Estimation of Flow Velocity Using Entropy Theory and Verification by Experimental Flume and Natural Rivers Data

Estimation of velocity distribution is one of the noteworthy challenges of water dynamics in seas and rivers. In this study, employing entropy theory and utilizing the proposed Cumulative Distribution Function (CDF), firstly, the proposed framework is proved and finally verified by experimental flume and natural rivers data. Entropy theory integrated with CDF can investigate the interrelations of physical phenomena which have a unique maximum, properly. This theory uses a global principle to conserve entropy, which is the maximization of entropy in any condition to stabilize thermodynamics systems. Furthermore, assuming velocity as a random variable leads to propose a function to estimate velocity distribution as 2D and 3D (). General Index Entropy (GIE) is used in this study and combined with proposed CDF. Comparing the proposed framework with previous methods shows that the proposed method has less sensitivity and more flexibility in estimation of parameters and significant accuracy to estimate velocity distribution. This method is applicable for maximum flow occurrence on and below the water surface.