Modeling of condensation heat transfer coefficients and flow regimes in flattened channels

Abstract

In this paper, an adaptive neuro-fuzzy inference system (ANFIS) with fuzzy C-means clustering (FCM) structure identification is proposed to model condensation heat transfer and flow regimes in flattened smooth tubes with different aspect ratios. The FCM-ANFIS model was trained by using experimental data points for six effective chosen parameters of saturation temperature, heat flux, mass flux, aspect ratio and hydraulic diameter of the flattened tube, and vapor quality. Three flow regimes of annular flow, stratified, and intermittent flow were linked to the effective parameters based on the experimental data. Two models were proposed to predict the condensation heat transfer coefficient and the flow regime of R134a and R410a in flattened smooth tubes. Three statistical criteria were used to ascertain the accurateness of the models compared to the experimental results. It is found that while among benchmarked cases, the proposed model for the condensation heat transfer coefficient performs well, the best result with the lowest error (MAE = 0.029, RMSE = 0.036 and MRE = 2.83%) is when Tsat = 45 °C, q″ = 10 kW/m2, G = 100 kg/m2. s, β = 6 and Dh = 2.3 mm. On the other hand, in the worst-performing case when the errors are MAE = 0.239, RMSE = 0.239 and MRE = 13.36%, the predicted results are still in the uncertainty range of the experimental result when Tsat = 45 °C, q″ = 5 kW/m2, G = 200 kg/m2. s, β = 6 and Dh = 2.3 mm.