首页|Predicting submerged vegetation drag with a machine learning-based method

Predicting submerged vegetation drag with a machine learning-based method

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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,Cd,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 Cd.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,Uv,eliminates the need for complex iterative processes to estimate Uv and avoids introducing additional errors associated with Uv estimation.This approach significantly enhances the model's predictive capabilities and results in a simpler and more user-friendly formula expression.

Drag coefficientgenetic programming algorithmsubmerged vegetationflow resistancecylinder array

Meng-yang Liu、Hong-wu Tang、Sai-yu Yuan、Jing Yan

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College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China

Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources,Hohai University Nanjing 210098,China

National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaPostdoctoral Innovation Talents Support ProgramChina Postdoctoral Science FoundationJiangsu Funding Program for Excellent Postdoctoral Talent

2022YFC320260152309088BX202301062023M7309322023ZB608

2024

10.1007/s42241-024-0034-6

水动力学研究与进展B辑
中国船舶科学研究中心

水动力学研究与进展B辑

CSTPCDEI
影响因子：0.596
ISSN：1001-6058
年,卷(期)：2024.36(3)