JOURNAL ARTICLE
Hydrodynamic shock in Rivers: Physical and numerical modeling of flow structures in tsunami-like bores.
Published In: Physics of Fluids, 2023, v. 35, n. 10. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Simon, Bruno; Lubin, Pierre; Chanson, Hubert 3 of 3
Abstract
This article focuses on the detailed numerical investigation of turbulent hydrodynamics induced by three-dimensional (3D) undular bores propagating upstream against steady flows in rectangular channels, with implications for understanding tsunami-like bores in rivers. Using large eddy simulation (LES) of the Navier–Stokes equations and synthetic eddy method (SEM) to reproduce turbulent inflow conditions, the study compares two- and three-dimensional simulations under various bore generation methods (dam break, fully or partially closed gates) and initial flow conditions. Key findings include the identification of three distinct flow scenarios beneath bores—complete flow reversal, oscillating flow, and no flow reversal—and the critical role of Reynolds number (around 5 × 10^4) in triggering near-bed turbulence and eddy shedding, which significantly alter flow structures despite similar free-surface profiles. The results demonstrate that 3D simulations with turbulent inflow better capture complex flow features such as cross-waves and coherent turbulent structures, highlighting the necessity of incorporating realistic turbulence for accurate modeling of positive surges relevant to tsunami propagation in estuarine and riverine environments.
Additional Information
- Source:Physics of Fluids. 2023/10, Vol. 35, Issue 10, p1
- Document Type:Article
- Subject Area:Earth and Atmospheric Sciences
- Publication Date:2023
- ISSN:1070-6631
- DOI:10.1063/5.0161096
- Accession Number:173362359
- Copyright Statement:Copyright of Physics of Fluids is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.