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Physics-informed neural network for deep learning of finned-tube evaporator performance: From the perspective of system modeling.

Author(s) : LU S-Y., CHEN E-Q., XU T-Y-H., LIANG X. Y., ZHANG C. L.

Type of article: IJR article

Summary

The finned-tube evaporator is widely used in refrigeration and heat pump systems. Accurate and efficient prediction of its performance is crucial for achieving digital twin and intelligent operation of complex systems, such as the multi-split variable refrigerant flow system. From the perspective of system modeling, neural networks for evaporator performance modeling should be able to predict the performance of energy (heat transfer rate), momentum (pressure drop), and mass (refrigerant inventory). However, existing neural networks generally fail
to fully satisfy these requirements. To address this limitation, this paper proposes a physics-informed neural network (PINN) for finned-tube evaporators from the perspective of system modeling. Distinct from conventional neural networks, the proposed PINN incorporates refrigerant inventory as an output parameter and the energy balance is integrated into the loss function as a physical constraint. Thus enhanced, the proposed PINN achieves excellent overall accuracy with low local error. The mean absolute percentage errors (MAPE) for predicting the total heat transfer rate, sensible heat transfer rate, refrigerant pressure drop, air pressure drop, and refrigerant inventory are 0.15%, 0.48%, 0.38%, 0.36%, and 0.30%, respectively. The root mean square errors (RMSE) are reduced by 42.4%, 11.2%, 18.4%, 34.1%, and 48.2%, respectively, compared to the previous neural network.

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Pages: 14 p.

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Details

  • Original title: Physics-informed neural network for deep learning of finned-tube evaporator performance: From the perspective of system modeling.
  • Record ID : 30034783
  • Languages: English
  • Subject: Technology
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 185
  • Publication date: 2026/05
  • DOI: http://dx.doi.org/10.1016/j.ijrefrig.2026.02.021

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