Multi-objective optimization of condensation heat transfer using teaching–learning-based optimization algorithm
Multi-objective optimization of condensation heat transfer using teaching–learning-based optimization algorithm
In this paper, the multi-objective optimization of R-245fa vapour condensation inside horizontal tube has been carried out using teaching–learning-based optimization algorithm. The teaching–learning-based optimization algorithm is teaching–learning procedure motivated and works on the impact of a teacher on the outcome of students in a class. Heat transfer coefficient and pressure drop with two parameters have been considered to evaluate the performance of the tube. The mass flux and vapour quality of refrigerant are taken as the parameters. The limit of mass flux and vapour quality are from 100 to 300 kg/m2 s and 0.1 to 0.8, respectively. The optimum values of heat transfer coefficient 2820.5 W/m2 K and pressure drop 1360.2 Pa are obtained with mass flux 137.65 kg/m2 s and vapour quality 0.77 using teaching–learning-based optimization algorithm.
Document information
Multi-objective optimization of condensation heat transfer using teaching–learning-based optimization algorithm
2017
Article (Journal)
English
| BKL: | 52.30 / 52.50 / 52.50 Energietechnik: Allgemeines / 52.30 Strömungskraftmaschinen, Turbomaschinen | |
| Local classification TIB: | 275/5345/5365/5500 |
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