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Prédiction par apprentissage automatique de la mauvaise distribution de l’écoulement de l’air dans les échangeurs de chaleur de type A.

Machine learning based prediction of airflow maldistribution in A-type heat exchangers.

Numéro : 2269

Auteurs : O'MALLEY B., TANCABEL J., AUTE V.

Résumé

Airflow maldistribution is one of the primary causes of performance degradation in air-to refrigerant heat exchangers (HX) and has been shown to decrease heat transfer by as much as 35%. As a result, many units are oversized to meet the target capacity, resulting in increased system cost and refrigerant charge. Several studies have explored how characteristics like package type and HX geometry impact the flow profile, but results are restricted to a limited range of parameters and cannot be extrapolated to new designs. In this work, a machine learning (ML) model is trained to predict the inlet flow profile of dry air entering A-type HXs across a broad range of geometries and conditions. Flow profiles are generated using a porous media CFD model and used to train an Artificial Neural Network (ANN) which exhibits maximum and average relative L2 norm errors of 0.48 and 0.05. Additionally, these predictions take less than a second to generate resulting in a speed up factor of 2.42E5 compared to CFD. Component-level simulations are conducted to determine the performance degradation resulting from the predicted airflow maldistribution profiles. The new ML model will enable rapid and accurate prediction of performance degradation resulting from airflow maldistribution in A-type HXs, allowing for more accurate and cost-effective HX design.

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Pages : 10 p.

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Détails

  • Titre original : Machine learning based prediction of airflow maldistribution in A-type heat exchangers.
  • Identifiant de la fiche : 30033103
  • Langues : Anglais
  • Sujet : Technologie
  • Source : 2024 Purdue Conferences. 20th International Refrigeration and Air-Conditioning Conference at Purdue.
  • Date d'édition : 17/07/2024

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