The laboratory study of energy dissipation in inclined drops equipped with a screen

Daneshfaraz, Rasoul; Asl, Mahdi Majedi; Bazyar, Ali; Abraham, John; Norouzi, Reza

doi:10.1080/23249676.2020.1799877

Cited by 16 publications

(12 citation statements)

References 8 publications

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“…Diversified SVM model results have been utilized to pridict vertical drop hydraulic parameters in the presence of dual horizontal screens (Daneshfaraz et al 2020a(Daneshfaraz et al , 2020b(Daneshfaraz et al , 2020c. SVM models have also been used to investigate labyrinth and arched labyrinth weir discharge coefficients (Roushangar et al 2017) and to study trapezoidal labyrinth weir discharge coefficients (Norouzi et al 2019a(Norouzi et al , 2019b.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…An experimental study was employed by (Daneshfaraz et al 2020a(Daneshfaraz et al , 2020b(Daneshfaraz et al , 2020c to investigate the impact of dual horizontal screens on hydraulic behavior. The results revealed that the mechanism of flow can be transformed from supercritical to subcritical downstream of a drop through the utilization of dual horizontal screens.…”

Section: Introductionmentioning

confidence: 99%

“…The results revealed that the mechanism of flow can be transformed from supercritical to subcritical downstream of a drop through the utilization of dual horizontal screens. Using vertical screens with two porosity ratios located downstream of inclined drops was investigated by (Daneshfaraz et al 2020a(Daneshfaraz et al , 2020b(Daneshfaraz et al , 2020c. Results showed that compared to a plane inclined drop, the screen also caused an increase of at least 407% and up to 903% in total relative energy dissipation.…”

Section: Introductionmentioning

confidence: 99%

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Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

et al. 2021

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This study was designed to evaluate the ability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, LR, and MLR to predict the relative energy dissipation(ΔE/Eu) for vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation. In the experiments, the discharge rate, drop height, and porosity of the screens were varied. Parameters yc/h, yd/yc, and p were input variables, and ΔE/Eu was the output variable. The efficiencies of the models were compared using the following metrics: correlation coefficient (CC), mean absolute error (MAE), root-mean-square error (RMSE), Normalized root mean square error (NRMSE) and Nash–Sutcliffe model efficiency (NSE). Results indicate that the performance of the ANFIS_gbellmf based model with a CC value of 0.9953, RMSE value of 0.0069, MAE value of 0.0042, NRMSE value as 0.0092 and NSE value as 0.9895 was superior to other applied models. Also, a linear regression yielded CC = 0.9933, RMSE = 0.0083, and MAE = 0.0067. This linear model outperformed multiple linear regression models. Results from a sensitivity study suggest that yc/h is the most effective parameter for predicting ΔE/Eu.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

et al. 2021

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“…The least-squares method is used to estimate the regression coefficients [12]. LR has been used in prior applications, for example for trapezoidal labyrinth side weir discharge coefficients [5] and evaporation modeling [6]. A schematic diagram showing the algorithm is provided in Fig.…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

“…ANFIS has an excellent ability to calculate the energy dissipation associated with inclined drops that are equipped with downstream vertical screens. Sadeghfam et al [5] used artificial intelligence to combine multiple models (AIMMs) for calculating supercritical jet flow upstream of screens. The results showed that the AIMM model has a good capability in modelling scouring dimensions.…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Predictions of Relative Energy Dissipation for Vertical Drops Equipped with a Horizontal Screen

Norouzi¹,

Daneshfaraz²,

Abraham³

et al. 2020

Preprint

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Drops are the most important and most common energy dissipator in irrigation networks and erodible canals and consequently, their performance must be well understood. This study was designed to evaluate the capability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, MLR, and LR to predict the relative energy dissipation (∆E/E0) in vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation with variable discharge, varying drop height, and porosity of the horizontal screens. Parameters yc/h, yd/yc, and p are considered as input variables and ∆E/E0 is the output variable. The efficiency of models was compared using Taylor's diagram, Box Plot of the applied error distribution, correlation coefficient (CC), mean absolute error (MAE) and root-mean-square error (RMSE). Results indicate that the performance of the ANFIS_gbellmf based model with CC value of 0.9953, RMSE value of 0.0069 and MAE value of 0.0042 was superior to other applied models. Also, the linear regression model with CC=0.9933, RMSE=0.0083, and MAE= 0.0067performs better than the multiple linear regression model in this study. Results of a sensitivity study suggest that yc/h is the most effective parameter for predicting ∆E/E0.

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Investigating the Effect of Horizontal Screen on Hydraulic Parameters of Vertical Drop

Daneshfaraz

Hasannia

Norouzi

et al. 2021

Iran J Sci Technol Trans Civ Eng

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The laboratory study of energy dissipation in inclined drops equipped with a screen

Cited by 16 publications

References 8 publications

Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

Soft Computing Predictions of Relative Energy Dissipation for Vertical Drops Equipped with a Horizontal Screen

Investigating the Effect of Horizontal Screen on Hydraulic Parameters of Vertical Drop

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