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 (viscous 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.
This work presents the verification and validation of the freely available simulation tool MoodyMarine, developed to help meet some of the demands for early stage development of MRE devices. MoodyMarine extends the previously released mooring module MoodyCore (Discontinuous Galerkin Finite Elements) with linear radiation-diffraction bodies, integrated pre-processing workflows and a graphical user interface. It is a C++ implementation of finite element mooring dynamics and Cummins equations for floating bodies with weak nonlinear corrections. A newly developed nonlinear Froude-Krylov implementation is verified in the paper, and MoodyMarine is compared to CFD simulations for two complex structures: a slack-moored floating offshore wind turbine and a self-reacting point-absorber with hybrid mooring.