Classification of Hydraulic Jump in Rough Beds

Abstract

This paper presents a classification using a decision tree algorithm of hydraulic jump over rough beds based on the approach Froude number, Fr1. Specifically, 581 datasets, from literature, were analyzed. Of these, 280 datasets were for natural rough beds and 301 were for artificial rough beds. The said dataset was divided into four classes based on the energy losses. To compare the performance of the decision tree classifier (J48), a multi-layer neural network (NN) was used. The results suggest an improved performance in terms of classification accuracy by the J48 algorithm in comparison to the NN classifier. Furthermore, the classifier model had only four leaves and achieved an accuracy of 91.56%. Furthermore, classification results showed that the first class (A) of hydraulic jump over the rough beds is approximately similar to that for the smooth bed. Moreover, in the next three classes (B, C, and D), upper values of Fr1 decreased with respect to the smooth bed classes. Lastly, in class D, the upper value of Fr1 reduced to 7.45, which indicates that the shear stress (i.e., the energy loss) grows sharply with increasing Fr1. Put simply, bed roughness effectively increases the energy dissipation with an increase in the Fr1.

Predicting submerged hydraulic jump characteristics using machine learning methods

“…Another important SHJ characteristic is the jump length, which plays an important role in the economic design of stilling basins as well as the length of the protection downstream of the hydraulic structures. In the study, the Long et al (Mahtabi et al 2020)'s criterion was used to measure the jump length, so that the distance between the gate to the end of the developed zone (end of return flows) is considered as the submerged jump length. Figure 9 shows the changes in the relative jump length (L j /y 2 ) versus the submergence ratio (S).…”

“…Until now, many studies have been conducted on characteristics of classic/free hydraulic jump (sequent depth ratio, relative energy loss, and jump length) (e.g. Kindsvater 1944;Bradley & Peterka 1957;Rao & Rajaratnam 1963;Rajaratnam 1967;Rajaratnam 1968;Hager et al 1990;Mahtabi et al 2020); however, the studies on submerged hydraulic jump characteristics are limited. Long et al (1990) studied a submerged hydraulic jump downstream of a sluice gate in a rectangular channel with a flat bed.…”

Numerical Investigation of Critical Hydraulic Parameters Using FLOW-3D: A Case Study of Taunsa Barrage, Pakistan

“…Ranking Extracted from Table 2 Ranking Extracted from Table 3 Ranking Extracted from Table 4 Ranking Extracted from Table 5 Overall Occurrence (1) ( 8 compares the free surface profiles at five different TWLs. The free surface data were taken from the downstream of the gate to the end of the stilling basin.…”

“…In the hydraulic jumps, the flow changes suddenly from supercritical to subcritical conditions and vice versa. During this chaotic phenomenon, a rapid rise in the free surface occurs that dissipates a large amount of energy due to the turbulent mixing [1][2][3][4]. A hydraulic jump (HJ hereafter) occurs in gravity-driven flows when Fr crosses unity and is defined as the ratio of inertial to gravitational forces.…”

Sensitivity Analysis of Adjustable River Surf Waves in the Absence of Channel Drop

“…Hydraulic jumps have been studied extensively ever since Leonardo da Vinci in the 16th century, but the main purpose of previous studies has been to utilize hydraulic jumps as a means of energy dissipation in hydraulic structures (e.g., [18,19]). Mahtabi et al [20] provided a decision tree algorithm for classification of hydraulic jumps and Macian-Perez et al [21] experimentally characterized the profile and velocity distribution of hydraulics jumps. Hassanpour et al [22] investigated the flow behavior and pressure fluctuations of the hydraulic jump at a channel expansion.…”