scholarly journals Conditional statistics of temperature fluctuations in turbulent convection

2001 ◽  
Vol 63 (4) ◽  
Author(s):  
Emily S. C. Ching ◽  
K. L. Chau
Keyword(s):  
Temperature Fluctuations ◽  
Turbulent Convection ◽  
Conditional Statistics

scholarly journals Transition on local temperature fluctuations in highly turbulent convection

2009 ◽  
Vol 87 (4) ◽  
pp. 44006 ◽  
Author(s):  
F. Gauthier ◽  
J. Salort ◽  
O. Bourgeois ◽  
J.-L. Garden ◽  
R. du Puits ◽  
...  
Keyword(s):  
Temperature Fluctuations ◽  
Turbulent Convection ◽  
Local Temperature

EFFECTS OF A LARGE-SCALE MEAN FLOW IN A SHELL MODEL OF TURBULENT CONVECTION

2007 ◽  
Vol 21 (23n24) ◽  
pp. 4178-4183 ◽  
Author(s):  
EMILY S. C. CHING ◽  
H. GUO
Keyword(s):  
Shell Model ◽  
Large Scale ◽  
Temperature Fluctuations ◽  
Scaling Behavior ◽  
The Other ◽  
Turbulent Convection ◽  
Mean Flow ◽  
Mean Velocity ◽  
Other Hand ◽  
Flow Reversals

The possible effects of a large-scale mean flow, represented as a non-zero mean velocity in the shell of the largest scale, are studied using a shell model of turbulent convection. In the regime where buoyancy is dynamically important, flow reversals are not observed. On the other hand, flow reversals are found in the regime where buoyancy is not dynamically important. In both regimes, the presence of such a large-scale mean flow does not change the scaling behavior of the velocity and temperature fluctuations.

scholarly journals Simulations of Boiling Systems Using a Lattice Boltzmann Method

2013 ◽  
Vol 13 (3) ◽  
pp. 696-705 ◽  
Author(s):  
L. Biferale ◽  
P. Perlekar ◽  
M. Sbragaglia ◽  
F. Toschi
Keyword(s):  
Numerical Simulations ◽  
Lattice Boltzmann Method ◽  
Latent Heat ◽  
Lattice Boltzmann ◽  
Temperature Fluctuations ◽  
Convective Cell ◽  
Turbulent Convection ◽  
Multi Phase ◽  
Boltzmann Method ◽  
Pseudo Potential

AbstractWe report about a numerical algorithm based on the lattice Boltzmann method and its applications for simulations of turbulent convection in multi-phase flows. We discuss the issue of ’latent heat’ definition using a thermodynamically consistent pseudo-potential on the lattice. We present results of numerical simulations in 3D with and without boiling, showing the distribution of pressure, density and temperature fluctuations inside a convective cell.

scholarly journals “Clusterization” and intermittency of temperature fluctuations in turbulent convection

2004 ◽  
Vol 69 (5) ◽  
Author(s):  
A. Bershadskii ◽  
J. J. Niemela ◽  
A. Praskovsky ◽  
K. R. Sreenivasan
Keyword(s):  
Temperature Fluctuations ◽  
Turbulent Convection

Investigation of the subgrid-scale fluxes and their production rates in a convective atmospheric surface layer using measurement data

2010 ◽  
Vol 660 ◽  
pp. 282-315 ◽  
Author(s):  
QINGLIN CHEN ◽  
SHUAISHUAI LIU ◽  
CHENNING TONG
Keyword(s):  
Surface Layer ◽  
Production Rate ◽  
Atmospheric Surface Layer ◽  
Potential Temperature ◽  
Measurement Data ◽  
Temperature Fluctuations ◽  
Positive Temperature ◽  
Subgrid Scale ◽  
Mean Values ◽  
Conditional Statistics

The subgrid-scale (SGS) potential temperature flux and stress in the atmospheric surface layer are studied using field measurement data. We analyse the mean values of the SGS temperature flux, the SGS temperature flux production rate, the SGS temperature variance production rate, the SGS stress and the SGS stress production rate conditional on both the resolvable-scale velocity and temperature, which must be reproduced by SGS models for large-eddy simulation to reproduce the one-point resolvable-scale velocity–temperature joint probability density function (JPDF). The results show that the conditional statistics generally depend on the resolvable-scale velocity and temperature fluctuations, indicating that these conditional variables have strong influences on the resolvable-scale statistics. The dependencies of the conditional SGS stress and the SGS stress production rate, which are partly due to the effects of flow history and buoyancy, suggest that model predictions of the SGS stress also affect the resolvable-scale temperature statistics. The results for the conditional flux and the conditional flux production rate vectors have similar trends. These conditional vectors are also well aligned. The positive temperature fluctuations associated with updrafts are found to have a qualitatively different influence on the conditional statistics than the negative temperature fluctuations associated with downdrafts. The conditional temperature flux and the temperature flux production rate predicted using several SGS models are compared with measurements in statistical a priori tests. The predictions using the nonlinear model are found to be closely related to the predictions using the Smagorinsky model. Several potential effects of the SGS model deficiencies on the resolvable-scale statistics, such as the overprediction of the vertical mean temperature gradient and the underprediction of the vertical temperature flux, are identified. The results suggest that efforts to improve the LES prediction of a resolvable-scale statistic must consider all the relevant SGS components identified using the JPDF equation and the surface layer dynamics. This study also provides impetus for further investigations of the JPDF equation, especially analytical studies on the relationship between the JPDF and the SGS terms that govern its evolution.

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