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ISSN Online: 2377-424X

ISBN Print: 978-1-56700-474-8

ISBN Online: 978-1-56700-473-1

International Heat Transfer Conference 16
August, 10-15, 2018, Beijing, China

CONDENSATION HEAT TRANSFER COEFFICIENT AND PRESSURE DROP OF R134a IN A TUBE: MODELING AND OPTIMIZATION

Get access (open in a dialog) DOI: 10.1615/IHTC16.cod.023147
pages 2203-2209

摘要

In this study, the condensation heat transfer coefficient and pressure drop of R134a were modeled by applying the adaptive neuro-fuzzy inference system (ANFIS) approach in an inclined tube. An input-output experimental dataset and grid partitioning (GP) structure identification method were used for ANFIS training. Two models were proposed for the condensation heat transfer coefficient and pressure drop of R134a based on the effective parameters of the mass flux (G), saturation temperature (Tsat), vapor quality (x), and inclination angle (β). Moreover, the non-dominated sorting genetic algorithm II (NSGA-II) multiobjective optimization method was applied to the proposed ANFIS models for the condensation heat transfer coefficient and pressure drop to find possible optimum points. The optimization results were shown in a Pareto front.