A reduced order model for space–time wave statistics using probabilistic decomposition–synthesis method. (1st September 2022)

• Record Type: Journal Article
• Authors: Tang, Tianning; Adcock, Thomas A.A.
• Is Part Of: Ocean engineering. Volume 259(2022)
• Publication Date: 2022-09-01
• Subjects: Surface wave -- Rogue wave; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2022.111860
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: We present a reduced order model for predicting space–time extreme wave statistics for directionally spread water waves. This reduced order model relies on the probabilistic decomposition–synthesis framework. In this framework, the probabilistic distribution is decomposed into a 'background' region, where there are negligible nonlinear effects and a 'instability' region where the nonlinear physics leads to heavy-tailed statistical behaviour. In our reduced order model, we approximate the statistics in the background region with linear simulations to reduce the computational cost. The statistics in the instability region are estimated with nonlinear simulations of focused wave groups with carefully chosen initial conditions to capture the nonlinear effects during the formation of the extreme waves. The probabilistic statistics from these two components are synthesised with analytical expressions to model the overall behaviour of weakly nonlinear waves. This reduced order wave statistical model can provide accurate predictions of nonlinear space–time wave statistics at a fraction of computational cost of direct nonlinear Monte-Carlo simulations. Highlights: Presents a reduced order model for space–time ocean wave statistics. Demonstrates the application of low dimensional parametrisation of random wave field. Suggests statistical interconnections between extreme events and system responses.