Benchmark of computational hydraulics models for open-channel flow with lateral cavities

Computational models in hydro-environmental engineering are diverse in their background formulation and span from two-dimensional depth-averaged shallow water models, to complex fully three-dimensional turbulence models resolving large-eddy simulation with surface capturing techniques, and to Lagrangian particle-based methods. This paper presents a first-of-its-kind comparison of six different computational hydraulics fluid dynamics models, namely Iber+, HO-SWM, GBVC, OpenFOAM (RANS), Hydro3D (LES) and DualSPHysics (SPH), in the prediction of mean velocities and free-surface dynamics in two benchmarks involving open-channel flows with symmetric lateral cavities. Results show that shallow-water models capture relatively well the main large-scale coherent structures of the in-cavity flow, with wider shear layers compared to three-dimensional models, and higher velocities in the main channel. Three-dimensional RANS, LES and SPH yield improved predictions of mean velocities compared with experimental data. Computational cost has been quantified for all models with a logarithmic growth when increasing model complexity. The transverse standing wave is captured by most models, with the shallow-water ones matching the theoretical value, while the three-dimensional models overestimate it slightly.

Dr Ouro gratefully acknowledges the help of the Supercomputing Wales project, which is partially sponsored by the European Regional Development Fund (ERDF) via the Welsh Government. Some of the presented material has been supported by the Dame Kathleen Ollerenshaw Fellowship that Dr Ouro holds at the University of Manchester. Dr Navas-Montilla acknowledges the support of the Regional Government of Aragón (Computational Fluid Mechanics Group, T32_20R). Dr Juez was funded by the ERC-StG 2021 programme of the European union under grant agreement number 101039181-SEDAHEAD and by the Regional Government of Aragón (Geoenvironmental Processes and Global Change group, E02_23R). Dr García-Feal was supported by the postdoctoral fellowship ‘Juan de la Cierva’ (ref. JDC2022-048667-I), funded by MCIN/AEI/10.13039/50 1100011033 and the European Union ‘NextGenerationEU’/PRTR; by the Spanish Ministerio de Universidades under application 33.50.460A.752, by the European Union NextGenerationEU/PRTR through a contract Margarita Salas from the University of Vigo. Dr Croquer acknowledges Prof. Sébastien Poncet as well as the support of the SciNet HPC Consortium. SciNet is funded by Innovation, Science and Economic Development Canada, the Digital Research Alliance of Canada, the Ontario Research Fund: Research Excellence, and the University of Toronto.

Peer reviewed