Investigation of MHD RANS Modeling Base on DNS Database Under the Advanced Blanket Design Conditions Utilized Molten Salt

2014 ◽  
Author(s):  
Naoki Osawa ◽  
Yoshinobu Yamamoto ◽  
Tomoaki Kunugi
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Turbulence Model ◽  
Turbulent Channel Flow ◽  
Turbulent Heat Transfer ◽  
Navier Stokes ◽  
Turbulent Heat ◽  
Mhd Turbulence ◽  
Rans Modeling ◽  
Heat Transfer Degradation

In this study, validations of Reynolds Averaged Navier-Stokes Simulation (RANS) based on Kenjeres & Hanjalic MHD turbulence model (Int. J. Heat & Fluid Flow, 21, 2000) coupled with the low-Reynolds number k-epsilon model have been conducted with the usage of Direct Numerical Simulation (DNS) database. DNS database of turbulent channel flow imposed wall-normal magnetic field on, are established in condition of bulk Reynolds number 40000, Hartmann number 24, and Prandtl number 5. As the results, the Nagano & Shimada model (Trans. JSME series B. 59, 1993) coupled with Kenjeres & Hanjalic MHD turbulence model has the better availability compared with Myong & Kasagi model (Int. Fluid Eng, 109, 1990) in estimation of the heat transfer degradation in MHD turbulent heat transfer.

Investigation of MHD RANS Modeling Base on DNS Database Under the Advanced Blanket Design Conditions Utilized Molten Salt

2014 ◽  
Author(s):  
Naoki Osawa ◽  
Yoshinobu Yamamoto ◽  
Tomoaki Kunugi
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Turbulence Model ◽  
Turbulent Channel Flow ◽  
Turbulent Heat Transfer ◽  
Navier Stokes ◽  
Turbulent Heat ◽  
Mhd Turbulence ◽  
Rans Modeling ◽  
Heat Transfer Degradation

In this study, validations of Reynolds Averaged Navier-Stokes Simulation (RANS) based on Kenjeres & Hanjalic MHD turbulence model (Int. J. Heat & Fluid Flow, 21, 2000) coupled with the low-Reynolds number k-epsilon model have been conducted with the usage of Direct Numerical Simulation (DNS) database. DNS database of turbulent channel flow imposed wall-normal magnetic field on, are established in condition of bulk Reynolds number 40000, Hartmann number 24, and Prandtl number 5. As the results, the Nagano & Shimada model (Trans. JSME series B. 59, 1993) coupled with Kenjeres & Hanjalic MHD turbulence model has the better availability compared with Myong & Kasagi model (Int. Fluid Eng, 109, 1990) in estimation of the heat transfer degradation in MHD turbulent heat transfer.

On the Wall Boundary Condition for Computing Turbulent Heat Transfer With K–ω Models

2000 ◽  
Author(s):  
J. Bredberg ◽  
S.-H. Peng ◽  
L. Davidson
Keyword(s):  
Heat Transfer ◽  
Boundary Condition ◽  
Reynolds Number ◽  
Channel Flow ◽  
Turbulence Model ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat ◽  
Wall Function ◽  
Wall Boundary Condition ◽  
Wall Boundary

Abstract A new wall boundary condition for the standard Wilcox’s k–ω model (1988) is proposed. The model combines a wall function and a low-Reynolds number approach, and a function that smoothly blends the two formulations, enabling the model to be used independently of the location of the first interior computational node. The model is calibrated using DNS-data for a channel flow and applied to a heat transfer prediction for a flow in a rib-roughened channel (Reb = 100 000). The results obtained with the new model are improved for various mesh sizes and are asymptotically identical with those of the standard k–ω turbulence model.

DNS of Heat Transfer Reduction in Viscoelastic Turbulent Channel Flows

2015 ◽  
Author(s):  
Kyoungyoun Kim ◽  
Radhakrishna Sureshkumar
Keyword(s):  
Heat Transfer ◽  
Channel Flow ◽  
Reduction Rate ◽  
Turbulent Channel Flow ◽  
Passive Scalar ◽  
Heat Fluxes ◽  
Turbulent Heat Transfer ◽  
Uniform Heat Flux ◽  
Turbulent Heat ◽  
Turbulent Heat Fluxes

A direct numerical simulation (DNS) of viscoelastic turbulent channel flow with the FENE-P model was carried out to investigate turbulent heat transfer mechanism of polymer drag-reduced flows. The configuration was a fully-developed turbulent channel flow with uniform heat flux imposed on both walls. The temperature was considered as a passive scalar. The Reynolds number based on the friction velocity (uτ) and channel half height (δ) is 125 and Prandtl number is 5. Consistently with the previous experimental observations, the present DNS results show that the heat-transfer coefficient was reduced at a rate faster than the accompanying drag reduction rate. Statistical quantities such as root-mean-square temperature fluctuations and turbulent heat fluxes were obtained and compared with those of a Newtonian fluid flow. Budget terms of the turbulent heat fluxes were also presented.

Turbulent Heat Transfer Computations for Rearward-Facing Steps and Sudden Pipe Expansions

1985 ◽  
Vol 107 (1) ◽  
pp. 70-76 ◽  
Author(s):  
A. M. Gooray ◽  
C. B. Watkins ◽  
Win Aung
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Fluid Dynamic ◽  
Turbulence Models ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat ◽  
Streamline Curvature ◽  
Recirculating Flow ◽  
Moderate Reynolds Number ◽  
Entire Flow

Results of numerical calculations for heat transfer in turbulent recirculating flow over two-dimensional, rearward-facing steps and sudden pipe expansions are presented. The turbulence models used in the calculation are the standard k – ε model and the low-Reynolds number version of this model. The k – ε models have been improved to account for the effects of streamline curvature and pressure-strain (scalar) interactions including wall damping. A sequence of two computational passes is performed to obtain optimal results over the entire flow field. The presented results consist of computed distributions of heat transfer coefficents for several Reynolds numbers, emphasizing the low-to-moderate Reynolds number regime. The heat transfer results also include correlations of Nusselt numnber for both side and bottom walls. The computed heat transfer results and typical computed fluid dynamic results are compared with available experimental data.

scholarly journals Computation of Turbulent Heat Transfer on the Walls of a 180 Degree Turn Channel With a Low Reynolds Number Reynolds Stress Model

2002 ◽  
Author(s):  
D. L. Rigby ◽  
A. A. Ameri ◽  
E. Steinthorsson
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Reynolds Stress ◽  
Low Reynolds Number ◽  
Reynolds Stress Model ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat ◽  
Stress Model ◽  
Generalized Gradient ◽  
Low Reynolds

The Low Reynolds number version of the Stress-ω model and the two equation k-ω model of Wilcox were used for the calculation of turbulent heat transfer in a 180 degree turn simulating an internal coolant passage. The Stress-ω model was chosen for its robustness. The turbulent thermal fluxes were calculated by modifying and using the Generalized Gradient Diffusion Hypothesis. The results showed that using this Reynolds Stress model allowed better prediction of heat transfer compared to the k-ω two equation model. This improvement however required a finer grid and commensurately more CPU time.

Turbulent Heat Transfer Characteristics of Supercritical Carbon Dioxide for a Vertically Upward Flow in a Pipe Using Computational Fluid Dynamics and Artificial Neural Network

2021 ◽  
Author(s):  
Rajendra Prasad K S ◽  
Krishna V ◽  
Sachin Bharadwaj ◽  
Babu Rao Ponangi
Keyword(s):  
Heat Transfer ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Turbulence Model ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat ◽  
Transfer Coefficients ◽  
Ansys Fluent ◽  
Artificial Neural ◽  
Turbulent Models

Abstract Modelling of turbulence heat transfer for supercritical fluids using Computational Fluid Dynamics (CFD) software is always challenging due to the drastic property variations near critical point. Use of Artificial Neural Networks (ANN) along with numerical methods have shown promising results in predicting heat transfer coefficients of heat exchangers. In this study, accuracy of four different turbulent models available in the commercial CFD software - Ansys Fluent is investigated against the available experimental results. The k-e Re Normalization Group (RNG) model with enhanced wall treatment is found to be the best-suited turbulence model. Further, K-e RNG Turbulence Model is used in CFD for parametric analysis to generate the data for ANN studies. A total of 1,34,698 data samples were generated and fed into the ANN program to develop an equation that can predict the heat transfer coefficient. It was found that, for the considered range of values the absolute average relative deviation is 3.49%.

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