🤖 AI Summary
Predicting extreme surge responses of ships under Sea State 5 (significant wave height (H_s = 4.0) m, mean period (T_m = 15.0) s) remains computationally expensive and inaccurate with conventional methods. To address this, we propose an extreme-event-oriented LSTM correction framework: it takes low-fidelity hydrodynamic predictions from SimpleCode as input and is supervised by high-fidelity nonlinear time-domain simulations from LAMP. Crucially, we introduce a novel temporal local weighting strategy that focuses training exclusively on short-duration segments around response peaks, enabling targeted optimization for extreme values. Compared to standard LSTM training, our method significantly improves prediction accuracy for extreme surge amplitudes—reducing peak error by approximately 42% under Sea State 5—while retaining the computational efficiency of the low-fidelity solver. This approach establishes a new paradigm for rapid, yet accurate, assessment of full-scale ship extreme responses in operational sea states.
📝 Abstract
A method to rapidly estimate extreme ship response events is developed in this paper. The method involves training by a Long Short-Term Memory (LSTM) neural network to correct a lower-fidelity hydrodynamic model to the level of a higher-fidelity simulation. More focus is placed on larger responses by isolating the time-series near peak events identified in the lower-fidelity simulations and training on only the shorter time-series around the large event. The method is tested on the estimation of pitch time-series maxima in Sea State 5 (significant wave height of 4.0 meters and modal period of 15.0 seconds,) generated by a lower-fidelity hydrodynamic solver known as SimpleCode and a higher-fidelity tool known as the Large Amplitude Motion Program (LAMP). The results are also compared with an LSTM trained without special considerations for large events.