Improvement of small channel heat transfer correlation using genetic algorithm for R290 refrigerant
The primary issues among the discussions on two-phase flow in small channels are the uncertainties about the contributions of nucleate boiling and forced convective towards the total two-phase heat transfer coefficient, the accuracy of the predicted two-phase heat transfer coefficient which remains...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/102002/1/YushazaziahMohdYunosPSKM2021.pdf.pdf |
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| Summary: | The primary issues among the discussions on two-phase flow in small channels are the uncertainties about the contributions of nucleate boiling and forced convective towards the total two-phase heat transfer coefficient, the accuracy of the predicted two-phase heat transfer coefficient which remains unsatisfactory, measured by the mean absolute error (MAE) between the correlation and experimental data, particularly that can accommodate pre-and post-dryout regions, and the limited experimental work for alternative refrigerants for the establishment of related correlations for a specific refrigerant. This thesis presents the results obtained using an optimization approach, Multi-objective Genetic Algorithm (MOGA) to show the conflicting effect of nucleate boiling and forced convective during two-phase flow of the natural refrigerant R290 in a small channel at the saturation temperature of 10°C under optimized conditions of mass flux, heat flux, channel diameter, and vapor quality. Subsequently, Single Objective Genetic Algorithm (SOGA) was utilized to improve a selected superposition two-phase heat transfer correlation for R290. Experimental data points of R290 from reported experiments in 1.0 to 6.0 mm circular diameters were used to minimize the MAE while searching for the optimum constants and coefficients in the suppression factor (??), and convective factor (??), for the pre-and the post-dryout regions. The newly optimized correlation for R290 has MAE between 17 and 34% for all case studies which involves 40% improvement from the original correlation. Validation was done against a new data set to see the applicability and limitation of the developed correlations. The proposed method is capable of obtaining a precise empirical prediction that fits well with experimental data, as an approach to further improve any existing correlations which can reduce the number of experiments and consequently minimizes associated cost involved. The improved correlation obtained in the present study provides an improved prediction of heat transfer coefficient that in turn leads to accurate design and consequently saves material, refrigerant, and cost for compact heat exchanging devices. |
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