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Machine learning based prediction of airflow maldistribution in air-to-refrigerant heat exchangers.

Author(s) : O'MALLEY B., TANCABEL J., AUTE V.

Type of article: IJR article

Summary

Flow maldistribution is a common challenge in heat exchanger (HX) design and particularly important for air-torefrigerant geometries where capacity losses can approach 65%. This has a major impact on central air conditioning systems, as compact duct design motivates the use of A-type HXs which are known to be affected by airflow maldistribution. Because velocity profiles are difficult to predict, components are often oversized leading to increased material cost, system footprint, and refrigerant charge. Several studies detail airflow maldistribution for individual HXs and packages, but findings cannot always be extrapolated to new designs. In this work, a machine learning (ML) based flow profile prediction framework is developed and applied to two common package configurations: (i) A-type and (ii) U-type HXs, across a broad range of HX geometries and flow rates. Porous media CFD simulations are validated against independent data for both package types as well as comprehensive in house measurements for a finless geometry with shape optimized non-round tubes, which validates the framework for new heat transfer surfaces. The ML models are trained on the porous media CFD simulations, predicting volumetric flow rate (VFR) within 1.1% and 1.9% with maximum relative L2 norm errors of 0.48 and 0.65, respectively, while also delivering 105 speed up factor compared to full porous media CFD. HX level simulations show an up to 9% reduction in heat transfer from flow maldistribution, with greater losses occurring at smaller half apex angles. This framework enables rapid and highly accurate prediction of airflow maldistribution induced capacity degradation.

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  • Original title: Machine learning based prediction of airflow maldistribution in air-to-refrigerant heat exchangers.
  • Record ID : 30035275
  • Languages: English
  • Subject: Technology
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 189
  • Publication date: 2026/09
  • DOI: http://dx.doi.org/https://doi.org/10.1016/j.ijrefrig.2026.107023

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