Toward prediction of turbulent atmospheric flows over propagating oceanic waves via machine-learning augmented large-eddy simulation

Wind-wave interactions have important effects on the energy harvesting of offshore wind farms. High-fidelity large-eddy simulation (LES) is a powerful approach for investigating wind-wave interactions in turbulent oceanic environments. Due to the large scale of the flow domain and the high grid resolution required to resolve multi-scale flow motions, however, brute-force LES of wind-wave interactions is computationally very expensive. We propose augmenting brute-force LES via machine-learning data-driven modeling (ML-LES) to dramatically reduce the computational time required to obtain converged turbulence statistics when the brute-force approach is employed. Namely, we employ a convolutional neural network (CNN) autoencoder trained and validated with LES data sets to develop a highly efficient ML-LES approach for computing turbulence statistics from just a few snapshots of instantaneous LES flow fields. Our results demonstrate the accuracy and efficiency of ML-LES in predicting the mean velocity, velocity fluctuations, and turbulence kinetic energy in highly stretched computational grid systems required to carry out simulations in real-life oceanic environments.

Automated summary

Wind-wave interactions play a significant role in offshore wind farm energy harvesting. High-fidelity large-eddy simulation (LES) is effective in studying turbulent oceanic environments, but it is computationally expensive. To address this issue, we propose a machine-learning data-driven modeling (ML-LES) method to enhance brute-force LES and reduce computational time. By training a convolutional neural network (CNN) autoencoder with LES datasets, our ML-LES approach accurately predicts turbulence statistics using only a few snapshots of instantaneous LES flow fields in stretched computational grid systems.