Assessment of the Turbulent Prandtl Number Effect on Simulating Heat Transfer Characteristics of Supercritical Water Flow in Bare Tubes

2017 ◽  
Author(s):  
Amjad Farah ◽  
Glenn Harvel ◽  
Igor Pioro
Keyword(s):  
Heat Transfer ◽  
Fluid Dynamics ◽  
Kinetic Energy ◽  
Prandtl Number ◽  
Turbulent Kinetic Energy ◽  
Supercritical Water ◽  
Dissipation Rate ◽  
Turbulence Models ◽  
Turbulent Prandtl Number ◽  
Wide Range

Computational Fluid Dynamics (CFD) is a numerical approach to modelling fluids in multidimensional space using the Navier-Stokes equations and databases of fluid properties to arrive at a full simulation of a fluid dynamics and heat transfer system. The turbulence models employed in CFD are a set of equations that determine the turbulence transport terms in the mean flow equations. They are based on hypotheses about the process of turbulence, and as such require empirical input in the form of constants or functions, in order to achieve closure. By introducing a set of empirical constants to a model, that model then becomes valid for certain flow conditions, or for a range of flows. Of those constants, the turbulent Prandtl number appears in multiple equations; energy, momentum, turbulent kinetic energy, turbulent kinetic energy dissipation rate, etc. and the value it takes in each equation is different and chosen empirically to fit a wide range of flows in the subcritical region. The studies that attempt to find the effect of varying the turbulent Pr number on simulation results, often only mention one number; presumably the one that appears in the energy equation (although it is never explicitly explained). The rest of the constants are treated as universally acceptable for generalized flow and not tested for their effect on flow parameters. A numerical study on heat transfer to supercritical water flowing in a vertical tube is carried out using the ANSYS FLUENT code and employing the Realizable k-ε (RKE) and the SST k-ε turbulence models. The 3-D mesh consists of a 1/8 slice (45° radially) of a bare tube. The study explored the effects of turbulent Pr numbers, and their variations, in order to understand their significance, and to build on previous knowledge to modify the turbulence models and achieve higher accuracy in simulating experimental conditions. The numerical results of 3D flow and thermal distributions under normal and deteriorated heat transfer conditions are compared to experimental results. The distributions of temperature and turbulence levels are used to understand the underlying phenomena of the heat transfer deterioration in supercritical water flows. Reducing the energy turbulent Pr number produced the most accurate prediction of the deterioration in heat transfer, by altering the production term due to buoyancy, which appears in the equations for turbulent kinetic energy as well as its dissipation rate. The buoyancy forces in upward flows act to reduce the turbulent shear stress, resulting in localized increase in wall temperatures.

Numerical Investigation on Heat Transfer of Supercritical Water With a Variable Turbulent Prandtl Number Model

2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Xiangfei Kong ◽  
Dongfeng Sun ◽  
Lingtong Gou ◽  
Siqi Wang ◽  
Nan Yang ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Prandtl Number ◽  
Supercritical Fluids ◽  
Supercritical Water ◽  
Viscosity Ratio ◽  
Turbulent Viscosity ◽  
Turbulence Models ◽  
Turbulent Prandtl Number ◽  
Vertical Tubes

Abstract Turbulent Prandtl number (Prt) has a great impact on the performance of turbulence models in predicting heat transfer of supercritical fluids. Unrealistic treatment of Prt may lead to large deviations of the prediction results from experimental data under supercritical conditions. In this study, the effect of Prt on heat transfer of supercritical water was extensively studied by using shear stress transport (SST) k–ω turbulence model, and the results suggested that using the existing Prt models would lead to failures in predicting the heat transfer characteristics of supercritical water under deteriorated heat transfer (dht) conditions. A new variable Prt model was proposed with the Prt varied with pressure, turbulent viscosity ratio, and molecular Prandtl number. The new model was validated by comparing the numerical results with the corresponding experimental data, and it was found that the new variable Prt model exhibited better performance on reproducing the dht of supercritical water in vertical tubes than those of the existing Prt models.

scholarly journals On the turbulent Prandtl number in homogeneous stably stratified turbulence

2010 ◽  
Vol 644 ◽  
pp. 359-369 ◽  
Author(s):  
SUBHAS K. VENAYAGAMOORTHY ◽  
DEREK D. STRETCH
Keyword(s):  
Kinetic Energy ◽  
Prandtl Number ◽  
Turbulent Kinetic Energy ◽  
Decay Time ◽  
Time Scale ◽  
Richardson Number ◽  
Stratified Flows ◽  
Length Scale ◽  
Stratified Turbulence ◽  
Turbulent Prandtl Number

In this paper, we derive a general relationship for the turbulent Prandtl number Prt for homogeneous stably stratified turbulence from the turbulent kinetic energy and scalar variance equations. A formulation for the turbulent Prandtl number, Prt, is developed in terms of a mixing length scale LM and an overturning length scale LE, the ratio of the mechanical (turbulent kinetic energy) decay time scale TL to scalar decay time scale Tρ and the gradient Richardson number Ri. We show that our formulation for Prt is appropriate even for non-stationary (developing) stratified flows, since it does not include the reversible contributions in both the turbulent kinetic energy production and buoyancy fluxes that drive the time variations in the flow. Our analysis of direct numerical simulation (DNS) data of homogeneous sheared turbulence shows that the ratio LM/LE ≈ 1 for weakly stratified flows. We show that in the limit of zero stratification, the turbulent Prandtl number is equal to the inverse of the ratio of the mechanical time scale to the scalar time scale, TL/Tρ. We use the stably stratified DNS data of Shih et al. (J. Fluid Mech., vol. 412, 2000, pp. 1–20; J. Fluid Mech., vol. 525, 2005, pp. 193–214) to propose a new parameterization for Prt in terms of the gradient Richardson number Ri. The formulation presented here provides a general framework for calculating Prt that will be useful for turbulence closure schemes in numerical models.

scholarly journals On the prediction of equilibrium states in homogeneous turbulence

1989 ◽  
Vol 209 ◽  
pp. 591-615 ◽  
Author(s):  
Charles G. Speziale ◽  
Nessan Mac Giolla Mhuiris
Keyword(s):  
Kinetic Energy ◽  
Numerical Simulations ◽  
Turbulent Kinetic Energy ◽  
Time Evolution ◽  
Dissipation Rate ◽  
Flow Instability ◽  
Turbulence Models ◽  
Second Order ◽  
Equilibrium States ◽  
Unstable Flow

A comparison of several commonly used turbulence models (including the K–ε model and three second-order closures) is made for the test problem of homogeneous turbulent shear flow in a rotating frame. The time evolution of the turbulent kinetic energy and dissipation rate is calculated for these models and comparisons are made with previously published experiments and numerical simulations. Particular emphasis is placed on examining the ability of each model to predict equilibrium states accurately for a range of the parameter Ω/S (the ratio of the rotation rate to the shear rate). It is found that none of the commonly used second-order closure models yield substantially improved predictions for the time evolution of the turbulent kinetic energy and dissipation rate over the somewhat defective results obtained from the simpler K–ε model for the unstable flow regime. There is also a problem with the equilibrium states predicted by the various models. For example, the K–ε model erroneously yields equilibrium states that are independent of Ω/S while the Launder, Reece & Rodi model and the Shih-Lumley model predict a flow relaminarization when Ω/S > 0.39 - a result that is contrary to numerical simulations and linear spectral analyses, which indicate flow instability for at least the range 0 [les ] Ω/S [les ] 0.5. The physical implications of the results obtained from the various turbulence models considered herein are discussed in detail along with proposals to remedy the deficiencies based on a dynamical systems approach.

A Near-Wall Eddy Conductivity Model for Fluids With Different Prandtl Numbers

1994 ◽  
Vol 116 (4) ◽  
pp. 844-854 ◽  
Author(s):  
R. M. C. So ◽  
T. P. Sommer
Keyword(s):  
Kinetic Energy ◽  
Prandtl Number ◽  
Turbulent Kinetic Energy ◽  
Dissipation Rate ◽  
Reynolds Numbers ◽  
Turbulence Statistics ◽  
Temperature Variance ◽  
Prandtl Numbers ◽  
Good Agreement ◽  
Near Wall

Near-wall turbulence models for the velocity and temperature fields based on the transport equations for the Reynolds stresses, the dissipation rate of turbulent kinetic energy, and the temperature variance and its dissipation rate are formulated for flows with widely different Prandtl numbers. Conventional high-Reynolds-number models are used to close these equations and modifications are proposed to render them asymptotically correct near a wall compared to the behavior of the corresponding exact equations. Thus formulated, two additional constants are introduced into the definition of the eddy conductivity. These constants are found to be parametric in the Prandtl number. The near-wall models are used to calculate flows with different wall thermal boundary conditions covering a wide range of Reynolds numbers and Prandtl numbers. The calculated Nusselt number variations with Prandtl number are in good agreement with established formulae at two different Reynolds numbers. Furthermore, the mean profiles, turbulence statistics, heat flux, temperature variance, and the dissipation rates of turbulent kinetic energy and temperature variance are compared with measurements and direct numerical simulation data. These comparisons show that correct near-wall asymptotic behavior is recovered for the calculated turbulence statistics and the calculations are in good agreement with measurements over the range of Prandtl numbers investigated.

Characteristics of Turbulence in a Turbofan Stage

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Jeremy Maunus ◽  
Sheryl Grace ◽  
Douglas Sondak ◽  
Victor Yakhot
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Dissipation Rate ◽  
Turbulence Models ◽  
Energy Dissipation Rate ◽  
Scale Model ◽  
Length Scale ◽  
Experimental Measurements ◽  
Hot Wire ◽  
Turbofan Engine

Two-equation turbulence models are commonly used in the simulation of turbomachinery flow fields, but there are limited experimental data available to validate the resulting turbulence quantities. Experimental measurements are available from NASA’s Source Diagnostic Test (SDT), a 1/5th scale model representation of the bypass stage of a turbofan engine. Detailed unsteady hot-wire anemometer data were taken at two axial locations between the rotor and fan exit guide vanes (FEGVs). Here, an accurate and consistent procedure is used to obtain the turbulent kinetic energy, dissipation rate, and integral length scale from structure functions calculated using the SDT data. These results are compared to the solutions provided by four proprietary CFD codes that employ two-equation turbulence models. The simulations are shown to predict the turbulent kinetic energy and length scale reasonably well as well as the trend in mean dissipation. The actual mean dissipation rates differ by nearly two orders of magnitude due to a difference in interpretation between the classical definition and what is used in CFD.

Turbulent Heat Transfer to Heavy Liquid Metals in Circular Tubes

Volume 4 ◽  
2004 ◽  
Author(s):  
X. Cheng ◽  
A. Batta ◽  
H. Y. Chen ◽  
N. I. Tak
Keyword(s):  
Heat Transfer ◽  
Prandtl Number ◽  
Liquid Metals ◽  
Turbulence Models ◽  
Numerical Results ◽  
Heavy Liquid ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat ◽  
Turbulent Prandtl Number ◽  
Mesh Structure

The present paper gives a brief literature review on turbulent heat transfer in heavy liquid metals (HLM), especially liquid lead-bismuth eutectic (LBE). Some models available in the open literature on heat transfer and turbulent Prandtl number are assessed. In addition, CFD analysis is carried out for circular tube geometries. The effect of turbulence models, mesh structure and turbulent Prandtl number on the numerical results is studied. Application of ε-type turbulence models with scalable wall function shows less dependence of the numerical results on mesh structure than the ω-type turbulence models with automatic wall treatment. The turbulent Prandtl number affects strongly the heat transfer performance. Comparison between the CFD results, heat transfer correlations and heat transfer test data reveals a decrease in turbulent Prandtl number by increasing Reynolds number. Based on the results achieved, recommendations are made on correlations of heat transfer and turbulent Prandtl number for LBE flows.

scholarly journals Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Population Size ◽  
Turbulent Kinetic Energy ◽  
Forced Convection ◽  
Inlet Temperature ◽  
Fluid Temperature ◽  
Interface System

AbstractComputational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

Turbulent Transport in Pin Fin Arrays: Experimental Data and Predictions

2005 ◽  
Author(s):  
F. E. Ames ◽  
L. A. Dvorak
Keyword(s):  
Heat Transfer ◽  
Fluid Dynamics ◽  
Boundary Layers ◽  
Fluid Dynamic ◽  
Turbulent Transport ◽  
Turbulence Models ◽  
Reynolds Numbers ◽  
Velocity Profiles ◽  
Pin Fin ◽  
Pin Fin Arrays

The objective of this research has been to experimentally investigate the fluid dynamics of pin fin arrays in order to clarify the physics of heat transfer enhancement and uncover problems in conventional turbulence models. The fluid dynamics of a staggered pin fin array have been studied using hot wire anemometry with both single and x-wire probes at array Reynolds numbers of 3000; 10,000; and 30,000. Velocity distributions off the endwall and pin surface have been acquired and analyzed to investigate turbulent transport in pin fin arrays. Well resolved 3-D calculations have been performed using a commercial code with conventional two-equation turbulence models. Predictive comparisons have been made with fluid dynamic data. In early rows where turbulence is low, the strength of shedding increases dramatically with increasing in Reynolds numbers. The laminar velocity profiles off the surface of pins show evidence of unsteady separation in early rows. In row three and beyond laminar boundary layers off pins are quite similar. Velocity profiles off endwalls are strongly affected by the proximity of pins and turbulent transport. At the low Reynolds numbers, the turbulent transport and acceleration keep boundary layers thin. Endwall boundary layers at higher Reynolds numbers exhibit very high levels of skin friction enhancement. Well resolved 3-D steady calculations were made with several two-equation turbulence models and compared with experimental fluid mechanic and heat transfer data. The quality of the predictive comparison was substantially affected by the turbulence model and near wall methodology.

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