Predictions of Turbulent Heat Transfer in an Axisymmetric Jet Impinging on a Heated Pedestal

1999 ◽  
Vol 121 (1) ◽  
pp. 43-49 ◽  
Author(s):  
S. Parneix ◽  
M. Behnia ◽  
P. A. Durbin
Keyword(s):  
Heat Transfer ◽  
Flat Plate ◽  
Turbulence Model ◽  
Reynolds Numbers ◽  
Turbulent Heat Transfer ◽  
Recent Experimental Data ◽  
Turbulent Heat ◽  
Normal Velocity ◽  
Comparison Results ◽  
The One

Cooling or heating of a flat plate by an impinging jet, due to its many applications, has been widely studied. Recent experimental data concerning more complex geometries has become available. In this study, the cooling of a heated pedestal mounted on a flat plate, a configuration which is closer to the one met in some engineering applications (e.g., cooling of electronic components), has been numerically simulated. The normal velocity relaxation turbulence model (V2F model) in an axisymmetric geometry has been adopted. Results have been obtained for a range of jet Reynolds numbers and jet-to-pedestal distances. Comparison of the predicted heat transfer coefficient with experiments has shown a very good agreement. For comparison, results have also been obtained with the widely used κ – ε turbulence model and the agreement with the data is poor.

A Summary of Experiments on Turbulent Heat Transfer From a Nonisothermal Flat Plate

1960 ◽  
Vol 82 (4) ◽  
pp. 341-348 ◽  
Author(s):  
W. C. Reynolds ◽  
W. M. Kays ◽  
S. J. Kline
Keyword(s):  
Heat Transfer ◽  
Boundary Layer ◽  
Experimental Investigation ◽  
Flat Plate ◽  
Reynolds Numbers ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat ◽  
Low Mach Number ◽  
Incompressible Boundary ◽  
Case Data

The results of an extensive experimental investigation of heat transfer to a turbulent incompressible boundary layer from a nonisothermal flat plate are summarized. Data presented extend the range of low-Mach-number confirmation of the von Karman analogy to Reynolds numbers of 4 × 106 for an isothermal plate. Data for a step wall-temperature distribution confirm experimentally the preferable expression for this important superposition kernel case. Data from a variety of other examples confirm the use of the superposition theories to predict heat transfer from nonisothermal surfaces.

DNS study on turbulent heat transfer induced by virtual turbulence at inlet in boundary layer on a flat plate

2020 ◽  
Vol 2020 (0) ◽  
pp. 0125
Author(s):  
Hirofumi HATTORI ◽  
Keita KANO ◽  
Haruka TADANO ◽  
Tomoya HOURA ◽  
Masato TAGAWA
Keyword(s):  
Heat Transfer ◽  
Boundary Layer ◽  
Flat Plate ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat

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.

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%.

Turbulent Heat Transfer at Low Reynolds Numbers

1969 ◽  
Vol 91 (4) ◽  
pp. 532-536 ◽  
Author(s):  
C. J. Lawn
Keyword(s):  
Heat Transfer ◽  
Gas Flow ◽  
Eddy Diffusivity ◽  
Reynolds Numbers ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat ◽  
Low Reynolds Numbers ◽  
Eddy Diffusivities ◽  
Uniform Wall ◽  
Uniform Wall Heat Flux

A realistic velocity profile and semiempirical values for the ratio of the eddy diffusivities of momentum and heat are used to solve the heat-balance equation for the situation of fully developed gas flow in a pipe with uniform wall heat flux. The predicted heat transfer is higher than the experimental at Reynolds numbers below 104 and this is shown to be due to the inadequacy of the simple eddy-diffusivity hypothesis.

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