Ind. Eng. Chem. Res. 2004, 43, 2816-2829

CFD Simulation of Heat Transfer in Turbulent Pipe Flow Mahesh T. Dhotre and Jyeshtharaj B. Joshi* Institute of Chemical Technology, University of Mumbai, Matunga, Mumbai-400 019, India

In the present work, the flow pattern in pipe flow has been simulated using a low-Reynoldsnumber k- model. The model has been extended to predict the heat-transfer coefficient in regions of both high and low Prandtl number. The computational fluid dynamic (CFD) approach has been shown to be superior to the conventional semiempirical approach for high turbulent Prandtl numbers and in cases where the molecular properties such as diffusivity and viscosity steeply change in the vicinity of the wall. 1. Introduction It is well-known that the rates of transfer of heat, mass, and momentum between a solid wall and a fluid in turbulent flow can, under suitable conditions, be characterized by an eddy diffusion coefficient. The concept of an eddy diffusion coefficient, however, does not provide a physical basis to illuminate the nature of turbulence; it does provide a means of calculating massor heat-transfer rates from momentum-transfer rates. The calculation procedure involves determining the momentum eddy diffusivity distribution from the slope of the velocity profiles, taking the eddy diffusivity of mass and heat to be equal or proportional to the eddy diffusivity of momentum, and then integrating the transport equations to obtain a temperature or concentration profile. The real success of any such analysis depends to a considerable extent on how carefully the eddy diffusivity variation is chosen near the wall. Calculations of this type have been reported in the literature, and the principal difference among most of them is the starting point of the velocity distribution. In view of the importance of the accurate description of the velocity profile and the eddy diffusivity near the wall, an attempt is made in the present work to employ computational fluid dynamics (CFD) for this purpose. In recent years, low-Reynolds-number k- models of turbulence have been widely used in numerical simulations because of their ability to resolve the near-wall region. The low-Reynolds-number k- modeling approach incorporates either a wall-damping effect, or a direct effect of molecular viscosity, or both in the turbulence transport equations. Fairly complete reviews of low-Reynolds-number k- models of turbulent shear flows have been presented by Patel et al.,1 Hrenya et al.,2 and Thakre and Joshi.3,4 Near-wall turbulence models or low-Reynolds-number models that attempt to describe the relative influence of molecular and turbulent viscosities have been developed for single-phase flows. Thakre and Joshi3 have analyzed 12 different lowReynolds-number k- models for the case of a singlephase pipe flow. For this purpose, they used four criteria, three of which were accurate predictions of the radial variation of axial velocity, the turbulent kinetic energy, and the eddy diffusivity (compared with nearwall experimental data of Durst et al.5). The fourth * To whom correspondence should be addressed. Tel.: 0091-22-2414 5616. Fax: 00-91-22-2414 5614. E-mail: [email protected] udct.org.

criterion was rather stringent and stipulated the necessary condition of the overall energy balance, i.e., the volume integral of  must be equal to the energy-input rate (the pressure drop multiplied by the volumetric flow rate). All four criteria were found to be satisfied by the model of Lai and So6 (LSO), as shown by Thakre and Joshi.3 The authors successfully applied Lai and So6 model to predict the heat-transfer coefficient in singlephase pipe flows. Because the heat-transfer process depends entirely on the flow pattern, a model that gives good flow predictions can be expected to give good heattransfer predictions as well. However, this statement should be in the form of a question. There are a few possible reasons for this limitation: (1) Few attempts have been made to extend the flow knowledge from lowReynolds-number models to heat transfer. (2) The accuracy of modeling the energy-dissipation equation (which directly affects the flow quantities and thereby heat transfer)is limited. (3) The selection of the turbulent Prandtl number, Prt (one value or radial profile), is difficult. The Prt concept is widely used in an eddy diffusivity model, in which the eddy diffusivity for the momentum is evaluated by either a mixing-length model or the k- model and the eddy diffusivity for heat is evaluated using Prt-based relationships. The knowledge of Prt is a central problem of all theoretical considerations concerning turbulent heat transfer in the duct flows. A large number of models that address the estimation of Prt for such situations have been published. Reviews of the existing work can be found in Reynolds,7 Kays,8 and more recently Churchill.9 In view of the above considerations, it was thought desirable to analyze the reported turbulent Prandtl number relationships and understand turbulent heat transfer using a low-Reynolds-number k- model over a wide range of Prandtl and Reynolds numbers. Toward this end, we selected the Prt formulation proposed by Yakhot and Orszag10 and compared the results with the experimental data of Monin and Yaglom,11 Skupinski et al.,12 Fuchs,13 and Kader.14 2. Previous Work After the pioneering beginning by Reynolds,15 a large number of analytical solutions have been published until fairly recently.16 In all of these cases, correlations were given for eddy diffusivity together with boundary conditions. The mass or heat balance equation was solved analytically, and the resulting predictions for the mass- and heat-transfer coefficients were given in the

10.1021/ie0342311 CCC: $27.50 © 2004 American Chemical Society Published on Web 04/24/2004

Ind. Eng. Chem. Res., Vol. 43, No. 11, 2004 2817

form of correlations. Asymptotic results have also been presented for high values of Sc and Pr. These types of studies spanned over 125 years; a brief summary of them, in chronological order, is presented in Table 1, and further details are provided by Thakre and Joshi.4 Churchill and co-workers9,28-30 investigated this problem comprehensively and presented new formulation for the prediction of turbulent flow and convection. Churchill28 proposed interesting simplified models and integral solutions for the case of fully developed convection in terms of the local fraction of transport due to turbulence. A very general and accurate correlating equation for the turbulent shear stress allowed the use of these integral formulations in the derivation of even more accurate numerical solutions for the velocity distribution in fully developed flow. This implementation for numerical calculations necessarily invokes some empiricism, but generally less than that required by prior models and formulations. Heng et al.29 used models proposed by Churchill28 to compute improved values for the Nusselt number for fully developed convection in uniformly heated round tubes over a wide range of Reynolds numbers. The results for two limiting (Pr ) 0, Pr f 0) and one intermediate (Pr ≈ 0.87) values were presented and found to be more accurate than the previous predictions. Yu et al.30 used models proposed by Churchill28 and presented differential models for fully developed turbulent flow and convection. They considered two cases, one for uniform heating and another more complex case of uniform temperature. They solved the differential model for all values of Pr and a wide range of Reynolds numbers. They found that the predictions (for the case of uniform heating) are slightly more accurate than those of Heng et al.29 Even though the latter authors had used the same basic model, Yu et al.30 obtained rapid and complete convergence by incorporating a stepwise procedure for solving the differential equations, whereas Heng et al.29 had evaluated integrals by quadrature. The predicted rates of heat transfer were found to be relatively insensitive to the particular empirical expressions used for the turbulent Prandtl number, which is the only significant source of uncertainty. Churchill9 showed the eddy viscosity at any location in fully developed turbulent pipe flow to be simply a ratio of the shear stress due to the time-averaged turbulent fluctuations in the velocity to that due to the molecular motion. The eddy conductivity in fully developed turbulent convection was similarly shown to be a ratio of the corresponding contributions to the radial heat flux density. Churchill9 re-expressed the turbulent Prandtl number in terms of stresses and used different Prt expressions proposed by Notter and Sleicher,25 Kays,8 Jischa and Rieke,31 and Yakhot et al.10 He found that the predictions of Kays8 and Yakhot et al.10 were in fair agreement for small values of Pr, but those of Jischa and Rieke31 and Notter and Sleicher25 were much higher. Further, he found the prediction of the Nusselt number to be insensitive to the turbulent Prandtl number and pointed out a need for improved measurements of flow parameters and correlating equations for turbulent Prandtl number. In a totally different approach, Fortuin et al.16 extended the random surface renewal (RSR) model of Fortuin and Klijn32 to describe heat- and mass-transfer processes in turbulent pipe flow assuming that the tube

wall is covered by a mosaic of fluid elements of laminar flow and random ages. This so-called extended random surface renewal model (ERSR) was used to derive a relationship between the friction factor and the mean value of the randomly distributed ages of the fluid elements at the tube wall, showing that the age distribution and the mean age of the fluid elements at the tube wall agree quantitatively with the experimental results obtained from the velocity signals measured with a laser Doppler anemometer. This information was then used to calculate the time-averaged radial profiles of the axial velocity, the temperature, and the concentration in the wall region; to derive equations for the quantitative prediction of heat- and mass-transfer coefficients; and to provide a basis for explaining the analogy between momentum, heat, and mass transfer in turbulent pipe flow. Figure 1 shows a comparison of predictions of various theories. It can be seen that large variations exist in the predictions, both among themselves and as compared with the experimental data. The above discussion (also refer to Thakre and Joshi4) gives an account for these variations. They are mainly due to the particular selection of eddy diffusivity profiles and the other simplifications in the solution procedure. In view of the importance of accurate descriptions of the velocity profile and the eddy diffusivity near the wall, various attempts have been made to employ computational fluid dynamics (CFD) for the prediction of heat transfer. These efforts can be classified as (a) eddy viscosity models, (b) Reynolds-stress model (RSM), (c) large eddy simulation (LES), and (d) direct numerical simulation (DNS). The widely used eddy viscosity models are of three types: k- (Launder and Spalding33), k-τ (Speziale et al.34), and k-ω (Wilcox35). Sarkar and So36 carried out a critical evaluation of these models against direct numerical simulation for six flow cases. They found that the k- models that are asymptotically consistent perform better than the k-τ and k-ω models. Moreover, the k-τ and k-ω models exhibit no real numerical advantage over the k- models. There are two main methods for the near-wall treatment of these models: (i) the wall function approach (Launder and Spalding37) and (ii) low-Reynolds-number modeling. In most industrial CFD simulations, a wall function approach is used, which bridges the viscous sublayer and makes use of the available knowledge in the logarithmic profile region of the boundary layer. This method allows for the use of much coarser near-wall grids, which also benefits the cell aspect ratio. On the downside, wall functions impose strict limitations on the grid-generation process, as they impose an upper limit on the grid density so that it remains in the logarithmic part of the boundary layer. Experience has shown that this is a severe limitation that has negatively impacted a large number of industrial CFD simulations. Another more promising approach is low-Reynolds-number modeling. Jones and Launder38 were the first to propose a low-Reynoldsnumber k- model for near-wall turbulence, which was then followed by a number of similar k- models. Fairly complete reviews of the development of such twoequation near-wall turbulence closures have been provided by Patel et al.,1 Hyrenya et al.,2 and Thakre and Joshi.4 Thakre and Joshi4 presented a systematic analysis of 12 versions of low-Reynolds-number k- models for heat transfer. The model by Lai and So6 was shown

momentum and energy transported at equal mass rates between the bulk of the fluid and the wall of the tube by the oscillatory radial motion of the eddies eddies penetrate only to a finite distance from the wall, and transport of momentum and energy through the remaining distance occurs by a linear process of molecular diffusion transport in the buffer layer assumed to occur by both molecular and eddy diffusivity; universal velocity profile used for the estimation of νT; closed form obtained by neglecting molecular diffusion in the turbulent core as in von Karman analysis, transport in the buffer layer assumed to occur by both molecular and eddy diffusivity; universal velocity profile used for the estimation of νT following Prandtl, assumed that νT ) 0 in the viscous sublayer and turbulent effects predominate in finite distance from the wall; distance can be estimated by the point at Prt ) 1 doubted truth of existence of a sublaminar layer for molecular diffusion and attempted to model by introducing into the laminar sublayer a small appropriate amount of eddy motion that decreases very rapidly to zero at the wall; implemented by introducing a correlation for νT in the laminar sublayer (y+ < 5) in terms of eddy diffusivity varying as the cubic power of the normal wall distance (a) eddy diffusivities for momentum (νT) and heat transfer (νH) assumed equal; variations of the shear stress (τ) and heat-transfer flux (q) across the tube assumed to have a negligible effect on the velocity and temperature distributions (b) contribution of molecular shear stress and heat transfer can be neglected in the region sufficiently far from the wall



Lin et al.21



von Karman18




Table 1. Previous Work: Analytical Approach

) )

1 - y+ R+ 1 - y+ R+

( (

+ ) 1 + q[1 - exp(q)] y + ) 2.78 q ) n2u+y+

( )

+ ) 1 + ) 0.4y+ y+ 1 can be estimated by the point at Prt ) 1 y+ 3 + ) 1 + 14.5 + ) 1 + 0.2y+ - 0.959 + +  ) 0.4y

+ ) 0.4y+

+ ) 0.2y+

0 e y+ e 26 y+ > 26

0 < y+ e 5 5 < y+ < 30 y+ > 30

0 e y+ e y+ 1 y+ 1 > 30

0 e y+ e 5 5 e y+ e30 y+ > 30

0 e y+ e 5 5 e y+ e 30 y+ > 30

+ ) 1 + ) 0.2y+ + ) 0.4y+



0 e y+ e 11.5 y+ > 11.5


νt ν

+ ) 1 + ) 0.4y+



+ ) 1 +


) )


Nu )

x2f Re Pr




[ ( ) ]}

ln Re + ln Pr + ln x2 / f + (1 + ln 0.2)


0.248Re Pr xf(Pr)-0.75 π

f Re Pr x 2 Nu ) x2f + F(Pr)

Nu )


x1f (T



1 + 5Pr (2/f)0.5 + 5(Pr - 1) + 5 ln 6

Re Prxf/2

1 1 δ+ 1 + 1Pr Re(f/2) Re Pr(f/2)

TW - T C Re Pr W - TM Nu ) Pr Re f Pr 5 + ln 1 + 5 + 2.5F ln Prt Prt 60 2

Nu )

Nu )

Nu ) Re Pr(f/2)

resulting equations for mass and heattransfer coefficients

used power-law expression for νT/ν near the wall

equation found to agree well with data over a wide range of Pr and Sc values (0.5-3200)

analysis found to be in agreement with experimental data for heat transfer in liquid metals

applicable for Pr ) 0.5-30

improvement over Prandtl treatment for Pr > 1 due to allowance made for the existence of turbulence down to y+ ) 5, rather than y+ ) 8.7, as by Prandtl

0.73 < Pr < 40 improvement over Reynolds treatment for Pr > 1 due to allowance made for the existence of turbulence down to y+ ) 8.7

applicable only for Pr ) 1


2818 Ind. Eng. Chem. Res., Vol. 43, No. 11, 2004

assumed shear stress and heat flux to be linear functions of the radial position within the tube; assumed eddy diffusivities for heat transfer and momentum transfer to be equal; maintained the form of the Prandtl analogy; values of constants varied to account for the turbulent transport in the laminar sublayer and the variation in thickness of the laminar sublayer with respect to the Reynolds number eddy viscosity distribution obtained in terms of increasing powers of y+ from the wall; away from the wall, von Karman’s velocity distribution function retained; after velocity and νT distributions defined, diffusion-convection equation solved numerically to obtain expressions for local mass-transfer rates without reference to any hypothetical mechanism of turbulence, chose a simple, empirical function for the eddy diffusivity that is constrained to go to zero as y f 0 and to agree with measurements in the region where measurements are reliable, y+ > 10 logarithmic velocity distribution law does not hold for the region close to the wall (i.e., y+ < 30); proposed a model assuming the eddy viscosity to be proportional to the distance from the wall, shear stress to be constant, and eddy viscosity and velocity to be continuous for the whole range of pipe section presented a method to calculate heat-transfer coefficients from a more fundamental approach using eddy viscosity data in the integration of the energy equation

Friend and Metzner23

Edwards and Smith27

Muzushina and Ogino26

Notter and Sleicher25

Wasan and Wilke24



Table 1. (Continued)

+ 3

+ 4

a(y+)3 - b(y+)4


νt ν


St ) 0.827b2/3(f/2)0.5Pr(-2/3)

( -

valid for Pr ) 0.6-100

+ ) 5.23 × 10-4(y+)3

valid for Pr > 100

valid for Re ) 3000-(3 × 106) Pr ) 0.5-600


St ) 0.0149Re-0.12 Pr(-2/3) Nu ) 5 + 0.016ReaPrb a ) 0.88 - 0.24/(4 + Pr) b ) 0.33 + 0.5 exp(-0.6Pr)


0 < y+ < 45

Re Pr(f/2)


Re Pr(f/2) f 1/2(Pr - 1) 1.2 + 11.8 2 Pr1/2

, 1 + xf/2[13.0(Pr)0.8 - 13.0] 0.2 e Pr e 2 Re Pr(f/2) Nu ) , 1 + xf/2[13.8(Pr)0.71 - 13.0] 2.0 e Pr e 100 Nu ) 0.058xf/2Re Pr0.34, 100 < Pr < 10000

Nu )

Nu )

resulting equations for mass and heattransfer coefficients

0 e y+ e y+ 1 + + y+ 1 e y e y2 + y2 e y+ e R+

0.00090y+3 (1 + 0.0067y+2)0.5

0 e y+ e 20



+ ) by+3 y+ y+ + ) 1 - + -1 2.5 R + ) 0.07R+ R ) tube radius

+ )

1 - a(y ) - b(y ) a ) 4.16 × 10-4 b ) 15.15 × 10-6

+ )



+ ) 1 +

Ind. Eng. Chem. Res., Vol. 43, No. 11, 2004 2819

2820 Ind. Eng. Chem. Res., Vol. 43, No. 11, 2004

Figure 1. Comparison of the predictions of Nusselt number for Re ) 10 000 using different approaches: (1) Reynolds,15 (2) Prandtl,17 (3) von Karman,18 (4) Colburn,89 (5) Deissler,22 (6) Wasan and Wilke,24 (7) Notter and Sleicher,25 (8) Friend and Metzner,23 (9) Edward and Smith,27 (10) Kays.8

to have the best overall predictive ability for heat transfer in turbulent pipe flow. The success of the Lai and So model was attributed to its success in predicting flow parameters such as mean axial velocity, turbulent kinetic energy, and energy dissipation rate. Further, they recommended using Prt variation near the wall to improve the predictive ability of this model. “Reynolds-stress” or “second-moment” turbulence models do not rely on the turbulent viscosity concept, but instead incorporate transport equations for second-order velocity and temperature correlations. Launder39 provides a comprehensive discussion of these models. The complexity of Reynolds-stress models led to the development of truncated forms known as “algebraic stress models” (ASMs). Despite some successes, it is still believed that the most reliable prediction methods are those based on a second-moment closure. The reason is that the turbulent interactions that generate the Reynolds stresses and heat fluxes can be treated with less empiricism. Moreover, for those processes that cannot be so handled, a more rational and systematic set of approximations can be derived. Although much progress has been achieved in recent years in the modeling of the Reynolds-stress transport equations, the modeling of the scalar field, on the other hand, is still rather primitive. This is because the development of the model for the heat flux depends largely on the availability and correctness of turbulent stresses predicted by the Reynolds-stress model. Turbulent heat-transfer process models using high-Reynolds-number second-moment closures have been developed by meteorological fluid dynamicists.40-42 On the other hand, applications of similar second-moment turbulence closures to engineering heat-transfer problems have been attempted by Baughn et al.43 and Launder and Samaraweera,44 among others. Prud’homme and Elghobashi45 proposed a second-order closure for momentum and an algebraic stress model for heat transfer. In all such secondmoment closure models, the principal hurdle has been the shortage of reliable and relatively accurate nearwall heat flux measurements, which has contributed to the slow development of the subject of near-wall turbulence modeling for heat fluxes. Recently, the model was extended by Yoo and So46 to calculate isothermal, variable-density flows in a suddenexpansion pipe. In their approach, the flow and turbu-

lence fields were resolved by a low-Reynolds-number second-moment closure. The scalar flux equation was closed by high-Reynolds-number models, and the nearwall scalar fluxes were evaluated assuming a constant turbulent Schmidt number. This is one way to handle the scalar flux-transport equations for the near-wall flow, even though the approach is known to be quite inappropriate for most turbulent heat- and masstransfer problems of engineering interest (Launder,47 Yoo and So46). The reason for this appears to be that, so far, no suitable near-wall second-moment closure for scalar flux transport has been developed. This is due, in part, to a lack of detailed near-wall scalar flux measurements and, also in part, to the unavailability of an asymptotically correct near-wall Reynolds-stress model. Lai and So48 developed a near-wall Reynoldsstress turbulence model that can correctly predict the anisotropy of the turbulent normal stresses. The success of that model motivated Lai and So6 to extend the momentum approach to model turbulent heat transport near a wall. The modeling approach was similar to that outlined in Lai and So48 and was based on the limiting wall behavior of the heat flux-transport equations. In this way, the modeled equation was valid all the way to the wall and the assumptions of a temperature wall function and a constant turbulent Prandtl number were not required. It should be noted, however, that detailed and accurate experimental documentation of wall turbulent flow is presently not available and the modeling of the dissipation rate of temperature variance is quite immature, as even the high-Reynolds-number version of the modeled equation is not well developed. Attempts to evaluate the various models have been published in the literature. In one such attempt, Martinuzzi and Pollard49 compared six different turbulence models (two low-Reynolds-number k- models and four algebraic stress models) and found that, for fully developed pipe flow, the low-Reynolds-number k- model provided the best predictions. In a similar attempt Pollard and Martinuzzi50 compared seven different turbulence models (two-low-Reynolds-number models and five Reynolds-stress models) and found that the low-Reynolds-number k- model provided predictions that were generally in closer accord with various experimental data. Thakre and Joshi3,51 found that the Lai and So k- model and the Reynolds-stress model gave overall good agreement with the experimental data for the cases of three Prandtl numbers. A comparative analysis between the k- and Reynolds-stress models for the prediction of the mean temperature and the Nusselt number showed the applicability of k- model for heat transfer to be relatively superior. Even though the Reynoldsstress models neglect the assumptions of isotropy and the turbulent Prandtl number, their applicability for heat transfer needs reconsideration. This is mainly because of the speculation that the heat flux transport is more complicated than momentum transport and is more likely to be influenced by two or more time scales instead of one. A second reason might inappropriate near-wall modeling of the dissipation and pressure scrambling terms. The overall discrepancy observed in both the k- and Reynolds-stress models for heat transfer can be attributed to our limited understanding of the dissipation term and also the lack of detailed near-wall temperature and scalar flux measurements at higher Prandtl numbers.

Ind. Eng. Chem. Res., Vol. 43, No. 11, 2004 2821

The first application of a large eddy simulation (LES) was made by Deardorff,52 who simulated turbulent channel flow at an indefinitely large Reynolds number. In this pioneering work, Deardorff showed that threedimensional computation of turbulence (at least for simple flows) is feasible. He did not fully resolve the turbulence but used a subgrid-scale (SGS) model to describe the behavior of the smallest scales. This use of subgrid-scale models made it possible to simulate turbulent channel flow at very high Reynolds number, despite the low resolution. Using only 6720 grid points, Deardorff was able to predict several features of turbulent channel flow with a fair amount of success. Of particular significance was the demonstration of the potential of LES for use in basic studies of turbulence. Following Deardorff’s work, Schumann53,54 also calculated turbulent channel flow and extended the method to cylindrical geometries (annuli). He developed a finitedifference method for simulations of turbulent flows in plane channels and annular pipe flows. His results agreed rather well with the experimental values. It should be noted that Schumann used up to 10 times more grid points than Deardorff, as well as an improved subgrid-scale model. In addition to dividing SGS stresses into a locally isotropic part and an inhomogeneous part, he employed a separate partial differential equation for the SGS turbulent kinetic energy. However, the added differential equation did not improve the results over the calculations in which only an eddy viscosity model was used (Schumann54). Grotzbach and Schumann55 extended their channel flow calculations to account for temperature fluctuations and heat transfer. Later extensions by Grotzbach included calculations of secondary flows in partly roughened channels, inclusion of buoyancy effects, and liquid metal flows in plane channels and annuli. Moin and Kim56 used LES for the simulation of channel flow at a Reynolds number of 13 800, but unlike Schumann, they did not model the wall-layer dynamics. Instead, they extended the calculations down to the wall. They resolved the wall layer in their LES of channel flow. Their results were found to be in agreement with experiments and have been used as input data for numerous studies of organized structure in wall-bounded turbulent flows. The arrival of direct numerical simulation (DNS) has shed some light on detailed flow structures in the nearwall region. Further, the detailed energy budgets derived from DNS data provide a route toward the modeling of wall turbulence. Orszag and Kells57 performed the first direct numerical simulation of channel flow. They performed a study of the nonlinear stages of the transition from laminar to turbulent channel flow. Orszag and Patera58 simulated turbulent flow in a channel by using the Orszag and Kells57 algorithm with the fully explicit Adams-Bashforth scheme to time advance the nonlinear terms. The Orszag and Kells57 algorithm was found to be satisfactory as long as it was used for high-Reynolds-number flows, for which the behavior at the wall is not critical. Marcus59 developed a modification of the Orszag and Kells57 algorithm that incorporates viscous effects into the pressure model and satisfies continuity at the walls. Marcus59 used his algorithm to simulate Taylor-Couette flow (where, for this problem, the behavior at the wall is critical to the entire flow field) and obtained excellent agreement with experimental measurements of wavy Taylor vortex flow.

Kim, Moin, and Moser60 applied DNS to investigate fully developed turbulent flow between two parallel plates. At a Reynolds number of 3300 using nearly 4 × 106 grid points, they computed various statistical parameters for the flow field. The general characteristics of the turbulence statistics showed good agreement with the experimental results of Ecjelmann61 and Kreplin and Eckelmann,62 except for some flow quantities in the wall layer. Recently, using same code, a similar DNS was performed at a larger Reynolds number of 7900. Together with the results of the previous DNS of Kim, Moin, and Moser,60 Antonia et al.63 focused on lowReynolds-number effects in the inner region of the turbulent flow. The DNS and experimental results reported in their paper agreed well and indeed showed significant low-Reynolds-number effects. In addition to these two computations, several other direct simulations of wall-bounded turbulent flows have been reported in the literature. For instance, some of these include studies of plane channel flow by Lyons et al.,64 developing turbulent boundary layer on a flat plate by Spalart,65 turbulent flow in a rotating channel by Kristoffersen and Andersson,66 and turbulent flow through a square duct by Gavrilakis.67 All of these computations have one commonality in that they considered geometries with either a rectangular cross section or a flat plate. Unger et al.68 used a second-order-accurate finite-difference method for the DNS of fully developed turbulent pipe flow, with Reynolds numbers up to 5300. They obtained good agreement with experiments by Westerweel et al.69 for the second-order statistics and fair agreement for higher-order statistics. Zhang et al.70 used a spectral method in their DNS of pipe flow with Reynolds numbers up to 4000. Their code uses Fourier transforms in the axial and azimuthal directions and spectral elements with Jacobi polynomials in the radial direction. Eggels et al.71 carried out a DNS to study fully developed turbulent pipe flow at a Reynolds number Re ) 7000. They found that the mean velocity profile in the pipe fails to conform to the accepted law of the wall, in contrast to the channel flow. This result confirms earlier observations reported in the literature. Many attempts have been made to apply DNS for fully developed turbulent channel flow because of its simple geometry and fundamental nature to understand the convective heat transfer between fluid and a solid wall. The first attempt in this area was made by Kim and Moin72 for Pr ) 0.1, 0.71, and 2.0 with Ret ) 180. Later, Lyons et al.64 carried out a similar DNS for Pr ) 1.0 with a lower Reynolds number of Re* ) 150. The two walls of their channel were kept at different temperatures. Kasagi et al.73 and Kasagi and Ohtsubo74 carried out DNS for Pr ) 0.71 and 0.025 with a slightly lower Reynolds number of Re* ) 150. They obtained the aggregate of the transport equations for the temperature variance, turbulent heat fluxes, and their dissipations. Papavassiliou and Hanratty75 carried out a DNS of channel flow for the velocity field and used a Lagrangian method to describe turbulent transport of the scalar field. They used tracking of heat markers that are released instantaneously from a point source on a wall boundary in a numerically simulated turbulent channel flow. This methodology was used to describe the transfer of heat. The calculated fully developed temperature profile was compared with a direct numerical simulation. Further, they pointed out that DNS solutions of

2822 Ind. Eng. Chem. Res., Vol. 43, No. 11, 2004 Table 2. Previous Work: CFD Approach researcher(s)


Reynolds number(s)



Jones and Launder38



5 × 105

low-Re k-

Martinuzzi and Pollard49 Pollard and Martinuzzi50 Lai and So6


∼120 × 50

10 000-380 000

RSM, k-


∼120 × 50

10 000-380 000

ASM, k-



21 000-71 000

RSM, low-Re k-





k-, k-ω

Sarkar and So36

channel and Couette

nonuniform, 110

1300 and 3000

k-, k-τ, k-ω

Thakre and Joshi3,4,51


nonuniform, ∼120

7000-50 000

k-, RSM








65 536



Grotzbach and Schumann55 Moin and Kim56


65 576

250 000



516 096

13 800


Kim et al.60


3 962 880



Rai and Moin


1 392 640



Lyons et al.64


1 064 960





3 145 728



Zhang et al.70




Papavassiliou and Hanratty75


1 064 960


Orlandi and Fatica90

rotating pipe

nonuniform, 786 432


DNS, Lagrangian method for heat transfer DNS

Eulerian heat balance equations are not possible at high Pr given the present status of computers and that the use of a Lagrangian method can overcome this difficulty. Kawamura et al.76 performed DNS for a wider range of Prandtl numbers from Pr ) 0.025 to Pr ) 5.0 with Re* ) 180. Recently, Wikstrom and Johansson77 performed a DNS with the higher Reynolds number value of Re*

remarks first to apply low-Reynolds-number approach; suggested damping function and constants compared five turbulence models; k- model found to give good predictions compared seven turbulence models; k- model found to give good predictions asymptotically correct near-wall Reynolds-stress closure and k- closure proposed; also verified the notion that Prt is not constant near a wall; if Prt is assumed to be constant, the results obtained are at variance with measurements compared eight low-Re k- and k-ω models for high-Reynolds-number, incompressible, turbulent boundary layers with favorable, zero, and adverse pressure gradients; results obtained underscore the k- model’s unsuitability for such flows compared 10 near-wall models; found k- models that are asymptotically consistent perform better than k-τ and k-ω models compared 12 k- models and 2 RSM, applied energy balance criterion, k- model found to give good predictions first application of LES to turbulent bounded flows with coarse grid used and improved the subgrid-scale model; employed a separate partial differential equation for SGS turbulent kinetic energy, but this did not improve results over the calculations used SGS model and extended for SGS temperature transport resolved near-wall region instead of using earlier artificial boundary conditions to account for effect of near-wall dynamics resolved all essential turbulence scales on the computational grid and used no subgrid model compared influence of a finite-difference vs spectral approach on the statistical results used pseudospectral technique to solve the equation without use of the subgridscale modeling and extended calculations to heat transfer found that the mean velocity profile in the pipe fails to conform to the accepted law of the wall, in contrast to channel flow found that the mean velocity profile in the pipe fails to conform to the accepted law of the wall, in contrast to channel flow; confirms earlier observations reported in the literature found that the use of Lagrangian methods can overcome the difficulty of solving the heat balance equation for high Pr by only use of DNS simulations performed by a finitedifference scheme, second-order accurate in space and time; when pipe rotates, a degree of drag reduction is achieved in the simulation just as in the experiments

) 265 with the different thermal boundary conditions of a uniform temperature difference. The details of all of these methods are provided in Table 2. The results of such direct numerical simulations, which appear to be convergent and in good agreement with experimental measurements, are yet limited to two fully developed flows, namely, flow between parallel

Ind. Eng. Chem. Res., Vol. 43, No. 11, 2004 2823

plates and flow along a flat plate, and, in both instances, to a fairly low range of fully turbulent motion. Also, the results of DNS are in the form of discrete numerical values and therefore provide no direct functional insight. Because of these limitations and because of the truly great computational requirements, this methodology is not yet of direct utility for design and control. 3. Mathematical Model For steady, isothermal, incompressible, fully developed turbulent pipe flow, the set of governing equations for the flow and heat are given as follows:

Momentum equation 0)

1 d du 1 dp r(ν + νT) r dr dr F dz c


] ( )


Transport equation for the turbulent kinetic energy (k) 0)

[ ( ) ] ()

νT dk du 1 d r ν+ - νT r dr σk dr dr




Transport equation for the turbulent energy dissipation rate () 0)

{ [( ) ]}

νT d 1 d r ν+ r dr σ dr


( )

C1f1νT du k dr



C2f22 + E (3) k


k2 νT ) Cµfµ 


fµ ) 1 - exp(-0.0115y+)



( )] [ ( ) ] [ ( )] [ ( ) ]{( ) ( )] ( )}

f1 ) 1 + 1 - 0.6 exp f2 ) 1 -

( ) [

 dxk E ) 2νC2f2 k dr


Re 104

exp -

Ret 2 exp 9 64

Ret 64





Ret 2 7  C -2 × 64 9 2 k 2 1 2νν 2 dxk  - 2ν (8) dr 2k y + exp -

All of the low-Re models adopt a damping function, fµ, to account for the direct effect of molecular viscosity on the shear stress near the wall (viscous sublayer and buffer zone). It should be noted that the wall functions used in connection with the standard k- model (fµ ) 1) are usually applied in a region y+ > 30. Further, in the low-Reynolds-number k- model, the function f2 is introduced primarily to incorporate the low-Reynoldsnumber effect in the destruction term of . The important criterion for the function f2 is that it should force the dissipation term in the  equation to vanish at the wall. For high-Reynolds-number flows far from the wall, the function f1 asymptotically approaches the value of 1 in accordance with the high-Reynolds-number form of the model. The k- model parameters are Cµ ) 0.09, C1 ) 1.35, C2 ) 1.8, σk ) 1, and σ ) 1.3.

The following thermal-energy equation defines the heat-transfer problem


[ (

) ]

νT/ν dT dT 1 d 1 ) rν + dx r dr Pr Prt dr


We consider heat transfer in a pipe with constant heat flux through the wall. Equation 9 was normalized, resulting in the following form: r+ ) r/R, u+ ) u/uτ, T + ) T/T*, where T* ) q/CpFuτ and ν+ ) ν/uτR. Before an attempt was made to analyze the available data at high Prandtl number, it was thought desirable to examine the behavior of the momentum eddy diffusivity at points very close to the wall, i.e., at values of y+ < 5. At high Prandtl number, this is precisely the region in which much of the temperature profile resides. This can be understood by examining the term parentheses in eq 9, namely, 1/Pr + (νT/ν)/Prt. Even though the eddy diffusivity ratio, νT/ν, is very small in this region, the molecular conduction term, 1/Pr, can become of the same order of magnitude or even smaller. Thus, turbulent heat transport (and hence Prt) can be important even though turbulent momentum transport is negligible. The reason for the concern for the variation of νT/ν near the wall is that it is virtually impossible to make experimental measurements in this region. Therefore, the values of Prt in this region must be inferred by indirect methods, e.g., by making complete boundary layer calculations using an eddy diffusivity model, and this cannot be done without accurate data on νT/ν. The Lai and So model provides a convenient way to evaluate νT/ν and permits fairly accurate solution of the momentum equation, i.e., velocity profile. However, the solution to the momentum equation is totally insensitive to values of νT/ν for y+ < 5, quite unlike the situation for the energy equation at high Prandtl number. Consequently, it is necessary to examine νT/ν more carefully in this region before trying to use the low-Reynoldsnumber model for the energy equation at high Prandtl number. 3.1. Turbulent Prandtl Number. In the past, a large number of formulations for Prt appeared in the literature. Most of these formulations were inspired by the observation in the 1950s that the experimentally determined heat-transfer coefficients for the flow of liquid metal in pipes seemed to fall well below what would be predicted using models such as the Reynolds analogy. It was first suggested by Jenkins78 that this was a result of the relatively high thermal conductivity at very low-Prandtl-number fluids, and Jenkins78 proposed a model based on the idea that a turbulent “eddy” could lose heat by simple conduction during its motion normal to the mean flow direction, thereby reducing the heat transferred by the turbulent exchange process. Most of the other analyses are based on variations of the same idea, but virtually all contain free constants that need to be evaluated through comparisons with the available experimental data. Three of them can be cited as representative. Experimental values of Prt in the turbulent core for Pr g 0.7 were correlated by Jischa and Rieke31 with expressions such as

Prt ) 0.85 +

0.015 Pr


Over the purported range of validity of the above equation, Prt varies only from 0.87 to 0.85. An expres-

2824 Ind. Eng. Chem. Res., Vol. 43, No. 11, 2004

tions for liquid metals. Kays8 found that most experimental values of Prt in the turbulent core could be represented satisfactorily with the empirical equation

Prt )

Figure 2. Performance comparison of the Yakhot equation (eq 12) with the Kays8 equation and the DNS data of Bell,80 Kasagi et al.,81 and Kim and Moin56 as a plot of turbulent Prandtl number versus turbulent Peclet number and the available experimental data on turbulent Prandtl number (s, eq 12; - - -, Prt ) 0.7/Pet + 0.85)

sion with a broader range is that of Notter and Sleicher25

Prt )



1 + 90Pr3/2(µt/µ)1/4


10 [0.025Pr(µt/µ) + 90Pr3/2(µt/µ)1/4] 35 + (µt/µ) (11)

The equation is in reasonable accord with eq 10 and with the computed values of Lyons et al.64 for Pr ) 1. However, the methodology was not designed to predict a sharp increase in Prt for large Pr and small µt/µ, in accord with the recently computed values of Papassiliou and Hanratty.79 A more comprehensive model was that of Yakhot et al.10 They presented an analytical solution based on the “renormalization group method” that is apparently devoid of empirical input. The relatively simple equation given by Yakhot et al.10 is worth examining




) (

1 - 1.1793 Preff 1 - 1.1793 Pr






1 + 2.1793 Preff 1 + 2.1793 Pr





1 (1 + νT/ν)

(1 + νT/ν) νT/ν 1 + Prt Pr

Nu )



Equation 12 covers a very wide range of Prandtl numbers, and at high Pr and high values of νT/ν, it converges to Prt ) 0.85. In this equation, Prt appears as a function of Pr and νT/ν. In Figure 2, eq 12 is shown graphically as Prt versus the product (νT/ν)Pr (i.e., Pet). At high values of (νT/ν)Pr, the curve approaches 0.85. Further, for Pr near 1.00 and higher, there is only slight variation with (νT/ν)Pr, which is, of course, consistent with the observations from the experimental data. At very low values of (νT/ν)Pr, the turbulent Prandtl number becomes very high, consistent with the observa-

2Re* Pr T+


This equation was used for numerical predictions of the Nusselt number using the k- model. It was thought desirable to establish the overall energy balance. The total (viscous and turbulent) energy dissipation rate can be calculated from the following equation

exp ) (12)


This equation is similar in form to an equation suggested by Reynolds,7 but in that case, Prt is an effective average for the entire boundary layer. This result then suggests that Prt is close to a unique function of (νt/ν)Pr. In the present work, eq 12 is used for the expression of Prt. Various experimental data have been analyzed and are plotted in Figure 2, which covers the entire Prandtl number range available from experiments. Some of the experiments were performed with fully developed flow in pipes, and others are from experiments on external flat-plate boundary layers with no pressure gradients. The data in the central portion of the graph are primarily the results of experiments with air; the data at low values of (νT/ν)Pr are largely for liquid metals; and the data for (νT/ν)Pr > 100 are for water and higher-Prandtl-number liquids. Note that there appears to be a continuum from the liquid metals to the high-Prandtl-number fluids and that all of the data tend toward about Prt ) 0.85 at high values of (νT/ ν)Pr. To solve eq 9, a model for Prt has to be specified. In the present work, we employ the Yakhot correlation for turbulent Prandtl number. The theory leading to relation 12 determines the turbulent diffusivity in terms of the molecular viscosity and the turbulent viscosity. In particular, it describes the interaction between molecular and turbulent transport, an effect of much significance at low Re and Pr values. Thus, the determination of turbulent heat transfer from eq 12 requires reliable data on turbulent viscosity. The expression for the normalized heat-transfer coefficient (Nusselt number) was obtained as follows


Preff )

0.7 + 0.85 Pet

∆P × volumetric flow rate mass of liquid in pipe


Normalizing the above equation ( by uτ3/R and u by uτ), one obtains

exp+ ) fu+3


where f, the friction factor, is expressed as f ) 0.046(Re)-0.2. Re is the Reynolds number based on the diameter and mean velocity of flow in the pipe. The predicted energy dissipation rate consists of viscous and turbulent energy dissipation rates + + ) turbulent + + predicted mean



Ind. Eng. Chem. Res., Vol. 43, No. 11, 2004 2825

( ) ( ) ∂u+ ∂r+

+ + mean ) ν

+ turbulent )ν


∂ui ∂ui ∂xj ∂xj


A comparison between the experimental and the predicted dissipation rates is presented in Table 3. 3.2. Boundary Conditions. Because the flow is axisymmetric, only the boundary conditions at the wall and the symmetry line need to be specified

r ) 0: r ) R:

du dk dT ) ) )0 dr dr dr

u ) k ) 0, q ) -κ

dT , dr w ) ν(d2k/dy2)w (19)

Equations 1-8 along with the boundary conditions (eqs 19) were solved. 3.3. Method of Solution. The solution procedure consisted of two steps: The first step was to solve the momentum equations to obtain the mean axial velocity, turbulent kinetic energy (k), and turbulent energy dissipation rate (). Because the momentum and heat equations present a set of coupled equations, the flow information obtained from the first step was used in the second step, which was to obtain the mean temperature profile. Thus, the solution of the coupled momentumheat model necessitates the solution of four ordinary differential equations for the k- model. The governing equations for the k- model are ordinary differential equations and therefore can be solved by any iteration scheme for split boundary-value problems. A finite-volume technique proposed by Patankar88 was used for the solution of the four coupled nonlinear equations for the k- models. The integration was carried out from center to wall. Grid generation is one of the important aspects of the numerical simulation. The robustness of any numerical code depends on the effectiveness and stability of the grid-generation scheme employed for the investigations. In the present work, a nonuniform grid (in the range of 70-175 grid points) was employed, with more than half of the points in the range r/R > 0.9. The predictions obtained from this technique were found to be insensitive to the grids, as doubling the number of grid points changed the solution profiles by less than 1%. Although the influence of grid density on flow is expected, the degree of sensitivity is more for heat transfer. This is especially true for the case of high Prandtl numbers, for which the thickness of the thermal boundary becomes smaller than that of Table 3. Numerical Simulations and the Energy Balance Calculations no. of no. of grid points iterations Reynolds required convergence required energy total + number(s) (range) criteria (range) input (eq 18) 7 400 9 800 22 000 22 500 103 000 149 000 202 000

75-85 75-85 75-85 75-85 85-175 85-175 85-175

10-4-10-5 10-4-10-5 10-4-10-5 10-4-10-5 10-4-10-5 10-4-10-5 10-4-10-5

150-250 150-250 150-250 150-250 150-250 150-250 150-250

31.96 32.88 35.61 36.18 41.60 43.16 44.49

28.83 30.08 34.39 35.03 41.53 42.97 43.54

Figure 3. Comparison of mean velocity profiles with the experimental data of Durst et al.5 (1) Universal velocity profile; (2) ERSR model, Fortuin et al.;16 (3) Yu et al.;30 (4) CFD for Reynolds number ) 7442.

Figure 4. Comparison of the shear stress profiles with the experimental data of Eggles,71 the DNS, Churchill,28 and the present CFD model.

the viscous sublayer. Hence, the heat transfer becomes confined to a very small region near the wall. To effectively capture the steep temperature gradients in this region, we selected suitably fine grids. The numerical details are given in Table 3. 4. Results and Discussion 4.1. Comparison of the Numerical Predictions with the Experimental Data. To test the accuracy of predictions as a result of flow simulation, an extensive comparison was made with the experimental data of Durst et al.5 A comparison of the predicted mean axial velocity (Re ) 7442) using the models of Yu et al.30 and Fortuin et al.16 and the present CFD model is shown in Figure 3. It can be seen that there is excellent agreement between the predictions of Yu et al.,30 the CFD model, and the experimental data of Durst et al.5 for the region 0.5 < y+ < 100. The model of Fortuin et al.16 can be seen to show some differences in both the nearwall and core regions. Churchill28 devised a very accurate and general correlating equation for the Reynolds stresses based on asymptotic solutions, experimental data, and results obtained by the DNS solution. Figure 4 shows a comparison between the Churchill28 correlation, the present CFD predictions, the DNS results, and the experimental

2826 Ind. Eng. Chem. Res., Vol. 43, No. 11, 2004

Figure 5. Comparison of the predicted Nusselt number with the experimental data of Skupinski et al.12 for Pr ) 0.0153 [4, experimental data; s, (1) Lai and So model with eq 12, (2) Lai and So model with Prt ) 0.85, (3) Yu et al.30].

data of Eggles.71 In the bulk region (r/R > 0.8), the predictions of all three models are very close to each other and agree with the experimental data. In the region 0.08 < r/R < 0.23, the DNS predictions can be seen to be more accurate. However, very close to the wall, the CFD, Churchill,28 and DNS predictions can be seen to be essentially the same. To validate the low-Reynolds-number k- model for heat transfer, fully developed pipe flow temperature measurements with constant-wall-heat-flux boundary conditions were chosen. Unlike the measurements of flow quantities, such as the mean velocity, accurate experimental data on the temperature profiles are scarce. Moreover, there are very few cases in which such measurements are reported for high Prandtl numbers. In the present work, the low-Prandtl-number experimental data of Skupinski et al.12 for liquid NaK (Pr ) 0.0153) and of Fuchs13 for liquid Na (Pr ) 0.007) and the high-Prandtl-number experimental data of Monin and Yaglom11 (1 < Pr < 106) were used so as to cover a wide range. 4.2. Heat Transfer at Low Prandtl Number. The prediction of turbulent heat transfer in the low-Prandtlnumber range is a crucial test for the model. This is because of the strong influence of the molecular Prandtl number on the value of Prt for fluids with very low Prandtl numbers (liquid metals). Figure 5 shows the comparison between a CFD predictions and the experimental data of Skupinski et al.12 for NaK, (Pr ) 0.0153). The simulations were also carried out using a constant Prt value of 0.85. Figure 6 shows a comparison between experimental data of Fuchs13 for Pr ) 0.007 and the CFD model using eq 12 at constant Prt ()0.85). As can be seen from Figure 6, the agreement between the predicted Nusselt number and the experimental data is good. The inclusion of the Prt correlation (eq 12) can be seen to improve the prediction of the Nusselt number. The use of constant Prt (0.85) decreases the predicted Nusselt number. The same observations were reported by Kays.8 Further, from Figures 5 and 6, close agreement can also be seen between the predictions of the present CFD model and those of Churchill.28 In Figure 7, the CFD-predicted temperature profiles for liquid mercury (Pr ) 0.02), a NaK eutectic mixture (Pr ) 0.029), and air (Pr ) 0.7) are plotted for pipe flow at Re ) 1 49 000. The numerical details are given in Table 1. The agreement can be seen to be excellent.

Figure 6. Comparison of the predicted Nusselt number with the experimental data of Fuchs13 for Pr ) 0.007 (2, experimental data; s, (1) Lai and So model with eq 12, (2) Lai and So model with Prt ) 0.85, (3) Yu et al.30].

Figure 7. Comparison of the mean axial temperature predictions of the k- model with the experimental data of Buhr et al.84

4.3. Heat Transfer for High Prandtl Number. At very high Prandtl numbers, the temperature profiles move closer to the wall and almost entirely inside the range of y+ ) 5. However, the flow is still very much governed by a turbulent exchange process as it has already been seen that the eddy diffusivity for heat, (νT/ ν)/Prt, though small, is still substantially greater than the molecular diffusivity for heat, 1/Pr. Additionally, the distance from the wall influences the value of the turbulent Prandtl number, which tends to increase Prt close to the wall. This increase in Prt near the wall is especially important for high-Prandtl-number fluids because of the very thin thermal boundary layer. Outside this thin layer near the wall, the turbulent Prandtl number seems to be constant for Pr > 1. Different model results of the temperature profile for Pr ) 95 are illustrated in Figure 8, together with the experimental data of Kader.14 The present model and the Yu et al.30 predictions are in agreement with the experimental data in the near-wall region. The ERSR model (Fortuin et al.16) overpredicts the experimental data in both the near-wall and core regions. The predictions from all of the models compare well with the experimental data up to y+ < 1, the difference widening with increasing wall distance. Figure 9 depicts the comparison between the CFD predictions and the experimental data of Monin and Yaglom11 (1 < Pr <

Ind. Eng. Chem. Res., Vol. 43, No. 11, 2004 2827

Figure 8. Comparison of the mean axial temperature predictions of the k- model with the experimental data of Kader14 for Pr ) 95.

CP ) specific heat at constant pressure, kJ‚kg-1‚°C-1 D ) term contained in the k equation (dp/dz)c ) constant axial pressure gradient E ) term contained in the  equation f ) friction factor fµ, f1, f2 ) damping functions used in the low-Reynoldsnumber k- and Reynolds-stress models k ) turbulent kinetic energy, m2/s2 k+ ) normalized turbulent kinetic energy, k/uτ2 q ) constant wall heat flux, W/m2 r ) radial component R ) radius of pipe, m T ) mean axial temperature, °C T* ) temperature based on friction velocity, q/CPDuτ, °C u ) axial velocity, m/s ub ) bulk mean axial velocity of the fluid, m/s uτ ) friction velocity, x-R(dp/dz)c/(2F), m/s p ) pressure drop, N/m2 y ) normal distance from the wall, R - r, m z ) axial coordinate, m Greek Symbols  ) turbulent energy dissipation rate, m2/s3 w ) turbulent energy dissipation rate at the wall, m2/s3 µ ) molecular viscosity of the fluid, kg‚m-1‚s-1 µt ) turbulent viscosity of the fluid, kg‚m-1‚s-1 F ) density of the liquid, kg/m3 ν ) molecular kinematic viscosity of the liquid, m2‚s-1 νT ) eddy viscosity of the liquid, m2/s νH ) eddy diffusivity for heat, m2/s σk ) turbulent Prandtl number for k σ ) turbulent Prandtl number for  Dimensionless Numbers

Figure 9. Comparison for the plot of the Nusselt number Nu as a function of Prandtl number Pr with the experimental data of Monin and Yaglom.11

106). It can be seen that the predictions are in good agreement. 5. Conclusion (1) The low-Reynolds-number k- model has been used to predict the liquid velocities and turbulent viscosity. The agreement between the predictions and the experimental profiles of axial velocity, kinetic energy, and eddy diffusivity was found to be excellent. The model also establishes a good energy balance. (2) The low-Reynolds-number model can be used effectively for the prediction of the turbulent heat transfer throughout the entire range of experimentally accessible Prandtl numbers using the Yakhot analysis for the turbulent Prandtl number. The model compares well with the experimental data of Monin and Yaglom11 over Prandtl number range of 1 < Pr < 105. (3) The agreement between the predicted Nusselt number and the experimental data of Skupinski et al.12 for Pr ) 0.0153 and Fuchs13 for Pr ) 0.007 was also found to be excellent. In addition, the predicted temperature profiles also showed good agreement with the experimental data and the predictions of Yu et al.30 near the wall. Notation CFD ) computational fluid dynamics, Cµ, C1, C2 ) turbulence parameters in the k- model

Pr ) Prandtl number; Pr ) Cpµ/k Prt ) turbulent Prandtl number (eq 12) Pet ) Peclet number, Pet ) (νt/ν)Pr Ret ) turbulent Reynolds number, Ret ) k2/ν Nu ) Nusselt number, Nu ) 2Rh/k Rey ) turbulent Reynolds number based on y, Rey ) xky/ν Re ) Reynolds number based on the mean velocity; Re ) 2Ru/ν Re* ) Reynolds number based on the friction velocity, Re* ) Ruτ/ν T + ) normalized mean axial temperature; T + ) T/T* y+ ) dimensionless wall distance, y+ ) yuτ/ν u+ ) normalized mean axial fluid velocity, u+ ) u/uτ

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Received for review November 3, 2003 Revised manuscript received March 11, 2004 Accepted March 17, 2004 IE0342311

CFD Simulation of Heat Transfer in Turbulent Pipe Flow

radial variation of axial velocity, the turbulent kinetic energy, and the eddy diffusivity (compared with near- wall experimental data of Durst et al.5). The fourth.

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