Model-based dimensionless neural networks for fin-and-tube condenser performance evaluation

• We developed dimensionless neural networks for fin-and-tube condensers.
• Dimensionless Pi-groups were derived from model-based dimensional analysis method.
• Three-layer perceptron neural network was served as the performance model.
• Neural networks well predicted the condenser performance with different refrigerants.

The paper presents a dimensionless neural network modeling method for the fin-and-tube refrigerant-to-air condensers which are widely used in air-cooled refrigeration and heat pump systems. The model-based dimensional analysis method is applied to develop the dimensionless Pi-groups for the condenser performance. The three-layer perceptron neural network is served as the performance model using the dimensionless Pi-groups as its inputs and outputs. Compared with a well-validated tube-by-tube first-principle model, the standard deviations of trained dimensionless neural networks are 0.66%, 4.83% and 0.11% for the heating capacity, the refrigerant pressure drop and the air pressure drop, respectively. The accuracy is also consistent with the previously developed dimensional neural networks. Furthermore, independent model validation using different refrigerants shows that the dimensionless models have good potential in predicting the condenser performance if the Pi-groups were in the range of training data.