# Stochastic Analysis of Natural Convection in Vertical Channels with Random Wall Temperature

Authors: Ryoichi Chiba
Journal: Mathematical Problems in Engineering
 (2017)

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## Abstract

This study attempts to derive the statistics of temperature and velocity fields of laminar natural convection in a heated vertical channel with random wall temperature. The wall temperature is expressed as a random function with respect to time, or a random process. First, analytical solutions of the transient temperature and flow velocity fields for an arbitrary temporal variation in the channel wall temperature are obtained by the integral transform and convolution theorem. Second, the autocorrelations of the temperature and velocity are formed from the solutions, assuming a stationarity in time. The mean square values of temperature and velocity are computed under the condition that the fluctuation in the channel wall temperature can be considered as white noise or a stationary Markov process. Numerical results demonstrate that a decrease in the Prandtl number or an increase in the correlation time of the random process increases the level of mean square velocity but does not change its spatial distribution tendency, which is a bell-shaped profile with a peak at a certain horizontal distance from the channel wall. The peak position is not substantially affected by the Prandtl number or the correlation time.


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## Body

## 1. Introduction

Recently, as reliability gains increasing importance in the design phase of thermal systems, conventional deterministic heat transfer analysis alone is not sufficient; analysis that considers uncertainties included in the systems themselves and/or thermal environments (in other words, heating/cooling conditions) is required. In general, accurately predicting the thermal or mechanical loads acting on components in high-temperature apparatus is very difficult. This alludes to the fact that many uncertain factors exist in the design of such apparatus [1]. In this situation, the temperature fields in solid objects and/or working fluid should be estimated stochastically.There has been an interest in analysing and quantifying the effects of uncontrollable random factors on the fluid flow and heat transfer performances of thermal systems. To estimate the effects quantitatively, probabilistic methods have been applied to heat transfer problems. Existing literature about the application of the methods to heat conduction problems is summarised in review articles [2–4], and therefore we do not enter further into the subject here. On the other hand, focusing on the application to convective heat transfer problems, one can find early studies of stochastic forced convection with heat source and initial and boundary conditions being white noises [5, 6]. Subsequently, other researchers investigated the effects of random temperature and velocity of a moving wall on the temperature field in a Couette flow [7], the effects of boundary temperature and boundary topology modelled by random fields on the temperature and velocity fields of natural convection in a square domain [8, 9], and the effects of boundary temperature, which was assumed to be a random field, on the Nusselt numbers of mixed convection in a horizontal channel [10].However, the effects of randomly fluctuating the temperature of a bounding wall on the fluid flow and thermal performance of natural convection in channels have not been investigated. The temperature fluctuations at the wall are not necessarily zero in real situations [11], which are caused by various natural noises. In addition, the effects of wall temperature fluctuations on the evolution of the fluid flow and heat transfer are significant in internal natural convection [12].In this paper, we address the stochastic natural convection problem of viscous fluid in an infinite vertical channel with temporally random wall temperature. Under the assumption that the wall temperature is a stationary random process, the autocorrelation functions are analytically derived for the fluid temperature and flow velocity. Numerical calculations are performed under the condition that the wall temperature is expressed either as white noise or as a stationary Markov process. We quantify the effects of the Prandtl number and the correlation time of the wall temperature fluctuation on the mean square values (i.e., second moments) of the fluid temperature and velocity. This stochastic problem may also be solved numerically by applying a variance propagation algorithm based on the finite element formulation [13]. However, our analytical solutions to the second moments are mathematically sounder.


## 2. Analysis of Natural Convection Problem

Let us consider the transient laminar natural convection of a viscous incompressible fluid in an infinite vertical channel, as shown schematically in Figure1. Two flat plates having infinite width and length (i.e., doubly infinite plates) are placed vertically with a separation distance of b in the fluid. Initially, the fluid is quiescent at uniform temperature T∞, and the vertical plates are maintained at the same temperature T∞. It is assumed that the temperature Tw(t) of the plate located at y=b fluctuates randomly for time t>0 whereas the other plate is kept constant at T∞. The physical properties of the fluid are constant except for its density; density variation is considered only in the buoyancy term, which is a linear function of temperature (i.e., the usual Boussinesq approximation). Viscous dissipation effects are neglected.Figure 1
Schematic of a vertical parallel-plate channel with random wall temperature.In this physical situation, the fluid motion can be regarded as one-dimensional (1D) with only one nonzero velocity component,u, varying with y and t. Due to the 1D nature, the continuity equation is trivially satisfied. The energy equation for the fluid reduces to the 1D conduction equation. Thus, the momentum equation for the x-direction and the energy equation are given, respectively, as follows [14–16]:(1a)∂u∂t=ν∂2u∂y2+gβT-T∞,(1b)1α∂T∂t=∂2T∂y2.The imposed initial and boundary conditions are as follows:(2a)u=0att=0,(2b)T=T∞att=0,(3a)u=0aty=0,(3b)T=T∞aty=0,(4a)u=0aty=b,(4b)T=Twtaty=b.For convenience, we nondimensionalize (1a) and (1b) as follows:(5a)∂u-∂τ=∂2u-∂ξ2+θ,(5b)Pr·∂θ∂τ=∂2θ∂ξ2,and the initial and boundary conditions may be stated as(6a)u-=0atτ=0,(6b)θ=0atτ=0,(7a)u-=0atξ=0,(7b)θ=0atξ=0,(8a)u-=0atξ=1,(8b)θ=ϕτatξ=1,where(9)θ=T-T∞T∞,u-=νgb2βT∞u,ξ=yb,τ=νb2t,Pr=να,ϕτ=TwτT∞-1.By applying the integral transform [17] and convolution theorem to (5b), (6b), (7b), and (8b), we obtain the temperature solution θ(ξ,τ) as(10)θξ,τ=2πPr∑m=1∞-1m+1msin⁡mπξ∫0τϕτ-texp⁡-m2π2Prtdt.Since (10) includes the randomly fluctuating temperature ϕ(τ), the temperature field must not be estimated deterministically but statistically. Therefore, the autocorrelation Rθ for θ is derived for the statistical estimation of the temperature field and can be expressed as follows:(11)Rθξ1,ξ2,τ,τ+h=θξ1,τ·θξ2,τ+h,where ·, ξ1,ξ2, and h denote the expectation operator, a set of arbitrary coordinate values, and an arbitrary time interval, respectively. If ϕ(τ) is a stationary random process, then Rθ is obtained by substituting (10) into (11) as follows:(12)Rθξ1,ξ2,h=2πPr2∑m=1∞∑n=1∞-1m+nmnsin⁡mπξ1sin⁡nπξ2×∫0∞∫0∞Rϕh+r-sexp⁡-m2π2Prr-n2π2Prsdrds,where Rϕ denotes the autocorrelation of ϕ(τ).The natural convective flow due to the temperature field given by (10) must be estimated statistically. An analytical solution to the initial-boundary value problem for (5a), (6a), (7a), and (8a) has already been derived [18] as follows:(13)u-ξ,τ=2∑m=1∞sin⁡mπξ∫01sin⁡mπx∫0τθx,τ-texp-m2π2tdtdx.Since the flow velocity also fluctuates randomly because of the randomness of θ, the autocorrelation of the vertical velocity Ru- is derived as its statistic. As in the case of the autocorrelation for the temperature given by (11), Ru- can be expressed as(14)Ru-ξ1,ξ2,τ,τ+h=u-ξ1,τ·u-ξ2,τ+h.By substituting (13) into (14) and assuming the stationarity in time, we obtain(15)Ru-ξ1,ξ2,h=4∑m=1∞∑n=1∞sinmπξ1sinnπξ2×∫01∫01sinmπpsinnπq∫0∞∫0∞Rθp,q,h+r-sexp-m2π2r-n2π2sdrdsdpdq,where Rθ is given by (12).The power spectrum densities forθ and u- are obtained from the following Wiener–Khintchine equation [19]:(16)Siξ,ω=12π∫-∞∞Riξ,ξ,hexp⁡-jωhdh,i=θoru-,where j is an imaginary number and ω is the angular frequency.


## 3. Numerical Results and Discussion

Some special cases of (12) and (15) are of theoretical interest. We now examine the cases in which ϕ(τ) is expressed as white noise or a stationary Markov process.


### 3.1. White Noise

White noise is the most basic model for stationary random processes and is often used for numerical computation because of its ease of mathematical treatment.Rϕ is represented by Dirac’s delta function [20] as(17)Rϕh=S0δh,where S0 is a constant. In this case, the channel wall temperature includes all frequency components. In order to estimate statistically the temperature and flow velocity, we here focus attention on their mean square. The mean square is identical with the variance if ϕ=0. The mean square of temperature is obtained by setting ξ1=ξ2=ξ and h=0 in (12) and then substituting (17) into it. For the mean square of flow velocity, setting ξ1=ξ2=ξ and h=0 in (15) as well, we have(18)θ2=4S0Pr∑m=1∞∑n=1∞-1m+nmnsin⁡mπξsin⁡nπξm2+n2,u-2=4S0π4Pr+1∑m=1∞∑n=1∞-1m+nmsin⁡mπξsin⁡nπξnm2+n22Pr·m2+Pr+2n2Pr·m2+n2.The infinite series in (18) are approximated with ∞→200 to obtain four significant digits of the mean square of temperature and flow velocity (see Tables 1 and 2).Table 1
Relationship between the convergence ofθ2/S0 and the number of terms, for Pr = 0.7.
ξ
Number of terms
20 × 20
50 × 50
100 × 100
200 × 200
400 × 400
0.2
0.29127
0.29118
0.29117
0.29117
0.29117
0.4
1.2917
1.2911
1.2910
1.2910
1.2910
0.6
4.0316
4.0275
4.0269
4.0267
4.0267
0.8
18.177
18.105
18.095
18.092
18.091Table 2
Relationship between the convergence ofu-2/S0 and the number of terms, for Pr = 0.7.
ξ
Number of terms
20 × 20
50 × 50
100 × 100
200 × 200
400 × 400
0.2
2.7879⋅10−3
2.7880⋅10−3
2.7880⋅10−3
2.7880⋅10−3
2.7880⋅10−3
0.4
8.7657⋅10−3
8.7663⋅10−3
8.7664⋅10−3
8.7664⋅10−3
8.7664⋅10−3
0.6
1.2335⋅10−2
1.2337⋅10−2
1.2337⋅10−2
1.2337⋅10−2
1.2337⋅10−2
0.8
8.4599⋅10−3
8.4648⋅10−3
8.4651⋅10−3
8.4651⋅10−3
8.4651⋅10−3Figures2 and 3 show the effects of the Prandtl number on the mean square values of the temperature and velocity, respectively. From Figure 2, it is evident that the temperature fluctuations are larger for a lower Prandtl number. This tendency can be easily explained as follows: if (5b) is considered to be the 1D heat conduction equation, then the Prandtl number is equivalent to the reciprocal of thermal diffusivity, and thus low Prandtl numbers promote the transmission of the randomness of the wall temperature into the fluid in the channel. The divergence of the mean square on the heated channel wall (i.e., ξ=1) is attributed to the white noise’s feature of having an infinite variance. This divergence is also observed in the mean square temperature of solids when the boundary temperature is given by white noise [18, 21, 22]. A sharp increase in the slope of the mean square near the channel wall results from high-frequency components [22].Figure 2
Mean square distribution of temperature forϕ(τ) of white noise.Figure 3
Mean square distribution of vertical velocity forϕ(τ) of white noise.Figure3 shows that the mean square velocity has bell-shaped profiles (convex upward), and it exhibits peak values in the vicinity of ξ=0.6. The details of the peak positions are discussed later. This figure indicates that the velocity fluctuations decrease with an increase in the Prandtl number; this tendency is consistent with Figure 2.


### 3.2. Stationary Markov Process

Stationary Markov processes have an exponential autocorrelation [23]. Thus, Rϕ is expressed as(19)Rϕh=S0exp⁡-hμ,where μ is a positive constant and referred to as correlation time. In this case, the mean square of θ and u- is given by(20)θ2=4S0μ∑m=1∞∑n=1∞-1m+nmnsin⁡mπξsin⁡nπξPr-μn2π22Prm2+n2Pr+μn2π2-μπ2Pr+μm2π2,(21)u-2=4S0μ∑m=1∞∑n=1∞-1m+nmnsin⁡mπξsin⁡nπξm2+n2Pr-μn2π2Fm,n,where (22)Fm,n=2Pr2Pr·m2+Pr+2n2Pr+1n2m2+n2π4Pr+μn2π2Pr·m2+n2-μ2μπ2m2+n2+2Pr+μm2π21+μn2π21+μm2π2.Tables 3 and 4 show the convergence performance of the solutions. One can see that the Markov process makes the convergence of solutions worse than white noise. This tendency is particularly visible in the mean square of temperature. Even for ∞→3200, four significant digits are not obtained. In contrast, an approximation of ∞→50 is sufficient to obtain four significant digits in the mean square velocity. Therefore, considering the balance between computation accuracy and computation time, the infinite series in (20) and (21) are approximated with ∞→3200, leading to three significant digits of the mean square temperature.Table 3
Relationship between the convergence ofθ2/S0 and the number of terms, for Pr = 0.7 and μ=1.
ξ
Number of terms
400 × 400
800 × 800
1600 × 1600
3200 × 3200
6400 × 6400
0.2
3.7092⋅10−2
3.7138⋅10−2
3.7161⋅10−2
3.7173⋅10−2
3.7179⋅10−2
0.4
0.14852
0.14873
0.14884
0.14889
0.14892
0.6
0.33596
0.33657
0.33688
0.33703
0.33711
0.8
0.60639
0.60826
0.60920
0.60967
0.60991Table 4
Relationship between the convergence ofu-2/S0 and the number of terms, for Pr = 0.7 and μ=1.
ξ
Number of terms
20 × 20
50 × 50
100 × 100
200 × 200
400 × 400
0.2
9.0018⋅10−4
9.0025⋅10−4
9.0025⋅10−4
9.0025⋅10−4
9.0025⋅10−4
0.4
2.7630⋅10−3
2.7633⋅10−3
2.7633⋅10−3
2.7633⋅10−3
2.7633⋅10−3
0.6
3.6241⋅10−3
3.6247⋅10−3
3.6248⋅10−3
3.6248⋅10−3
3.6248⋅10−3
0.8
2.0526⋅10−3
2.0535⋅10−3
2.0536⋅10−3
2.0536⋅10−3
2.0536⋅10−3Figures4 and 5 show the mean square distributions of the temperature and flow velocity for different Prandtl numbers and correlation time, respectively. Because multiplying the Prandtl number by k is equivalent to multiplying the correlation time by 1/k in θ2 (see (20)), identical curves are obtained for different combinations of Pr and μ in Figure 4. This fact means that the spatial distribution of θ2/S0 depends only on a new parameter Pr/μ. A comparison among the results for a fixed value of μ indicates that a decrease in the Prandtl number increases the temperature fluctuation; this is the same as in the case of white noise. On the other hand, a comparison among the results for a fixed value of Pr shows that a short correlation time reduces the temperature fluctuation of the fluid. This is because a short correlation time increases the fraction of high-frequency components in the wall temperature fluctuation, which decay rapidly with increasing distance from the wall.Figure 4
Mean square distribution of temperature forϕ(τ) of a stationary Markov process.Figure 5
Mean square distribution of vertical velocity forϕ(τ) of a stationary Markov process.Figure5 demonstrates that the distribution tendency of u-2/S0 is essentially the same as in Figure 3. Interestingly, a clear difference in the mean square velocity is observed between the parameter sets for which the identical mean square distribution of temperature is obtained, for example, (Pr,μ)=(0.7,1) and (0.07, 0.1). In comparison between those parameter sets, the mean square velocity is smaller for a shorter correlation time.The peak positions of the mean square distributions of velocity are tabulated in Table5 for different Prandtl numbers. In the case of the Markov process, a decrease in the correlation time slightly increases the ξ-coordinate values of the peak position for all the Prandtl numbers. In addition, because white noise has zero correlation time [24], it yields larger ξ-coordinate values than the Markov process. However, roughly speaking, ξ≈0.6 in any of these cases.Table 5
ξ coordinate values at which the maximum mean square velocity is observed.
Pr
μ (stationary Markov process)
White noise
0.01
0.1
1
∞
0.07
0.6147
0.5875
0.5786
0.5773
0.6247
0.7
0.6069
0.5883
0.5784
0.5773
0.6114
7
0.6205
0.6093
0.5853
0.5773
0.6212


## 3.1. White Noise

White noise is the most basic model for stationary random processes and is often used for numerical computation because of its ease of mathematical treatment.Rϕ is represented by Dirac’s delta function [20] as(17)Rϕh=S0δh,where S0 is a constant. In this case, the channel wall temperature includes all frequency components. In order to estimate statistically the temperature and flow velocity, we here focus attention on their mean square. The mean square is identical with the variance if ϕ=0. The mean square of temperature is obtained by setting ξ1=ξ2=ξ and h=0 in (12) and then substituting (17) into it. For the mean square of flow velocity, setting ξ1=ξ2=ξ and h=0 in (15) as well, we have(18)θ2=4S0Pr∑m=1∞∑n=1∞-1m+nmnsin⁡mπξsin⁡nπξm2+n2,u-2=4S0π4Pr+1∑m=1∞∑n=1∞-1m+nmsin⁡mπξsin⁡nπξnm2+n22Pr·m2+Pr+2n2Pr·m2+n2.The infinite series in (18) are approximated with ∞→200 to obtain four significant digits of the mean square of temperature and flow velocity (see Tables 1 and 2).Table 1
Relationship between the convergence ofθ2/S0 and the number of terms, for Pr = 0.7.
ξ
Number of terms
20 × 20
50 × 50
100 × 100
200 × 200
400 × 400
0.2
0.29127
0.29118
0.29117
0.29117
0.29117
0.4
1.2917
1.2911
1.2910
1.2910
1.2910
0.6
4.0316
4.0275
4.0269
4.0267
4.0267
0.8
18.177
18.105
18.095
18.092
18.091Table 2
Relationship between the convergence ofu-2/S0 and the number of terms, for Pr = 0.7.
ξ
Number of terms
20 × 20
50 × 50
100 × 100
200 × 200
400 × 400
0.2
2.7879⋅10−3
2.7880⋅10−3
2.7880⋅10−3
2.7880⋅10−3
2.7880⋅10−3
0.4
8.7657⋅10−3
8.7663⋅10−3
8.7664⋅10−3
8.7664⋅10−3
8.7664⋅10−3
0.6
1.2335⋅10−2
1.2337⋅10−2
1.2337⋅10−2
1.2337⋅10−2
1.2337⋅10−2
0.8
8.4599⋅10−3
8.4648⋅10−3
8.4651⋅10−3
8.4651⋅10−3
8.4651⋅10−3Figures2 and 3 show the effects of the Prandtl number on the mean square values of the temperature and velocity, respectively. From Figure 2, it is evident that the temperature fluctuations are larger for a lower Prandtl number. This tendency can be easily explained as follows: if (5b) is considered to be the 1D heat conduction equation, then the Prandtl number is equivalent to the reciprocal of thermal diffusivity, and thus low Prandtl numbers promote the transmission of the randomness of the wall temperature into the fluid in the channel. The divergence of the mean square on the heated channel wall (i.e., ξ=1) is attributed to the white noise’s feature of having an infinite variance. This divergence is also observed in the mean square temperature of solids when the boundary temperature is given by white noise [18, 21, 22]. A sharp increase in the slope of the mean square near the channel wall results from high-frequency components [22].Figure 2
Mean square distribution of temperature forϕ(τ) of white noise.Figure 3
Mean square distribution of vertical velocity forϕ(τ) of white noise.Figure3 shows that the mean square velocity has bell-shaped profiles (convex upward), and it exhibits peak values in the vicinity of ξ=0.6. The details of the peak positions are discussed later. This figure indicates that the velocity fluctuations decrease with an increase in the Prandtl number; this tendency is consistent with Figure 2.


## 3.2. Stationary Markov Process

Stationary Markov processes have an exponential autocorrelation [23]. Thus, Rϕ is expressed as(19)Rϕh=S0exp⁡-hμ,where μ is a positive constant and referred to as correlation time. In this case, the mean square of θ and u- is given by(20)θ2=4S0μ∑m=1∞∑n=1∞-1m+nmnsin⁡mπξsin⁡nπξPr-μn2π22Prm2+n2Pr+μn2π2-μπ2Pr+μm2π2,(21)u-2=4S0μ∑m=1∞∑n=1∞-1m+nmnsin⁡mπξsin⁡nπξm2+n2Pr-μn2π2Fm,n,where (22)Fm,n=2Pr2Pr·m2+Pr+2n2Pr+1n2m2+n2π4Pr+μn2π2Pr·m2+n2-μ2μπ2m2+n2+2Pr+μm2π21+μn2π21+μm2π2.Tables 3 and 4 show the convergence performance of the solutions. One can see that the Markov process makes the convergence of solutions worse than white noise. This tendency is particularly visible in the mean square of temperature. Even for ∞→3200, four significant digits are not obtained. In contrast, an approximation of ∞→50 is sufficient to obtain four significant digits in the mean square velocity. Therefore, considering the balance between computation accuracy and computation time, the infinite series in (20) and (21) are approximated with ∞→3200, leading to three significant digits of the mean square temperature.Table 3
Relationship between the convergence ofθ2/S0 and the number of terms, for Pr = 0.7 and μ=1.
ξ
Number of terms
400 × 400
800 × 800
1600 × 1600
3200 × 3200
6400 × 6400
0.2
3.7092⋅10−2
3.7138⋅10−2
3.7161⋅10−2
3.7173⋅10−2
3.7179⋅10−2
0.4
0.14852
0.14873
0.14884
0.14889
0.14892
0.6
0.33596
0.33657
0.33688
0.33703
0.33711
0.8
0.60639
0.60826
0.60920
0.60967
0.60991Table 4
Relationship between the convergence ofu-2/S0 and the number of terms, for Pr = 0.7 and μ=1.
ξ
Number of terms
20 × 20
50 × 50
100 × 100
200 × 200
400 × 400
0.2
9.0018⋅10−4
9.0025⋅10−4
9.0025⋅10−4
9.0025⋅10−4
9.0025⋅10−4
0.4
2.7630⋅10−3
2.7633⋅10−3
2.7633⋅10−3
2.7633⋅10−3
2.7633⋅10−3
0.6
3.6241⋅10−3
3.6247⋅10−3
3.6248⋅10−3
3.6248⋅10−3
3.6248⋅10−3
0.8
2.0526⋅10−3
2.0535⋅10−3
2.0536⋅10−3
2.0536⋅10−3
2.0536⋅10−3Figures4 and 5 show the mean square distributions of the temperature and flow velocity for different Prandtl numbers and correlation time, respectively. Because multiplying the Prandtl number by k is equivalent to multiplying the correlation time by 1/k in θ2 (see (20)), identical curves are obtained for different combinations of Pr and μ in Figure 4. This fact means that the spatial distribution of θ2/S0 depends only on a new parameter Pr/μ. A comparison among the results for a fixed value of μ indicates that a decrease in the Prandtl number increases the temperature fluctuation; this is the same as in the case of white noise. On the other hand, a comparison among the results for a fixed value of Pr shows that a short correlation time reduces the temperature fluctuation of the fluid. This is because a short correlation time increases the fraction of high-frequency components in the wall temperature fluctuation, which decay rapidly with increasing distance from the wall.Figure 4
Mean square distribution of temperature forϕ(τ) of a stationary Markov process.Figure 5
Mean square distribution of vertical velocity forϕ(τ) of a stationary Markov process.Figure5 demonstrates that the distribution tendency of u-2/S0 is essentially the same as in Figure 3. Interestingly, a clear difference in the mean square velocity is observed between the parameter sets for which the identical mean square distribution of temperature is obtained, for example, (Pr,μ)=(0.7,1) and (0.07, 0.1). In comparison between those parameter sets, the mean square velocity is smaller for a shorter correlation time.The peak positions of the mean square distributions of velocity are tabulated in Table5 for different Prandtl numbers. In the case of the Markov process, a decrease in the correlation time slightly increases the ξ-coordinate values of the peak position for all the Prandtl numbers. In addition, because white noise has zero correlation time [24], it yields larger ξ-coordinate values than the Markov process. However, roughly speaking, ξ≈0.6 in any of these cases.Table 5
ξ coordinate values at which the maximum mean square velocity is observed.
Pr
μ (stationary Markov process)
White noise
0.01
0.1
1
∞
0.07
0.6147
0.5875
0.5786
0.5773
0.6247
0.7
0.6069
0.5883
0.5784
0.5773
0.6114
7
0.6205
0.6093
0.5853
0.5773
0.6212


## 4. Concluding Remarks

A stochastic natural convection analysis was presented based on the correlation theory approach. The laminar natural convection of viscous fluid was studied in an asymmetrically heated vertical channel that is composed of doubly infinite plates. Analytical solutions of the autocorrelations for the temperature and flow velocity were derived under the condition that the random fluctuation in the channel wall temperature can be modelled as a stationary random process. Numerical calculations were performed for the wall temperature given as white noise or a stationary Markov process. Numerical results demonstrated the effects of the Prandtl number and the correlation time of the random process on the mean square values of the temperature and flow velocity. The main conclusions are summarised as follows:(i)
A decrease in the Prandtl number or an increase in the correlation time of the random process increases the mean square velocity but does not change its spatial distribution tendency, which is a bell-shaped profile with a peak at a certain horizontal distance from the channel wall.(ii)
The peak position is not substantially affected by the Prandtl number or the correlation time. When measured with the dimensionless coordinate,ξ, the position is ξ ≈ 0.6, and no more than about 10% of shift in position occurs depending on the values of those parameters.



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