Computational and experimental study of multi-grain-size sediment transport and sorting process

Abstract

Successful prediction of the interaction between turbulent flows and river ecosystems is essential for flood management, development of mitigation strategies, and infrastructure protection. Natural riverbeds consist of bed materials of multiple grain sizes that interact with turbulence differently, leading to spatial classification of particle sizes. Such particle sorting processes can only be captured by models capable of handling multiple grain sizes. We present a numerical framework that couples large-eddy simulation with a multi-grain-size sediment transport module to track multiple grain size classes, their local fractions within the bed-load layer, and bed evolution. A laboratory experiment was conducted in a flume with multi-size bed material to examine sediment particle redistribution and sorting. Measured sediment grain size distributions and bed elevations were used to validate the numerical framework. The results of the multi-grain-size sediment transport model for bed deformation were then compared against those of a uniform sediment transport model, demonstrating that the former reduces the prediction errors relative to the experimental measurements from 37.98 to 3.83 percent for maximum scour depth and from 48.35 to 11.54 percent for maximum deposition height. The validated numerical framework was also applied to large-scale meandering rivers with multi-grain bed material, simulating their bed deformation and sediment sorting. The simulation results revealed the formation of particle sorting patterns in meandering rivers, with coarser and finer sediment particles accumulating near the erosional depression (scour) and depositional (point bar) regions, respectively. These findings support the coupled framework's ability to predict sediment particle sorting and highlight the importance of representing bed material heterogeneity in numerical modeling of meandering rivers.