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 relat...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.展开更多
Aquatic vegetation is a vital component of natural river ecosystems,playing a crucial role in maintaining ecological balance,providing habitat and improving water quality.However,the presence of vegetation results in ...Aquatic vegetation is a vital component of natural river ecosystems,playing a crucial role in maintaining ecological balance,providing habitat and improving water quality.However,the presence of vegetation results in increased resistance in vegetated channels compared with non-vegetated channels,rendering traditional sediment movement predictions inadequate for the latter.Consequently,the concept of a vegetation influence factor,denoted by CDah,has been proposed by previous researchers to represent the effect of vegetation on sediment movement in watercourses.In this study,we focus on exploring the vegetation resistance coefficient(CD)among the vegetation influence factors,evaluating two different calculation methods for vegetation resistance coefficient,and presenting two expressions through genetic algorithm analysis to predict the incipient flow velocity of sediment in vegetated watercourses.The predicted values from the new formulae show excellent agreement with measured data,highlighting the high accuracy of the proposed methods in predicting the incipient flow velocity of sediment.Our results provide a solid theoretical basis for understanding the influence of aquatic vegetation on sediment particle movement.展开更多
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)+1 种基金the China Postdoctoral Science Foundation(Grant No.2023M730932)the Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2023ZB608).
文摘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.
基金Project supported by the Natural Science Foundation of Beijing (Grant No.8232052)the National Natural Science Foundation of China (Grant No.51809286).
文摘Aquatic vegetation is a vital component of natural river ecosystems,playing a crucial role in maintaining ecological balance,providing habitat and improving water quality.However,the presence of vegetation results in increased resistance in vegetated channels compared with non-vegetated channels,rendering traditional sediment movement predictions inadequate for the latter.Consequently,the concept of a vegetation influence factor,denoted by CDah,has been proposed by previous researchers to represent the effect of vegetation on sediment movement in watercourses.In this study,we focus on exploring the vegetation resistance coefficient(CD)among the vegetation influence factors,evaluating two different calculation methods for vegetation resistance coefficient,and presenting two expressions through genetic algorithm analysis to predict the incipient flow velocity of sediment in vegetated watercourses.The predicted values from the new formulae show excellent agreement with measured data,highlighting the high accuracy of the proposed methods in predicting the incipient flow velocity of sediment.Our results provide a solid theoretical basis for understanding the influence of aquatic vegetation on sediment particle movement.
