Numerical modeling of cavitation on spillway’s flip bucket

Parsaie, Abbas; Dehdar-Behbahani, Sadegh; Haghiabi, Amir Hamzeh

doi:10.1007/s11709-016-0337-y

Cited by 34 publications

(12 citation statements)

References 15 publications

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“…Moreover, CFD simulations have been employed to solve hydraulic engineering problems successfully. Hence, developing a simulation which is capable of reflecting the results of actual experiments is of great practical value [13][14][15][16][17][18]. The models presented in this paper could be potentially applied in engineering applications, such as leak control in the water supply industry, filledtower optimization in the chemical industry, and drip irrigation design in agricultural irrigation.…”

Section: Introductionmentioning

confidence: 99%

Experimental and numerical modeling of sidewall orifices

Cheng

Shen

2020

SN Appl. Sci.

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The discharge coefficient of an orifice is important for the outflow through the orifice. While it cannot be quantified theoretically as the outflow through an orifice depends on a number of parameters such as the pipe pressure, the liquid velocity and the shape and the area of the orifice. In this study, experiments and computational fluid dynamics (CFD) simulations were performed to find out the factors influencing discharge coefficient and the corresponding mechanism. The CFD simulation is based on Navier-Stokes equations combined with RNG k − ε turbulence model. The results show that a negative exponential function could fit the relationship between orifice discharge coefficient, pipe pressure, and orifice area more accurately. The relationship between the discharge coefficient of the orifice and the velocity was linear. In general, the simulation results fit well with the experimental results, which indicates that CFD simulation could be used to study pipeline leakage.Keywords Outflow · Orifice · Velocity · Pressure · Numerical model · Computational fluid dynamics (CFD) Abbreviations Q totalThe inlet flow rate, m 3 /s Q outThe outflow rate, m 3 /s Q endThe outlet flow rate, m 3 /s A crossThe area of the cross-section, m 2 k aThe ration of the orifice area which represents the relative size of the orifice area H 1The upstream water head, m P a Atmospheric pressure, P a ρThe water density, kg/m 3 gThe gravity constant, 9.8 m/s 2 α 0The kinetic energy correction factor of the inlet flow ν comeThe upstream velocity, m/s P outThe pressure of the shrinkage section out of the orifice, P a α outThe kinetic energy correction factor of the orifice outflow h w

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Section: Introductionmentioning

confidence: 99%

Experimental and numerical modeling of sidewall orifices

Cheng

Shen

2020

SN Appl. Sci.

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“…In this dissipation system, the flow digs a scour hole in the river bed, which is shape dependent on the rock bed resistance as well as the effort caused by the jet. Numerical modeling of the hydraulic phenomenon through computational fluid dynamic (CFD) approaches is one of the main parts of high-cost hydraulic structure studies [7]. To model hydraulic characteristics, several methods such as physical and numerical methods can be used.…”

Section: Introductionmentioning

confidence: 99%

Research on the Optimization Design of Abnormal Flip Buckets

Wang

2020

Mathematical Problems in Engineering

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This study constructs an optimization model to address ski-jump energy dissipation problems in different reservoir environments. The multiobjective genetic algorithm is herein applied as the calculation method. In the process of changing the ski-jump flow model, the runoff of ski-jump flow is changed (especially the abnormal flip bucket). Water flow might go from having a single falling point to multiple falling points. Optimization is only performed after the stabilization of the fall of the water because the fall of the water from the starting point to the ground is not stable over a short period. Only a stable flow can reduce the computation time for optimization. The optimal overflow width and height of the flip bucket were calculated through optimal computation, which can minimize the scouring force of the water flow as it falls to the ground. The results obtained provide theoretical references for practical engineering and reduce the potential safety hazards. The energy in flip buckets following the optimization of the water flow can be fully dissipated, creating an ecosystem where water can flow unobstructed. Guiding a water flow to water-deficient areas is of great significance in ensuring the long-term protection of environments and ecosystems.

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“…Although the energy lost in the nape regime is more than the skimming flow, but due economic reasons, the stepped spillways are designed under skimming flow condition. By advancing the computer technology, the investigators have tried to study the properties of flow over the spillway using the numerical methods (Parsaie et al 2016a(Parsaie et al , 2018b. Numerical modeling is divided into two main groups as computational fluid dynamic (CFD) and soft computing.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of energy dissipation on stepped spillway using evolutionary computing

Parsaie

Haghiabi

2019

Appl Water Sci

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In this study, using the M5 algorithm and multilayer perceptron neural network (MLPNN), the capability of stepped spillways regarding energy dissipation (ED) was approximated. For this purpose, relevant data was collected from valid sources. The study of the developed model based on the M5 algorithm showed that the Drop and Froude numbers play important roles in modeling and approximating the ED. The error indices of M5 algorithm in training were R 2 = 0.99 and RMSE = 2.48 and in testing were R 2 = 0.99 and RMSE = 2.23. The study of developed MLPNN revealed that this model has one hidden layer which includes five neurons. Among the tested transfer functions, the great efficiency was related to the Tansing function. The error indices of MLPNN in training were R 2 = 0.97 and RMSE = 3.73 and in testing stages were R 2 = 0.97 and RMSE = 3.98. Evaluation of the results of both applied methods shows that the accuracy of the MLPNN is a bit less than the M5 algorithm. Keywords Energy dissipation • Soft computing • Drop number • Spillways • M5 algorithm Abbreviations ANFIS Adaptive neuro-fuzzy inference system CFD Computational fluid dynamic DN Drop number ED Energy dissipation Fr Froude number g Gravity acceleration GEP Genetic expression programming GMDH Group method of data handling H Total head of flow h s Height of steps L c Length of crest L s Length of steps MARS Multivariate adaptive regression splines Max Maximum Min Minimum MLPNN Multilayer perceptron neural network N Number of steps q Discharge per width y 0 Flow depth over the crest y 1 and y 2 Conjugated depths of hydraulic jump This article is part of the Topical Collection "Water and Energy" guest edited by Enrico Drioli.

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Numerical modeling of cavitation on spillway’s flip bucket

Cited by 34 publications

References 15 publications

Experimental and numerical modeling of sidewall orifices

Experimental and numerical modeling of sidewall orifices

Research on the Optimization Design of Abnormal Flip Buckets

Evaluation of energy dissipation on stepped spillway using evolutionary computing

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