Optimization of R245fa Flow Boiling Heat Transfer Prediction inside Horizontal Smooth Tubes Based on the GRNN Neural Network

Abstract

An optimal prediction model for flow boiling heat transfer of refrigerant mixture R245fa inside horizontal smooth tubes is proposed based on the GRNN neural network. The main factors strongly affecting flow boiling such as mass flux rate (G), heat flux (q), quality of vapor-liquid mixture (x), evaporation temperature (Tev), and tube inner diameter (D) are used as the inputs of the model and the flow boiling heat transfer coefficient (h) as the output. Neural network model is used to optimize the prediction of flow boiling heat transfer coefficient of R245fa in horizontal light pipe through training and learning. The prediction results are in good agreement with the experimental results. For the network model of heat transfer, the average deviation is 7.59%, the absolute average deviation is 4.89%, and the root mean square deviation is 10.51%. The optimized prediction accuracy of flow boiling heat transfer coefficient is significantly improved compared with four frequently used conventional correlations. The simulation results reveal that the modeling method based on R245fa neural network is feasible to calculate the flow boiling heat transfer coefficient, and it may provide some guidelines for the optimization design of tube evaporators for R245fa.

Machine Learning-Driven Microchannel Geometry and Flow Dimensionless Parameters Optimization for Maximizing Heat Transfer

“…This study incorporated key inputs such as heat flux, mass flux, steam properties, and system pressures obtained from the existing literature. Likewise, Liang et al [16] applied the Generalized Regression Neural Network (GRNN) to explore the impact of parameters like mass flux rate, heat flux, vaporliquid mixture quality, evaporation temperature, and tube inner diameter on heat transfer coefficients. Their findings demonstrated the superior predictive capabilities of the GRNN model, with an average deviation of 7.59%, an absolute average deviation of 4.89%, and a root mean square deviation of 10.51% compared to traditional empirical correlations.…”

Corrigendum to “Optimization of R245fa Flow Boiling Heat Transfer Prediction inside Horizontal Smooth Tubes Based on the GRNN Neural Network”

“…In the article titled "Optimization of R245fa Flow Boiling Heat Transfer Prediction inside Horizontal Smooth Tubes Based on the GRNN Neural Network," [1], the authors detected some errors in the content of the article where the last sentence in Section 4.2, "Although the prediction error of the RBF network in the last section is increased, the GRNN network model has better generalization after training. The GRNN network forecast curve is shown in Figure 4" should be updated to "So, the GRNN network model has good generalization after training.…”