Energy Loss in Skimming Flow over Cascade Spillways: Comparison of Artificial Intelligence-Based and Regression Methods

Nouri, Meysam; Sihag, Parveen; Salmasi, Farzin; Kisi, Özgür

doi:10.3390/app10196903

Cited by 15 publications

(6 citation statements)

References 47 publications

(55 reference statements)

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“…Mean Absolute Error (MAE): MAE of prediction is defined as the absolute difference between the predicted and experimental (observed) values of relative coefficient of discharge, per residue [20]. Where summation is carried out for all residues and N is the total number of observations.…”

Section: Parameters For Performance Appraisalmentioning

confidence: 99%

“…Normalized Root Mean Square Error (NRMSE): It relates the RMSE to the observed range of the variable. Thus, the NRMSE can be interpreted as a fraction of the overall range that is typically resolved by the model [20].…”

Section: Parameters For Performance Appraisalmentioning

confidence: 99%

“…Recently, soft computing techniques and/or artificial intelligence (AI)-based models have been used for the estimation C d of hydraulic structures [16][17][18]. Due to some negative issues in common methods, including a large quantity of influencing parameters and their interactions, a large number of assumptions, complexity of the solutions, high uncertainty, etc., these models can be utilized as a direct solution to the problem [19,20]. Salmasi and Abraham [21] used genetic programming for identifying the discharge coefficient for inclined slide gates.…”

Section: Introductionmentioning

confidence: 99%

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Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches

et al. 2022

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Radial gates are widely used for agricultural water management, flood controlling, etc. The existence of methods for the calculation of the discharge coefficient (Cd) of such gates are complex and they are based on some assumptions. The development of new usable and simple models is needed for the prediction of Cd. This study investigates the viability of a metaheuristic regression method, the Gaussian Process (GP), for the determination of the discharge coefficient of radial gates. For this purpose, a total of 2536 experimental data were compiled that cover a wide range of all the effective parameters. The results of GP were compared with the Group Method of Data Handling (GMDH), Multivariate Adaptive Regression Splines (MARS), and linear and nonlinear regression models for predicting Cd of radial gates in both free-flow and submerged-flow conditions. The results revealed that the radial basis function-based GP model performed the best in free-flow condition with a Correlation Coefficient (CC) of 0.9413 and Root Mean Square Error (RMSE) of 0.0190 while the best accuracy was obtained from the Pearson VII kernel function-based GP model for the submerged flow condition with a CC of 0.9961 and RMSE of 0.0132.

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Section: Parameters For Performance Appraisalmentioning

confidence: 99%

Section: Parameters For Performance Appraisalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches

et al. 2022

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“…In addition, energy loss was greater in labyrinth spillways with two cycles than those with one cycle. Nouri et al [18] developed some data-based models to explain the relationships between hydraulic parameters. Multiple linear and nonlinear regression-based equations were developed based on dimensional analysis theory to compute energy dissipation over cascade spillways.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional analysis of the performance of circular stepped spillways in the skimming flow regime

Ghaderi,

Ebrahimnezhadian,

Mollazadeh

2024

J Braz. Soc. Mech. Sci. Eng.

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By creating an artificial rough bed, stepped spillways increase energy dissipation during the spillway and reduce the risk of cavitation and the dimensions of the stilling basin. Combining stepped spillways with labyrinth spillways in order to improve their performance can be an attractive idea. Therefore, in this research, the hydraulic characteristics of the flow, the amount of energy dissipation and the residual energy of the flow downstream of the stepped spillway with a circular labyrinth configuration, with different numbers of cycles, were compared with the conventional stepped spillway. Validation of the numerical model was done using the results of the physical model. Four three-dimensional configurations of stepped spillway including: conventional stepped, two-, three-, and four-cycle circular labyrinth were modeled using OpenFOAM open source for the skimming flow regime. In order to simulate the flow, InterFOAM solver and RNG K  − turbulence model were used. The results of the numerical model showed that the combination of the labyrinth spillway with the stepped spillway causes an increase of 7.4, 28 and 18.3 percent of energy dissipation in circular forms of two, three and four cycles compared to its conventional type for a constant discharge and yc/h=1.68. Therefore, among the circular labyrinth configurations, the three-cycle form (3CSS) has the highest dissipation rate. In addition, in the three-cycle circular labyrinth configuration, compared to other conventional and labyrinth configurations, it has resulted in less negative pressure in the upper half of the vertical face of the steps.

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“…Irrigation area water measurement technology is a core technology in irrigation area water monitoring and information construction [1]. Accurate flow measurement can improve the water supply capacity of all levels of channels and reduce water loss, which is the foundation of scientific water allocation and the modern management of irrigation areas [2,3]. In recent years, Ningxia has vigorously carried out modernization transformation of irrigation areas and installed over 4000 sets of measurement and control gates, involving more than 20 domestic and foreign manufacturers.…”

Section: Introductionmentioning

confidence: 99%

Flow Prediction of a Measurement and Control Gate Based on an Optimized Back Propagation Neural Network

Hou,

Niu,

Zhu

et al. 2023

Applied Sciences

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The measurement and control gate, as a new type of measurement and control equipment, has been widely used for water quantity control in irrigation areas. However, there is a lack of methods for calibrating the flow inside the measurement box at present. This paper establishes a flow prediction model based on a back propagation (BP) neural network and its optimization algorithm by using 450 sets of sample data obtained from the indoor gate overflow test and verified the effectiveness and accuracy of the prediction model by using another 205 sets of sample data. The results show that the gate flow prediction model based on a BP neural network and its optimization algorithm has self-adaptability to different flow patterns, and its prediction accuracy is significantly higher than that of the traditional water measurement method. Compared to the unoptimized BP model, the BP model optimized by the genetic algorithm (GA) or particle swarm optimization (PSO) has higher prediction accuracy and better error distribution. Both GA and PSO algorithms can be used to optimize the initial weights and thresholds of the BP flow prediction model. However, by comprehensively analyzing the prediction accuracy, error distribution, and running time, the PSO algorithm has better optimization performance compared to the GA algorithm. The prediction model can provide a reference for flow rate calibration and the anomaly rejection of measurement and control gates in the irrigation area.

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Energy Loss in Skimming Flow over Cascade Spillways: Comparison of Artificial Intelligence-Based and Regression Methods

Cited by 15 publications

References 47 publications

Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches

Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches

Three-dimensional analysis of the performance of circular stepped spillways in the skimming flow regime

Flow Prediction of a Measurement and Control Gate Based on an Optimized Back Propagation Neural Network

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