# A Weibull Distribution: Flow and Heat Transfer of Nanofluids Containing Carbon Nanotubes with Radiation and Velocity Slip Effects

Authors: Izamarlina Asshaari; Alias Jedi; Kafi Dano Pati
Journal: Mathematical Problems in Engineering
 (2020)

---

## Abstract

In this study, the Tiwari and Das model is numerically studied, in case of a moving plate containing both single-walled and multiwalled carbon nanotubes (SWCNTs and MWCNTs, respectively), in the presence of thermal radiation and the slip effect. Employing the similarity transformation, a set of 2nd-order partial differential equations (which are used to model the flow and heat transfer) are solved numerically using the boundary value problem with 4th-order accuracy (BVP4C) method. The effects of related parameters, such as the volume fraction of nanoparticles, moving, slip, and radiation parameter on the heat transfer performance are analysed and discussed. Results indicate that a unique solution was placed when the plate travels in assisting flow conditions. Additionally, as the nanoparticle volume fraction (φ) rises at φ = 0.2, the skin friction and heat transfer rate decrease. It is also observed that when the slip parameter (β) increases at β = 0.4, the skin friction decreases, whereas the heat transfer rate increases. Meanwhile, the heat transfer rate decreases when the thermal radiation (NR) increases to 0.7. Moreover, it is found that the SWCNTs are more efficient when the skin friction coefficient and the Nusselt number are considered. It is found that the Weibull distribution is more suitable in fitting the skin friction data.


---

## Body

## 1. Introduction

Originally discovered in 1995, nanofluids are a class of fluids that have been attracting significant attention of researchers in various fields. Owing to their advantages, nanofluids have been implemented in various industrial sectors, such as energy and biomedical fields. Choi and Eastman [1] reported that the thermal conductivity of nanofluids can be enhanced by dispersing nanosized particles in the fluid. Moreover, it was discovered that the flow of the base fluid improved by suspending nanoparticles in it [2]. Alwaeli et al. [3] also reported that the addition of alumina and carbon black nanoparticles improved the cooling effect on the solar panels. Meanwhile, carbon nanotubes (CNTs) have become one of the most effective materials, owing to their ability to enhance the thermal characteristics of the fluid, high electrical conductivity, unique optical transmission, and high tensile strength. They can also increase the entropy generation [4]. CNTs are rolled-up graphene sheets arranged in a cylindrical shape. They are of two types: single-walled (SWCNTs) and multiwalled (MWCNTs) [5]. It was observed that the boundary layer separation could be delayed if suction effects on the CNTs’ nanoparticle volume fraction are provided [6]. According to Naganthran et al. [7] and Ahmad et al. [8], CNTs have higher thermal conductivity. When dispersed in the base fluid, they can accelerate the rate of the heat transfer and subsequently increase the base fluid’s thermal conductivity. Using Buongiorno’s concept, Khan et al. [9] analysed the effects of the Brownian motion and thermophoresis on the CNTs’ flow, using Xue’s model. Anuar et al. [10] reported the enhanced performance of CNTs, owing to the existence of the velocity slip in boundary conditions.Nanoparticles exhibit very unique physical and mechanical properties [11]. Graphene nanoparticles, for example, are used in many applications, especially in nanotechnology [12] and more widely in cooling technology [13, 14]. As such, the study of rheological characteristics of the hybrid nanofluids is of considerable importance in order to determine the effects of two or more nanoparticles in a base fluid [15, 16]. The important outcome of such a hybrid nanoparticle is to enhance the thermal properties of nanofluids in heat transfer applications [17–20]. There are many widely used methods to enhance heat transfer. For instance, nanofluids are used to increase the heat transfer rate [21, 22]. Additionally, suitable changes in the geometrical conditions, cross-sectional area, and composition of microchannels [23, 24] can also significantly improve the heat transfer performance.In previously reported studies, the researchers focused only on studying the boundary layer flow of nanofluids, without considering the slip effect. As a result, Bhattacharyya et al. [25] explored the presence of slip effect and found that, as the value of the velocity slip parameter increased, it resulted in a decrease of the boundary layer thickness. Using this concept, Bachok at al. [26] applied this study to a moving plate, using dispersed copper nanoparticles in a base fluid. Later, Imtiaz et al. [27] analysed the effects of CNTs by employing the thermal radiation effect in the modelling systems and discovered that single-wall CNTs offer a larger heat transfer rate. Many intriguing properties of nanofluids have been discussed in the literature presented here. Recently, as scientists are showing significant interest in heat transfer properties, the utilization of nanofluids in real-life situations, in this aspect, seems promising. To achieve this, fundamental research in the mathematical aspect of the thermal conductivity of nanofluids is needed in order to understand the mechanisms that can change the nanofluid behaviour. Therefore, the objective of this study is to investigate and highlight the effects of thermal radiation and slip parameter of SWCNTs and MWCNTs in the case of a moving plate. This is performed by employing a theoretical mathematical model, previously introduced by Tiwari and Das [28]. Their model presented the effects of nanoparticle volume fraction in influencing the viscosity of the nanofluid.


## 2. Materials and Methods

### 2.1. Governing Equations

Consider a steady, two-dimensional horizontal moving plate, which is placed in a nanofluid containing SWCNTs and MWCNTs. The plate has a constant velocityUw. The temperature of the wall, Tw, is expected to remain uniform, whereas the temperature, T∞, of the ambient fluid is assumed constant. A schematic of the model is shown in Figure 1. Taking into account the thermal radiation effect, the following equations can be written (as indicated in [20, 21]):(1)∂u∂x+∂v∂y=0,(2)u∂u∂x+v∂u∂y=μnfρnf∂2u∂y2,(3)u∂T∂x+v∂T∂y=αnf∂2T∂y2−1ρCpnf∂qr∂y.Figure 1
Flow configuration of the physical phenomenon.Solving equations (1)–(3), the following conditions are derived:(4)u=Uw+ξ1∂u∂y,v=0,T=Twaty=0,u⟶U∞,T⟶T∞asy⟶∞,where u and v are the velocity elements (in x and y directions, respectively), T is the temperature in the boundary layer (in Kelvin), qr is the radiative heat flux, μ is the viscosity, and ρ and α are the density and thermal diffusivity of the nanofluid. Here, ξ1 denotes the slip factor and is defined as ξ1=ξRew+Re∞, where ξ is the primary length and Rew and Re∞ are the Reynolds numbers.It is important to have a basic understanding about the thermophysical properties of nanofluids, such as thermal diffusivity, density, dynamic viscosity, heat capacity, and thermal conductivity, before we develop a thermal system. For example, thermal conductivity is an important factor in determining the heat transfer effectiveness. Moreover, the dynamic viscosity indicates that the resistance of the fluid directly affects the pressure and decreases it, for example, in pumping power (PP) systems. Meanwhile, the effectiveness of the working fluid is determined by the heat capacity, which can be inferred from the fact how the generated heat moves away from the heat source, in the fluid. Table1 lists the thermal diffusivity, density, dynamic viscosity, heat capacity, and thermal conductivity of nanofluids. The relations given in the second column indicate that these parameters are suitable and quite important to determine the effect of CNTs on the flow and heat transfer of the system.Table 1
Parameters defined in the work [29].
ParametersGoverning equationsThermal diffusivityαnf=knf/ρCpnf, whereknf = thermal conductivity of nanofluidsρCpnf = heat capacity of nanofluidsDensityρnf=1−φρf+φρCNT, whereφ = nanoparticle volume fractionρf = density of the fluidρCNT = density of carbon nanotubesDynamic viscosityμnf=μf/1−φ2.5, whereμf = dynamic viscosity of the fluidHeat capacityρCpnf=1−φρCpf+φρCpCNT, whereρCpf = heat capacity of the fluidρCpCNT = heat capacity of carbon nanotubesThermal conductivityknf/kf=1−φ+2φkCNT/kCNT−kflnkCNT+kf/2kf/1−φ+2φkf/kCNT−kflnkCNT+kf/2kf, whereknf = thermal conductivity of nanofluidskf = thermal conductivity of the fluidkCNT = thermal conductivity of carbon nanotubesTable2 lists the physical properties of water (base fluid), SWCNTs, and MWCNTs.Table 2
The physical properties of fluids and CNTs [30].
Physical propertiesBase fluidCarbon nanotubesWaterSWCNTMWCNTρ (kg m−3)99726001600Cp (J kg−1K−1)4179425796k (W m−1K−1)0.61366003000μf (×10−3 kg/m.s)1.781Applying the Rosseland approximation [24, 25], the thermal radiation is expressed as(5)qr=−4σ∗3k∗∂T4∂y,with σ∗ and k∗ are Stefan–Boltzmann’s and average absorption’s coefficients, respectively.Omitting the higher order,T can be expressed as(6)T4≈4T∞3T−3T∞4.Equation (6) is differentiated over T, and then resubstitute the results in equation (5); therefore, equation (3) now becomes(7)u∂T∂x+v∂T∂y=αnf+16σ∗3k∗T∞3ρCpnf∂2T∂y2.To solve the set of partial equations (1)–(8) and taking into account the equations in Table 1, we applied the similarity transformation [31], which is given by the following equation:(8)η=Uνfxy,ψ=νfxUfη,θη=T−T∞Tw−T∞.Here,U represents the composite velocity U=Uw+U∞,θ the dimensionless temperature, and ψ is the stream function. Here, the terms u and v are defined as u=∂ψ/∂y and v=−∂ψ/∂x, which satisfy continuity equation (1). This leads us to(9)11−φ2.51−φ+φρCNT/ρff‴+12ff″=0,(10)1Prknf/kf1−φ+φρCpCNT/ρCpf1+43NRθ″+12fθ′=0,with the radiation parameter NR=4σ∗T∞3/k∗k and Prandtl number Pr=νf/αf. In accordance with equations (9) and (10), converted equation (4) can be represented as(11)f0=0,f′0=γ+βf″0,θ0=1,f′η⟶1−γ,θη⟶0asη⟶∞,where the velocity ratio parameters, γ and β, are expressed as(12)γ=UwU,β=ξUνf.It is necessary to identify the skin friction coefficientCf and the local Nusselt number Nux, which are expressed as(13)Cf=τwρfU2,Nux=xqwkfTw−T∞,where τw and qw are defined as(14)τw=μnf∂u∂yy=0,qw=−knf∂T∂yy=0+qry=0.To derive an expression for equations (13) and (14), equation (8) is used. So, we can write(15)CfRex1/2=11−φ2.5f″0,(16)NuxRex−1/2=−knfkf+43NRθ′0,where Rex=Ux/νf is the local Reynolds number.


### 2.2. Anderson–Darling Test Statistic

The data for the reduced skin friction were further analysed using the Anderson–Darling (AD) statistic. The goodness of fit is determined to verify which distribution in the skin friction data fits well. The AD test was selected over other tests because of two reasons: (1) the AD test is one of the best goodness-of-fit tests for a small size of the sample, and (2) the AD test is, most often, used in practice. To check which distribution in the data would follow, we need to apply the hypothesis tests that will be discussed later in this paper. The null hypothesis states that the data would follow a specific population as stated in Table3. However, the alternative hypothesis states that the data would not follow a specific population.Table 3
Distribution test forf″0.
DistributionCumulative distribution function (CDF),G(xi)WeibullGxi=1−e−xi/δβ, where xi, δ, and β stand for the skin friction data, scale, and shape parameters, respectively.ExponentialGxi=1−e−θxi, where xi and θ stand for the skin friction data and the scale parameter, respectively.GammaGxi=γStacδ,xi/β/Γδ, where Γ(δ) is the gamma function, xi, δ, and β stand for the skin friction, scale, and shape parameters, respectively. γStacδ,xi/β is the lower incomplete gamma function.First, the test statistic AD∗ must be computed based on AD, but to conclude which best fits the data, the AD∗ test statistic must be significantly lower than the others. Hence, we conclude that the skin friction data were drawn from a specific population. The AD∗ test statistic is illustrated in Table 4, and the formula to compute the AD is given as follows:(17)AD=−n−1n∑i=1n2i−1lnGxi+ln1−Gxn−i+1,where n is the sample size, i runs from 1 to n (calculated when the skin friction data are sorted in the ascending order), and F(xi) is a CDF for the specified distribution function.Table 4
Anderson–Darling test statistic.
DistributionAD∗ valueWeibullAD1+0.2/nExponentialAD1+0.6/nGammaAD1+0.6/n


## 2.1. Governing Equations

Consider a steady, two-dimensional horizontal moving plate, which is placed in a nanofluid containing SWCNTs and MWCNTs. The plate has a constant velocityUw. The temperature of the wall, Tw, is expected to remain uniform, whereas the temperature, T∞, of the ambient fluid is assumed constant. A schematic of the model is shown in Figure 1. Taking into account the thermal radiation effect, the following equations can be written (as indicated in [20, 21]):(1)∂u∂x+∂v∂y=0,(2)u∂u∂x+v∂u∂y=μnfρnf∂2u∂y2,(3)u∂T∂x+v∂T∂y=αnf∂2T∂y2−1ρCpnf∂qr∂y.Figure 1
Flow configuration of the physical phenomenon.Solving equations (1)–(3), the following conditions are derived:(4)u=Uw+ξ1∂u∂y,v=0,T=Twaty=0,u⟶U∞,T⟶T∞asy⟶∞,where u and v are the velocity elements (in x and y directions, respectively), T is the temperature in the boundary layer (in Kelvin), qr is the radiative heat flux, μ is the viscosity, and ρ and α are the density and thermal diffusivity of the nanofluid. Here, ξ1 denotes the slip factor and is defined as ξ1=ξRew+Re∞, where ξ is the primary length and Rew and Re∞ are the Reynolds numbers.It is important to have a basic understanding about the thermophysical properties of nanofluids, such as thermal diffusivity, density, dynamic viscosity, heat capacity, and thermal conductivity, before we develop a thermal system. For example, thermal conductivity is an important factor in determining the heat transfer effectiveness. Moreover, the dynamic viscosity indicates that the resistance of the fluid directly affects the pressure and decreases it, for example, in pumping power (PP) systems. Meanwhile, the effectiveness of the working fluid is determined by the heat capacity, which can be inferred from the fact how the generated heat moves away from the heat source, in the fluid. Table1 lists the thermal diffusivity, density, dynamic viscosity, heat capacity, and thermal conductivity of nanofluids. The relations given in the second column indicate that these parameters are suitable and quite important to determine the effect of CNTs on the flow and heat transfer of the system.Table 1
Parameters defined in the work [29].
ParametersGoverning equationsThermal diffusivityαnf=knf/ρCpnf, whereknf = thermal conductivity of nanofluidsρCpnf = heat capacity of nanofluidsDensityρnf=1−φρf+φρCNT, whereφ = nanoparticle volume fractionρf = density of the fluidρCNT = density of carbon nanotubesDynamic viscosityμnf=μf/1−φ2.5, whereμf = dynamic viscosity of the fluidHeat capacityρCpnf=1−φρCpf+φρCpCNT, whereρCpf = heat capacity of the fluidρCpCNT = heat capacity of carbon nanotubesThermal conductivityknf/kf=1−φ+2φkCNT/kCNT−kflnkCNT+kf/2kf/1−φ+2φkf/kCNT−kflnkCNT+kf/2kf, whereknf = thermal conductivity of nanofluidskf = thermal conductivity of the fluidkCNT = thermal conductivity of carbon nanotubesTable2 lists the physical properties of water (base fluid), SWCNTs, and MWCNTs.Table 2
The physical properties of fluids and CNTs [30].
Physical propertiesBase fluidCarbon nanotubesWaterSWCNTMWCNTρ (kg m−3)99726001600Cp (J kg−1K−1)4179425796k (W m−1K−1)0.61366003000μf (×10−3 kg/m.s)1.781Applying the Rosseland approximation [24, 25], the thermal radiation is expressed as(5)qr=−4σ∗3k∗∂T4∂y,with σ∗ and k∗ are Stefan–Boltzmann’s and average absorption’s coefficients, respectively.Omitting the higher order,T can be expressed as(6)T4≈4T∞3T−3T∞4.Equation (6) is differentiated over T, and then resubstitute the results in equation (5); therefore, equation (3) now becomes(7)u∂T∂x+v∂T∂y=αnf+16σ∗3k∗T∞3ρCpnf∂2T∂y2.To solve the set of partial equations (1)–(8) and taking into account the equations in Table 1, we applied the similarity transformation [31], which is given by the following equation:(8)η=Uνfxy,ψ=νfxUfη,θη=T−T∞Tw−T∞.Here,U represents the composite velocity U=Uw+U∞,θ the dimensionless temperature, and ψ is the stream function. Here, the terms u and v are defined as u=∂ψ/∂y and v=−∂ψ/∂x, which satisfy continuity equation (1). This leads us to(9)11−φ2.51−φ+φρCNT/ρff‴+12ff″=0,(10)1Prknf/kf1−φ+φρCpCNT/ρCpf1+43NRθ″+12fθ′=0,with the radiation parameter NR=4σ∗T∞3/k∗k and Prandtl number Pr=νf/αf. In accordance with equations (9) and (10), converted equation (4) can be represented as(11)f0=0,f′0=γ+βf″0,θ0=1,f′η⟶1−γ,θη⟶0asη⟶∞,where the velocity ratio parameters, γ and β, are expressed as(12)γ=UwU,β=ξUνf.It is necessary to identify the skin friction coefficientCf and the local Nusselt number Nux, which are expressed as(13)Cf=τwρfU2,Nux=xqwkfTw−T∞,where τw and qw are defined as(14)τw=μnf∂u∂yy=0,qw=−knf∂T∂yy=0+qry=0.To derive an expression for equations (13) and (14), equation (8) is used. So, we can write(15)CfRex1/2=11−φ2.5f″0,(16)NuxRex−1/2=−knfkf+43NRθ′0,where Rex=Ux/νf is the local Reynolds number.


## 2.2. Anderson–Darling Test Statistic

The data for the reduced skin friction were further analysed using the Anderson–Darling (AD) statistic. The goodness of fit is determined to verify which distribution in the skin friction data fits well. The AD test was selected over other tests because of two reasons: (1) the AD test is one of the best goodness-of-fit tests for a small size of the sample, and (2) the AD test is, most often, used in practice. To check which distribution in the data would follow, we need to apply the hypothesis tests that will be discussed later in this paper. The null hypothesis states that the data would follow a specific population as stated in Table3. However, the alternative hypothesis states that the data would not follow a specific population.Table 3
Distribution test forf″0.
DistributionCumulative distribution function (CDF),G(xi)WeibullGxi=1−e−xi/δβ, where xi, δ, and β stand for the skin friction data, scale, and shape parameters, respectively.ExponentialGxi=1−e−θxi, where xi and θ stand for the skin friction data and the scale parameter, respectively.GammaGxi=γStacδ,xi/β/Γδ, where Γ(δ) is the gamma function, xi, δ, and β stand for the skin friction, scale, and shape parameters, respectively. γStacδ,xi/β is the lower incomplete gamma function.First, the test statistic AD∗ must be computed based on AD, but to conclude which best fits the data, the AD∗ test statistic must be significantly lower than the others. Hence, we conclude that the skin friction data were drawn from a specific population. The AD∗ test statistic is illustrated in Table 4, and the formula to compute the AD is given as follows:(17)AD=−n−1n∑i=1n2i−1lnGxi+ln1−Gxn−i+1,where n is the sample size, i runs from 1 to n (calculated when the skin friction data are sorted in the ascending order), and F(xi) is a CDF for the specified distribution function.Table 4
Anderson–Darling test statistic.
DistributionAD∗ valueWeibullAD1+0.2/nExponentialAD1+0.6/nGammaAD1+0.6/n


## 3. Results and Discussion

The obtained results are analysed in order to demonstrate the CNTs’ flow in the influences ofNR and β. With the help of the bvp4c solver in software MATLAB, the system of nonlinear ordinary equations (9) and (10) and conditions given in equation (11) are numerically solved. The solver bvp4c was programmed with a finite difference code that implements the 3-stage Lobatto IIIa formula. This effective solver required the users to have a set of initial guesses with a combination of the boundary layer thickness. Results are obtained when the boundary conditions are asymptotically fulfilled, and no errors are produced in MATLAB. The use of bvp4c codes solved the transformed momentum and energy equations (9) and (10), respectively, and boundary conditions given in equation (11), thus validating the numerical result. The obtained values for the present results and the data obtained from previously reported studies [10, 32] are included in Table 5 for comparison. As evident, the present result and the data in the literature are found to be in agreement.Table 5
Data off′0 for water-SWCNT with the absence of NR and β.
ΦΛBachok et al. [32]Anuar et al. [10]Present resultFirstSecondFirstSecondFirstSecond0−0.30.43390.03670.43390.03670.43390.0367−0.20.41240.01140.41240.01140.41240.0114−0.10.37740.0010.37740.00110.37740.001100.33210.33210.33210.5000.000010.44380.4438−0.44370.1−0.30.40980.03450.40980.0347−0.20.38950.01070.38950.0108−0.10.35640.0010.35640.000800.31360.31360.500.000010.4191−0.41910.2−0.30.37740.03070.37740.0319−0.20.35870.00990.35870.0099−0.10.328200.32820.000100.28880.28880.500.000010.3861−0.3860Figures2 and 3 elucidate the water-SWCNTs’ variations, for different values of φ, from 0 to 0.2, when β = 0.2 and NR = 0.1. As it is seen, the dual solutions occur when the plate shifts to opposite directions γc<γ<0, while an exact solution occurs when the plate moves in assisting flow conditions γ≥0. However, no solution exists when γ<γc. As seen, the increase in φ results in decreased f″0 values. In Figure 2, it is evident that the effect of slip parameter and thermal radiation as well as CNTs added to the nanofluid made it more viscous. Hence, increasing φ will increase the fluid viscosity, thereby increasing the fluid’s resistance to flow. Figure 3 indicates that the heat transfer rate decreases with increasing aided volume fraction of nanoparticles. The effect of radiation parameter transports heat to functional flow, and hence, the squeezed nanofluid flow temperature decreases.Figure 2
Distinction ofφ on f″0.Figure 3
Distinction ofφ on −θ′0.The variation ofCfRex1/2 with different ranges of φ and β is presented in Figures 4 and 5 as the plate travels in assisting flow. As highlighted in Figure 4, if the value of β increased in both SWCNTs and MWCNTs, the skin friction coefficient CfRex1/2 begins to depreciate. However, in Figure 5, NuxRex−1/2 increases with the increase of φ. As seen, larger values of CfRex1/2 and NuxRex−1/2 are seen by SWCNTs due to their larger density and thermal conductivity. These are illustrated in Figures 4 and 5. High values of heat transfer rate and fluid friction factor for SWCNTs (compared with MWCNTs) are due to thermal boundary resistance between the CNT and the surrounding fluid. SWCNTs are highly hydrophobic because the particles are bounded together due to higher van der Waals force of attraction.Figure 4
Distinction ofCfRex1/2 with φ and β for water-based nanofluid.Figure 5
Distinction ofNuxRex−1/2 with φ and β for water-based nanofluid.The SWCNTs’ flow and heat transfer are further verified by studying the effects of difference in ranges ofφ on the velocity and temperature profiles. Figures 6 and 7 graphically illustrate the convergence series solution of the velocity and temperature profiles. From the figures, it is noted that these profiles asymptotically suffice the converted conditions in equation (11). The dual solutions can also be spotted from these figures. Moreover, the exact solutions have a boundary layer thickness thinner than that of the dual solutions. It also indicates that f′ is a decreasing function of φ, owing to an increase in φ. However, in Figure 7, with the presence of NR = 0.1 and β = 0.2, the thermal boundary layer thickness increases, which would decelerate the heat transfer.Figure 6
Effect of various values ofφ on the velocity profile for water-based SWCNTs.Figure 7
Effect of various values ofφ on the temperature profile for β = 0.2, γ = −0.5, and NR = 0.1.Figures8 and 9 portray the effect of β on the velocity and temperature profiles. These figures indicate that, as the value of β increases, the momentum thickness and thermal boundary layer thickness decrease. The influence of various values of thermal radiation on the temperature profile for water-based SWCNTs is also presented in Figure 10. It can be seen quite evidently that an increase in the value of NR leads to a higher boundary layer thickness.Figure 8
Effect of various values ofβ on the velocity profile for water-based SWCNTs.Figure 9
Effect of various values ofβ on the temperature profile for water-based SWCNTs.Figure 10
Effect of various values of thermal radiation on the temperature profile for water-based SWCNTs.Further tests are conducted using the Anderson–Darling test statistics. The skin friction data (from Figure2) are utilized to perform this test. The parameters, for each distribution that was tested, are displayed in Table 6. Table 7 shows the AD test statistics for the reduced skin friction data. Overall, the lowest AD values are obtained from the same distribution, i.e., the Weibull distribution. It implies that, as the volume fraction of nanoparticles increases, AD values still acknowledge that the Weibull distribution is the optimum one, among the other two distributions (gamma and exponential). Therefore, it can be concluded that the flow enhancement depends on low values of nanoparticle volume fraction and slip parameter. However, the higher thermal radiation will enhance the temperature at the boundary layer. The data of the Weibull distribution should concur with the data from different orders of slip; hence, it is very important to fit the data using statistical modelling.Table 6
Parameters of statistical distributions for the water-based CNT.
Φ00.10.2WeibullΔ0.365390.348540.31421Β0.086720.081750.07506GammaΑ0.069470.064930.06017Β4.538494.641354.50228ExponentialΘ0.315270.301350.27089Table 7
AD test for the reduced skin friction.
Φ00.10.2Weibull19.355719.7525317.24832Gamma29.1025631.1821130.17244Exponential41.9986646.0373248.92685


## 4. Conclusion

In the present work, a detailed analysis of the influences of slip and thermal radiation of carbon nanotubes’ (SWCNTs and MWCNTs) flow over a moving plate is conducted. From the analysis, an exact solution was found when the plate moves in the assisting flow condition, while dual solutions appeared in the opposing flow condition. It is also found that an increment in volume fraction of nanoparticles atφ = 0.2 decreased the skin friction and heat transfer rate since it influences the boundary layer thickness. At β = 0.4, the skin friction decreases, whereas the heat transfer rate increases. In contrast, as NR increases to 0.7, the heat transfer rate decreases. In addition, for all other values of β = 0, 0.2, and 0.4, it is found that the SWCNTs are more efficient, as compared with MWCNTs, with regard to the skin friction coefficient and the Nusselt number. Moreover, these results verified that the skin friction data fit the Weibull distribution, which are found to be in good agreement with results of the previous study [33]. The slip and thermal radiation are also modelled based on the flow and heat enhancement of CNTs. However, to explain the flow and heat enhancement of CNTs in studies on fluid dynamics, further research in the domain of slip is needed.



---

*Source: 9417598-2020-07-07.xml*