Numerical determination of condensation pressure drop of various refrigerants in smooth and micro-fin tubes via ANN method - Colak, Andac Batur; Celen, Ali; Dalkilic, Ahmet Selim (WALTER DE GRUYTER GMBH, 2022)
In the current work, the pressure drop of the refrigerant flow in smooth and micro-fin pipes has been modeled with artificial neural networks as one of the powerful machine learning algorithms. Experimental analyses have been evaluated in two groups for the numerical model such as operation parameters/physical properties and dimensionless numbers used in two-phase flows. Feed forward back propagation multi-layer perceptron networks have been developed evaluating the practically obtained dataset having 673 data points covering the flow of R22, R134a, R410a, R502, R507a, R32 and R125 in four different pipes. The outputs acquired from the artificial neural network have been evaluated with the target ones, and the performance factors have been estimated and the prediction accuracy of the network models has been resourced comprehensively. The results revealed that the neural networks could predict the pressure drop of the refrigerant flow in smooth and micro-fin pipes between 10% deviation bands.
Keywords: ANN; machine learning; micro-fin pipe; pressure drop; two-phase flow; SMALL-DIAMETER SMOOTH; THERMAL-CONDUCTIVITY; HEAT-TRANSFER; HYBRID NANOFLUID; PREDICTION; FLOW; EVAPORATION; MODELS; Nuclear Science & Technology