Estimating the Bed-Load Layer Thickness in Open Channels by Tsallis Entropy

Author:

Zhu ZhongfanORCID,Yu Jingshan

Abstract

In the research field of river dynamics, the thickness of bed-load is an important parameter in determining sediment discharge in open channels. Some studies have estimated the bed-load thickness from theoretical and/or experimental perspectives. This study attempts to propose the mathematical formula for the bed-load thickness by using the Tsallis entropy theory. Assuming the bed-load thickness is a random variable and using the method for the maximization of the entropy function, the present study derives an explicit expression for the thickness of the bed-load layer as a function with non-dimensional shear stress, by adopting a hypothesis regarding the cumulative distribution function of the bed-load thickness. This expression is verified against six experimental datasets and are also compared with existing deterministic models and the Shannon entropy-based expression. It has been found that there is good agreement between the derived expression and the experimental data, and the derived expression has a better fitting accuracy than some existing deterministic models. It has been also found that the derived Tsallis entropy-based expression has a comparable prediction ability for experimental data to the Shannon entropy-based expression. Finally, the impacts of the mass density of the particle and particle diameter on the bed-load thickness in open channels are also discussed based on this derived expression.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

General Physics and Astronomy

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3. Field quantification and spatial variation of bed load transport in mountain stream: a case study of Gujarat (India);International Journal of Hydrology Science and Technology;2023

4. IMPROVEMENT OF CALCULATION ACCURACY FOR PREDICTING OF SEDIMENT DEPOSITION INDUCED BY TSUNAMI RUN-UP USING BEDLOAD LAYER MODEL;Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering);2022

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