# A Review of Pool-Boiling Processes Based on Bubble-Dynamics Parameters
## 6 Boiling-Heat-Transfer Coefficient h and Heat Flux q
### Heat Flux \(q\)
\[q=q_{con}+q_{me}+q_{nc} \tag{61}\]  
\[q_{con}=2\sqrt{\pi\lambda_{l}\rho_{l}c_{pl}f}D_{b}^{2}N_{s} \tag{62}\]  
\[q_{me}=\rho_{v}h_{lv}N_{s}f\frac{\pi D_{b}^{3}}{6} \tag{63}\]  
\[q_{nc}=0.14\rho_{l}c_{pl}\left[\beta g\Delta T^{4}\Bigg{(}\frac{\lambda_{l}}{ \rho_{l}c_{pl}}\Bigg{)}^{2}\right]^{0.33}\
u_{l}^{-0.33} \tag{64}\]  
The proposed prediction models of [PERSON] et al. [130], [PERSON] et al. [132] and [PERSON] et al. [131] were compared with experimental data measured by [PERSON] et al. [35], and the result is shown in Figure 14a by [PERSON] et al. [120]. The comparison shows that about 80% of the experimental data obtain within 25% of the error with the predictive models proposed by [PERSON] et al. [130] and [PERSON] et al. [132], while the [PERSON] and [PERSON] [131] models exhibit a low degree of agreement with the experimental data. The [PERSON] et al. [132] model is the best fit for the working conditions at high heat flux, and further validation was carried out by [PERSON] et al. [120]. Figure 14b shows the results of the predictive correlation proposed by [PERSON] et al. in comparison with three different pool boiling heat flux experiments, i.e., the pool boiling of water on the vertical surface which was conducted by [PERSON] and [PERSON] [35], the pool boiling of R134a on the horizontal tube which was conducted by [PERSON] and [PERSON] [133], and the pool boiling of propane on the horizontal tube surface which was conducted by [PERSON] and [PERSON] [134]. As can be seen from the Figure 14b, the [PERSON] et al. [132] model has a prediction error within \(\pm\)25% from the results of the three sets of experimental data. It is well known that the relationship between the heat flux \(q\) and the heat transfer coefficient \(h\) is \(q=h\Delta T\) from [PERSON]'s law of cooling. In the prediction about the heat transfer coefficient \(h\) or the heat flux \(q\), if the wall superheat is known, it can be quickly achieved through one quantity to obtain the value of the other, and the appropriate choice should be made according to the demands in relevant experiments. Mechanistic correlations are insufficient to capture the interactions among parameters, and this can be accomplished with computer assistance [135]. In order to solve the heat transfer problem during boiling, many researchers [136; 137; 138] have attempted to solve the complex flow and heat transfer problems during boiling by means of Computational Fluid Dynamics (CFD). For the simulation of boiling processes by CFD, various bubble correlations can be used to improve the accuracy of the simulation. Although CFD simulations have been very successful and widely used in predicting single-phase flows, their effectiveness for two-phase flows has not been fully realized [139], and CFD also has difficulty in accurately describing a variety of complex structural surfaces. For unstudied boiling cases, the difficulty of CFD modeling and the degree of similarity to other conditions should be weighed to decide whether to adopt mathematical formulation models or CFD models. By reviewing various behaviors of bubbles in the boiling process, it can be found that the complexity and unpredictability of boiling mainly comes from the complex coupling relationship between the mass transfer brought by bubble nucleation as well as growth, and the energy transfer brought by thermal convection as well as phase change. Moreover, the parameters in the boiling process are changing from moment to moment, which affects and is affected by other bubble characteristic qualities.