# Condensation Flow of Refrigerants Inside Mini and Microchannels: A Review
## 2 Micro and Mini-Channel Condensation
### Void Fraction Models
Additionally, their expanded dataset enabled the extraction of additional parameters such as slug frequencies, vapour bubble velocity, vapour bubble dimensions, and liquid film thicknesses, offering comprehensive insights into the multiphase flow dynamics. [PERSON] and [PERSON] [44] \begin{table}
\begin{tabular}{l c} \hline \hline
**Author** & **Void Fraction Prediction Model** \\ \hline [PERSON] and [PERSON] [34] & \(\propto=\left[1+0.28\left(\frac{1-x}{x}\right)^{0.64}\left(\frac{\rho_{c}}{ \mu_{1}}\right)^{0.36}\left(\frac{\mu_{1}}{\mu_{e}}\right)^{0.07}\right]^{-1}\) (9) \\ [PERSON] [35] & \(\propto=\left[1+\frac{1-x}{x}\left(\frac{\rho_{c}}{\mu_{1}}\right)^{2/3} \right]^{-1}\) (10) \\ [PERSON] [36] & \(\propto=\left[1+\left(\frac{1-x}{x}\right)^{0.74}\left(\frac{\rho_{c}}{\mu_{ 1}}\right)^{0.65}\left(\frac{\mu_{1}}{\mu_{e}}\right)^{0.13}\right]^{-1}\) \\ [PERSON] [37] & \(\propto=\frac{x}{\rho_{c}}\left[\left(1+0.12(1-x)\right)\left(\frac{x}{\rho_{ c}}+\frac{1-x}{\rho_{c}}\right)+\frac{118(1-x)\left[\sigma\left(\rho_{c}-\rho_{c} \right)\right]^{0.25}}{\sigma_{0}\rho_{3}}\right]^{-1}\) (11) \\ [PERSON] et al. [38] & \(\propto=\left[1+0.604\left(\frac{1-\varsigma_{0}}{\kappa_{0}}\right)^{0.340} \right]^{-1}\) (12) \\ [PERSON] et. al. [39] & \(\frac{\alpha_{0}+\alpha_{1}\kappa_{0}}{\ln\left[\frac{\kappa_{0}}{\kappa_{0}} \right]}\) (13) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The existing void fraction prediction models can be used for micro/minichannel condensation flows. compared three different void fraction models in their study for modelling microchannel condensate flow. As a result of this comparison, they determined that the most compatible model with the data obtained from the study was the [PERSON] [36] model. For this reason, the [PERSON] [36] model for microchannel condensate flows is one step ahead of other void fraction models. [PERSON] et al. [46] emphasised the impact of diameter on the distribution of \(\alpha\) across the channel cross-section. They noted that for d = 15 mm and G = 150 kg/m\({}^{2}\)s, nearly all of the liquid accumulates in the condensate layer along the entire range of x, particularly evident in wavy flow patterns. Conversely, under similar conditions, the percentage of liquid confined to the condensate layer fluctuates from 6% at x = 0.86 to 91% at x = 0.13 for d = 7 mm. Consequently, in larger diameters, gravitational effects predominate over surface tension and inertia throughout the vapour quality range, effectively retaining the liquid predominantly within the condensate layer situated at the bottom of the tube. Furthermore, [PERSON] et al. [46] compared their void fraction measurements against several prediction methods, including the homogeneous model, [PERSON] [36], [PERSON] et al. [47], [PERSON] and [PERSON] [48], [PERSON] et al. [39], [PERSON] et al. [42], and their own proposed method [46]. Generally, these prediction methods exhibited reasonable agreement with the experimental data of [PERSON] et al. [46], with some exceptions. The homogeneous model tended to overpredict the experimental data, particularly at low void fractions, while the [PERSON] [36] method showed underpredictions. [PERSON] et al. [39] method provided reasonable predictions, but failed to capture the increasing trend of void fraction with rising mass flux. The methods that yielded the most accurate predictions according to [PERSON] et al. [46], were their own proposed method [46] and the one by [PERSON] et al. [42], which combines [PERSON] et al. [39] correlations for annular flow and [PERSON] [49] for wavy flow. It is important to note that the [PERSON] et al. [46] method was fine-tuned based on their own dataset, hence its superior performance with their data.