Real-time phase-resolved ocean wave prediction in directional wave fields: Enhanced algorithm and experimental validation. (15th May 2023)

• Record Type: Journal Article
• Authors: Kim, I.-C.; Ducrozet, G.; Bonnefoy, F.; Leroy, V.; Perignon, Y.
• Is Part Of: Ocean engineering. Volume 276(2023)
• Publication Date: 2023-05-15
• Subjects: Ocean waves -- Phase-resolved model -- Real-time prediction -- Data assimilation -- Directional wave -- Wave tank experiments; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2023.114212
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: This paper details a comprehensive study of deterministic real-time wave forecasting in directional seas. By using wave models on the basis of a Lagrangian description, a good balance was achieved between computational efficiency and model accuracy. Nevertheless, due to the highly non-uniform spatial distribution of data and the relatively small size of data in time inherent to remote optical measurements, the initial conditions are determined through an optimization process, which is computationally demanding, especially in multidirectional sea states. Accordingly, in order to offer a real-time system of wave prediction in the case of multidirectional waves, we propose a simplified and succinct assimilation method for the process of wave reconstruction. We also develop a three-dimensional spatio-temporal prediction zone where the future evolution of wave fields can be estimated based on wave measurements. Lastly, we outline a tank-scale experimental campaign conducted to mimic the measurements of a LIDAR in a real configuration. A comparison of model performances with the experimental observations shows that in a multidirectional approach, it is necessary to consider wave components in direction as well as in frequency to achieve nearly the same accuracy as for unidirectional seas. Highlights: Simplified data assimilation reduces computational costs while maintaining model accuracy. The algorithm is validated against experiments conducted to mimic the measurements of a LIDAR. Considering directional aspects improves the model accuracy for the directional cases.