Chapter 13 Performance of six turbulence models in predicting two-phase flow on a hydraulic test bench
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Khadija Rahal
Abstract
In this study, numerical computations were conducted using the CFD software ANSYS Fluent 17.0. The volume of fluid approach was employed to capture the position of the interface between water and air. The unsteady Reynolds-averaged Navier-Stokes (URANS) equations were used to model the unsteady turbulent flow in a large-scale hydraulic test bench. These equations require turbulence models to close the Reynolds stress tensor. The choice of the turbulence model is a compromise between the accuracy and the computing time, and they can affect the solution accuracy. This study mainly aims to examine six turbulence models and to select the best model that can provide the most accurate simulation of the flow field over the hydraulic test bench. The six turbulence models examined are the standard k-ε model, the realizable k-ε model, the RNG k-ε model, the standard k-ω model, the transition SST k-ω model and the BSL k-ω model. The distribution of the water-air flow characteristics such as the magnitude velocity, the pressure and the turbulence characteristics were presented for each model. The validation of our numerical model was achieved by comparing the present predictions with the experimental data of (Koshizuka et al. 1995) for the breaking of a dam. It is worth noting that the RNG k-ε model provided the best findings along the test section compared to the other models. We can conclude that the RNG k-ɛ model is the best in terms of accuracy, stability and efficiency of the computed solutions.
Abstract
In this study, numerical computations were conducted using the CFD software ANSYS Fluent 17.0. The volume of fluid approach was employed to capture the position of the interface between water and air. The unsteady Reynolds-averaged Navier-Stokes (URANS) equations were used to model the unsteady turbulent flow in a large-scale hydraulic test bench. These equations require turbulence models to close the Reynolds stress tensor. The choice of the turbulence model is a compromise between the accuracy and the computing time, and they can affect the solution accuracy. This study mainly aims to examine six turbulence models and to select the best model that can provide the most accurate simulation of the flow field over the hydraulic test bench. The six turbulence models examined are the standard k-ε model, the realizable k-ε model, the RNG k-ε model, the standard k-ω model, the transition SST k-ω model and the BSL k-ω model. The distribution of the water-air flow characteristics such as the magnitude velocity, the pressure and the turbulence characteristics were presented for each model. The validation of our numerical model was achieved by comparing the present predictions with the experimental data of (Koshizuka et al. 1995) for the breaking of a dam. It is worth noting that the RNG k-ε model provided the best findings along the test section compared to the other models. We can conclude that the RNG k-ɛ model is the best in terms of accuracy, stability and efficiency of the computed solutions.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of authors VII
- Chapter 1 Use of digital systems in the design system of photovoltaic solar stations 1
- Chapter 2 Potential wind energy in Turkmenistan 21
- Chapter 3 Potential of using biogas technology in Turkmenistan 31
- Chapter 4 Energy efficiency 45
- Chapter 5 Latent renewable energy in Turkmenistan 57
- Chapter 6 Approximate stochastic simulation algorithms 67
- Chapter 7 The role of supply chain management in the construction industry 95
- Chapter 8 Selection of threshold in binary graphs of biological networks 121
- Chapter 9 Model selection criteria with bootstrap algorithms: applications in biological networks 133
- Chapter 10 Technocracy in Governance: new directions in city functioning and urban planning 149
- Chapter 11 Outlier detection in biomedical data: ECG-focused approaches 161
- Chapter 12 Optimization of debt collection strategies for South African banks with machine learning models 183
- Chapter 13 Performance of six turbulence models in predicting two-phase flow on a hydraulic test bench 209
- Index 231
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of authors VII
- Chapter 1 Use of digital systems in the design system of photovoltaic solar stations 1
- Chapter 2 Potential wind energy in Turkmenistan 21
- Chapter 3 Potential of using biogas technology in Turkmenistan 31
- Chapter 4 Energy efficiency 45
- Chapter 5 Latent renewable energy in Turkmenistan 57
- Chapter 6 Approximate stochastic simulation algorithms 67
- Chapter 7 The role of supply chain management in the construction industry 95
- Chapter 8 Selection of threshold in binary graphs of biological networks 121
- Chapter 9 Model selection criteria with bootstrap algorithms: applications in biological networks 133
- Chapter 10 Technocracy in Governance: new directions in city functioning and urban planning 149
- Chapter 11 Outlier detection in biomedical data: ECG-focused approaches 161
- Chapter 12 Optimization of debt collection strategies for South African banks with machine learning models 183
- Chapter 13 Performance of six turbulence models in predicting two-phase flow on a hydraulic test bench 209
- Index 231
