Eventos Anais de eventos
ENCIT 2018
Brazilian Congress of Thermal Sciences and Engineering
Prediction of heat transfer coefficient during condensation of R404a in helically coiled tubes using adaptive neuro-fuzzy inference system
Submission Author:
TULIO DA MOTTA CORREA , MG , Brazil
Co-Authors:
Ali Khosravi, Juan Jose Garcia Pabon, TULIO DA MOTTA CORREA, Luiz Machado
Presenter: TULIO DA MOTTA CORREA
doi://10.26678/ABCM.ENCIT2018.CIT18-0166
Abstract
Condenser is an important heat exchanger widely used in refrigeration and air conditioning systems. Heat transfer coefficient (HTC) of two-phase flow of refrigerants is important in order to design a heat exchanger. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed to predict the HTC under condensation of R404a in a helically coiled tube. Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) are used to optimize the ANFIS model. Vapor quality, mass flux of refrigerant, pitch and curvature radius are considered as input variables of the network and HTC is selected as its output variable. The results illustrate that using GA to optimize the ANFIS model improves correlation coefficient with approximately 8% and decreases the value of RMSE around 52% for testing datasets. Also, the ANFIS model optimized with PSO algorithm reports the best performance for the predicted HTC with correlation coefficient and MAPE respectively as 0.9876 and 1.35% for the testing datasets.
Keywords
ANFIS, Particle Swarm Optimization, Genetic Algorithms, Heat transfer coefficient, R404A, Neural Network
