Convection heat-transfer data, correlation

Colburn, A.P. Trans. Am. Inst. Chem. Eng. 29 (1933) 174. A method of correlating forced convection heat transfer data and a comparison with fluid friction. 

Colburn, A. P. A Method of Correlating Forced Convection Heat Transfer Data and a Comparison with Fluid Friction, Trans. AIChE, vol. 29, p.174, 1933. 

What functional form of equation is normally used for correlation of free-convection heat-transfer data ... 

Algebraic correlations are used extensively to represent turbulent convective heat transfer data. One of the earliest correlations used for fully developed turbulent flow heat transfer is the Dittus-Boelter correlation ... 

Convection Heat Transfer. Convective heat transfer occurs when heat is transferred from a soHd surface to a moving fluid owing to the temperature difference between the soHd and fluid. Convective heat transfer depends on several factors, such as temperature difference between soHd and fluid, fluid velocity, fluid thermal conductivity, turbulence level of the moving fluid, surface roughness of the soHd surface, etc. Owing to the complex nature of convective heat transfer, experimental tests are often needed to determine the convective heat-transfer performance of a given system. Such experimental data are often presented in the form of dimensionless correlations. 

Effect of Uncertainties in Thermal Design Parameters. The parameters that are used ia the basic siting calculations of a heat exchanger iaclude heat-transfer coefficients tube dimensions, eg, tube diameter and wall thickness and physical properties, eg, thermal conductivity, density, viscosity, and specific heat. Nominal or mean values of these parameters are used ia the basic siting calculations. In reaUty, there are uncertainties ia these nominal values. For example, heat-transfer correlations from which one computes convective heat-transfer coefficients have data spreads around the mean values. Because heat-transfer tubes caimot be produced ia precise dimensions, tube wall thickness varies over a range of the mean value. In addition, the thermal conductivity of tube wall material cannot be measured exactiy, a dding to the uncertainty ia the design and performance calculations. 

The data of Fig. 20 also point out an interesting phenomenon—while the heat transfer coefficients at bed wall and bed centerline both correlate with suspension density, their correlations are quantitatively different. This strongly suggests that the cross-sectional solid concentration is an important, but not primary parameter. Dou et al. speculated that the difference may be attributed to variations in the local solid concentration across the diameter of the fast fluidized bed. They show that when the cross-sectional averaged density is modified by an empirical radial distribution to obtain local suspension densities, the heat transfer coefficient indeed than correlates as a single function with local suspension density. This is shown in Fig. 21 where the two sets of data for different radial positions now correlate as a single function with local mixture density. The conclusion is That the convective heat transfer coefficient for surfaces in a fast fluidized bed is determined primarily by the local two-phase mixture density (solid concentration) at the location of that surface, for any given type of particle. The early observed parametric effects of elevation, gas velocity, solid mass flux, and radial position are all secondary to this primary functional dependence. 

All correlations based on ambient temperature data where thermal radiation is negligible should be considered to represent only the convective heat transfer coefficient hc. 

With the above functions and empirical correlations, it becomes possible to calculate the overall convective heat transfer coefficient hc by Eqs. (16, 4, and 22-24). Figure 26 shows a plot presented by Lints and Glicksman which compares predictions by this method with experimental data from several different sources. Reasonably good agreement is obtained over a range of bed densities corresponding to approximately 0.5 to 3% volumetric solid concentration. 

Changes in convective heat transfer coefficients due to autoclave position and loading scheme are difficult to model. These coefficients are more easily correlated from experimental data. This correlation can be determined from monitoring thermocouples attached to tools or by correlating air flow based on autoclave position. These coefficients are crucial for determining the rate of heat transfer from the autoclave environment into the part. Heat transfer coefficients are also a function of autoclave pressure however, the adjustment for... 

In order to understand and correlate the heat transfer data, the relevant physical properties of the suspensions must be carefully evaluated. The experimental determination of heat capacity and density pose no particular problem. In many instances it is possible to estimate these values accurately by assuming them to be weight averages of those of the two components. In contrast, great difficulty is associated with the accurate determination of thermal conductivity and viscosity, largely owing to the fact that the solids tend to settle readily in any device where convection currents are eliminated, as they must be for these... 

The convective heat transfer coefficients hi and h0 must be calculated from equations that involve the geometry of the system, the physical properties of the fluid, and the velocity with which it is flowing. These equations are obtained variously by more or less fundamental analysis of the heat transfer and fluid flow mechanisms, or by correlation of experimental data, or by combinations of these methods. A few typical values of the film coefficients are... 

Bjorge, R. W., G. R. Hall, and W. M. Rohsenow Correlations of Forced Convection Boiling Heat Transfer Data, Int. J. Heat Mass Transfer, vol. 25, p. 753, 1982. 

The functional dependence expressed in Equations 4.29 and 4.31 governs the behavior of convective heat transfer. In some cases the functionality can be determined analytically, but in most cases it can be determined only as a statistical correlation of experimental data. Dimensionless groups are used to generalize empirical correlations for convective heat transfer. These groups can be determined from the parameters in Equations 4.29 and 4.31. They are the Reynolds, Nusselt, Grashof, and Prandtl numbers, respectively, defined as ... 

In using the fin efficiency, the seemingly precise analytical solutions actually are premised upon several assumptions of unknown validity. Therefore, the resulting numerical values must be used cautiously and conservatively. And it must be remembered that, when using convective heat-transfer correlations for finned surfaces, the experimental data underlying these correlations were reduced using computed fin efficiencies. 

FIGURE 18.20 Correlation of single-phase convection data downstream of the stagnation line. Reprinted from D. T. Vader, F. P. Incropera, and R. Viskanta, "Local Convective Heat Transfer from a Heated Surface to an Impinging Planar Jet of Water, International Journal of Heat and Mass Transfer 34, pp. 611-623, 1991, with kind permission from Elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, OX5 1GB, U.K. 

A semiempirical analysis of heat transfer to impacting sprays has been developed by considering the major components of spray heat transfer to consist of (1) contact heat transfer to impacting droplets, (2) convective heat transfer to gas, and (3) thermal radiation heat transfer [142], The model further assumes that the droplet interference is negligible (i.e., dilute sprays), and the three heat transfer components are independent of each other. The heat transfer data to a single impacting droplet have been correlated by the Weber number, surface temperature superheat, and thermophysical properties. 

Mist-cooling heat transfer characteristics have been reviewed by Nishio and Ohkubo [146], and an attempt has been made to correlate existing experimental data. The mist-cooling convective heat transfer coefficient is correlated in terms of relevant flow parameters such as droplet diameter d, droplet velocity Vd, and the volume flux of the droplets V by the empirical equation... 

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