Data-driven study enhances wave load predictions in coastal marine structures, suggesting improved forecasting methods.
Coastal marine structures are exposed to complex wave fields after nonlinear wave transformations due to the varying bathymetry and irregular coastlines. In the previous efforts to define the coastal design sea states and analyze hydrodynamic loads on coastal structures, the down-scale NORA-SARAH procedure has been developed. The hindcast database NORA3, the phase-averaging wave model SWAN, the phase-resolving wave model REEF3D, the arbitrary Eulerian-Lagrangian (ALE) fast force calculation method and the hydrodynamic coupling (HDC) are deployed to complete this cascade down-scale process. This work presents a data-driven study on complex coastal wave and wave load prediction to complement the NORA-SARAH procedure. The work attempts to use a large quantity of numerical simulations with various offshore met-ocean inputs in southern Norway close to Kristiansand for coastal wave and load predictions. These synthetic simulation data of the coastal waves, including phase-resolving surface elevations and the ALE-based force estimations are used to train a neural network-based machine learning (ML) algorithm. This artificial intelligence (AI)-powered procedure examines the capability, efficiency and possibility for operational phase-resolved ocean wave and wave load forecasting for coastal marine structures.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: