Predicting submerged vegetation drag with a machine learning-based method  

作　　者：Meng-yang Liu Hong-wu Tang Sai-yu Yuan Jing Yan 

机构地区：[1]College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing,210098,China [2]Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources,Hohai University,Nanjing,210098,China

出　　处：《Journal of Hydrodynamics》2024年第3期534-545,共12页水动力学研究与进展B辑（英文版）

基　　金：supported by the National Key Research and Development Program of China(Grant No.2022YFC3202601);the National Natural Science Foundation of China(Grant No.52309088);the China Postdoctoral Science Foundation(Grant No.2023M730932);the Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2023ZB608).

摘　　要：Accurate estimation of the drag forces generated by vegetation stems is crucial for the comprehensive assessment of the impact of aquatic vegetation on hydrodynamic processes in aquatic environments.The coupling relationship between vegetation layer flow velocity and vegetation drag makes precise prediction of submerged vegetation drag forces particularly challenging.The present study utilized published data on submerged vegetation drag force measurements and employed a genetic programming(GP)algorithm,a machine learning technique,to establish the connection between submerged vegetation drag forces and flow and vegetation parameters.When using the bulk velocity,U,as the reference velocity scale to define the drag coefficient,C_(d),and stem Reynolds number,the GP runs revealed that the drag coefficient of submerged vegetation is related to submergence ratio(H^(*)),aspect ratio(d^(*)),blockage ratio(ψ^(*)),and vegetation density(λ).The relation between vegetation stem drag forces and flow velocity is implicitly embedded in the definition of C_(d).Comparisons with experimental drag force measurements indicate that using the bulk velocity as the reference velocity,as opposed to using the vegetation layer average velocity,U_(v),eliminates the need for complex iterative processes to estimate U_(v)and avoids introducing additional errors associated with U_(v)estimation.This approach significantly enhances the model’s predictive capabilities and results in a simpler and more user-friendly formula expression.

关 键 词：Drag coefficient genetic programming algorithm submerged vegetation flow resistance cylinder array 

分 类 号：TV131.2[水利工程—水力学及河流动力学]