Correlation of Two-Phase Pressure Drop of the R1234yf in Smooth Horizontal Tubes: An Artificial Intelligence Approach

Ali Khosravi, Juan Garcia, Juan Garcia, Marcos Felipe Machado Silva , Mamdouh El Haj Assad, Mohammad Malekan

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

High global warming potential (GWP) refrigerants that have been used as working fluids in industrial and residential applications are alleged to pose a threat to environmental security. R134a refrigerant is the most widely used refrigerant medium temperature with GWP of 1430, which therefore must be replaced. One substitute is the R1234yf refrigerant with a GWP lower than 1. In this study, pressure drop during evaporation of R1234yf refrigerant in smooth horizontal tubes is investigated by experimental study and developing intelligent methods. Calculation of the pressure drop for two-phase flow in evaporation and condensation processes needs many experimental tests. Therefore, in this investigation, machine learning algorithms (MLAs) are employed to predict the pressure drop during evaporation of R1234yf refrigerant. Three methods of MLAs are developed to predict the target. These methods are adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized with particle swarm optimization (ANFIS-PSO), and ANFIS optimized with genetic algorithm (ANFIS-GA). The intelligent models are constructed based on tube diameter (3.2, 4.8, 6.4, and 8 mm); saturation pressure, mass velocity and quality of vapor as input variables and the pressure drop is selected to be the target. The results demonstrated that increasing the mass velocity increases the pressure gradient. Also, for the larger value of tube diameter, the pressure drop has lower values. The behavior of two-phase pressure drop was accurately predicted by the ANFIS-PSO model. Moreover, the PSO algorithm for increasing the performance prediction of the ANFIS model performs better than the GA.
Original languageEnglish
Title of host publication18th Brazilian Congress of Thermal Sciences and Engineering
Publication statusPublished - 16 Nov 2020
MoE publication typeA4 Article in a conference publication
EventBrazilian Congress of Thermal Sciences and Engineering - Virtual, Online, Brazil
Duration: 16 Nov 202020 Nov 2020
Conference number: 18

Conference

ConferenceBrazilian Congress of Thermal Sciences and Engineering
CountryBrazil
CityVirtual, Online
Period16/11/202020/11/2020

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