New dynamic two-layer model for predicting depth-averaged velocity in open channel flows with rigid submerged canopies of different densities

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{YANG:2020:AWR,

• author = "Fan Yang and Wen-Xin Huai and Yu-Hong Zeng",
• title = "New dynamic two-layer model for predicting depth-averaged velocity in open channel flows with rigid submerged canopies of different densities",
• journal = "Advances in Water Resources",
• volume = "138",
• pages = "103553",
• year = "2020",
• ISSN = "0309-1708",
• DOI = "doi:10.1016/j.advwatres.2020.103553",
• URL = "http://www.sciencedirect.com/science/article/pii/S0309170819303148",
• keywords = "genetic algorithms, genetic programming, Depth-averaged velocity, Open channel flow, Rigid submerged vegetation, Dynamic two-layer model",
• abstract = "The depth-averaged velocity is the commonly used engineering quantity in natural rivers, and it needs to be predicted in advance, especially in flood seasons. A model that can provide a unified physical foundation for open channel flows with different canopy densities remains lacking despite ongoing researches. Here, we use the concept of the auxiliary bed to describe the influence of momentum exchange on rigid canopy elements with varying density and submergence. The auxiliary bed divides the vegetated flow into a basal layer and a suspension layer to predict average velocity in each layer separately. In the basal layer, the velocity profile is assumed to be uniform. In the suspension layer, a parameter called {"}penetration depth{"} is applied to present the variations in velocity distribution. We also apply a data-driven method, called genetic programming (GP), to derive Chezy-like predictors for average velocity in the suspension layer. Compared to the hydraulic resistance equation for rough-wall flows, the new formulae calculated by the weighted combination method show sound physical meanings. In addition, comparison with other models shows that the new dynamic two-layer model achieves high accuracy in flow rate estimation, especially for vegetated flow with sparse canopies",

}

Genetic Programming entries for Fan Yang Wen-Xin Huai Yu-Hong Zeng

Citations