wind farm aerodynamic research.

We numerically simulate the hydrodynamic response of a floating offshore wind turbine (FOWT) using computational fluid dynamics. The FOWT under consideration is a slack-moored 1:70 scale model of the UMaine VolturnUS-S semi-submersible platform. The test cases under consideration are (i) static equilibrium load cases, (ii) free decay tests, and (iii) two focused wave cases of different wave steepness. The FOWT is modelled using a two-phase Navier-Stokes solver inside the OpenFOAM-v2006 framework. The catenary mooring is computed by dynamically solving the equations of motion for an elastic cable using the MoodyCore solver. The results are shown to be in good agreement with measurements.

MoodyMarine is a weakly nonlinear potential flow model for wave-body and mooring simulations with a graphical user interface. In this work we present the extension of the model to deal with constrained multi-body dynamics. By combining different translation and rotation constraints most joints can be modelled. As the constraints are imposed through springs and dampers in the explicit time-stepping algorithm, a slight manual tuning is required to make sure the bodies are constrained properly. Nevertheless, this tuning is shown not to influence the final results. In the paper we compare to existing test cases in literature as well as against experimental data. In all test cases there is a good agreement between the target solutions and MoodyMarine.

Linear potential flow (LPF) models remain the tools-of-the trade in marine and ocean engineering despite their well-known assumptions of small amplitude waves and motions. As of now, nonlinear simulation tools are still too computationally demanding to be used in the entire design loop, especially when it comes to the evaluation of numerous irregular sea states. In this paper we aim to enhance the performance of the LPF models by introducing a hybrid LPF-ML (machine learning) approach, based on identification of nonlinear force corrections. The corrections are defined as the difference in hydrodynamic force (vis- cous and pressure-based) between high-fidelity CFD and LPF models. Using prescribed chirp motions with different amplitudes, we train a long short-term memory (LSTM) network to predict the corrections. The LSTM network is then linked to the MoodyMarine LPF model to provide the nonlinear correction force at every time-step, based on the dynamic state of the body and the corresponding forces from the LPF model. The method is illustrated for the case of a heaving sphere in decay, regular and irregular waves – including passive control. The hybrid LPF-model is shown to give significant improvements compared to the baseline LPF model, even though the training is quite generic.

Numerical models used in the design of floating bodies routinely rely on linear hydrodynamics. Extensions for hydrodynamic nonlinearities can be approximated using e.g. Morison type drag and nonlinear Froude-Krylov forces. This paper aims to improve the approximation of nonlinear forces acting on floating bodies by using machine learning (ML). Many ML models are general function approximators and therefore suitable for representing such nonlinear correction terms. A hierarchical modelling approach is used to build mappings between higher-fidelity simulations and the linear method. The ML corrections are built up for FNPF, Euler and RANS simulations. Results for decay tests of a sphere in model scale using recurrent neural networks (RNN) are presented. The RNN algorithm is shown to satisfactory predict the correction terms if the most nonlinear case is used as training data. No difference in the performance of the RNN model is seen for the different hydrodynamic models.

We present a hybrid linear potential flow - machine learning (LPF-ML) model for simulating weakly nonlinear wave-body interaction problems. In this paper we focus on using hierarchical modelling for generating training data to be used with recurrent neural networks (RNNs) in order to derive nonlinear correction forces. Three different approaches are investigated: (i) a baseline method where data from a Reynolds averaged Navier Stokes (RANS) model is directly linked to data from a LPF model to generate nonlinear corrections; (ii) an approach in which we start from high-fidelity RANS simulations and build the nonlinear corrections by stepping down in the fidelity hierarchy; and (iii) a method starting from low-fidelity, successively moving up the fidelity staircase. The three approaches are evaluated for the simple test case of a heaving sphere. The results show that the baseline model performs best, as expected for this simple test case. Stepping up in the fidelity hierarchy very easily introduce errors that propagate through the hierarchical modelling via the correction forces. The baseline method was found to accurately predict the motion of the heaving sphere. The hierarchical approaches struggled with the task, with the approach that steps down in fidelity performing somewhat better of the two.