This study investigates the contributions of different mechanisms of heat transfer in two phase flow boiling heat transfer coefficient for propane (natural refrigerant) in a small channel. The effects of heat flux and mass flux towards maximizing the heat transfer is studied by comparing experimental data from 7.6 mm diameter horizontal channel and outcomes from optimization using multi-objective genetic algorithm (MOGA). The latter is utilized as a tool for a fast prediction of the heat transfer coefficient, investigating the conditions for optimized mass flux, heat flux and vapor quality that can simultaneously generate maximized heat transfer due to nucleate boiling as well as forced convection. A swift prediction of the performance of "new" refrigerants can save time and money in our efforts towards a greener environment. Two correlations are investigated to predict the heat transfer coefficient. In the low vapor quality region, the optimized outcomes based on established correlations follow the trend of the experimental data. The optimized predicted heat transfer coefficient due to nucleate boiling contribution decreases with increasing quality while that due to forced convection increases. Discrepancies still exist between correlations since they include/exclude terms that are believed to be contributing factors in the overall heat transfer coefficient. The capability of MOGA exhibited here contributes towards a faster prediction of the heat transfer pattern and trend which is useful in the investigations of the performance of future "new" refrigerants as well as newly developed correlations under different flow and experimental conditions.
|Number of pages||6|
|Publication status||Published - 1 Jan 2017|
|Event||8th International Conference on Applied Energy, ICAE 2016 - Beijing, China|
Duration: 8 Oct 2016 → 11 Oct 2016
- forced convective
- heat transfer coefficient
- nucelate boiling