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Document IIF

Étude de la prédiction de fuite de frigorigène à l’aide de l’apprentissage automatique.

Investigation of refrigerant leakage behavior prediction using machine learning.

Numéro : 1207

Auteurs : YOKONO R., OHAMA H., KAMADA M., HORI K.

Résumé

This study explores the construction of a refrigerant leakage behavior prediction model with a sufficient level of accuracy for screening high-risk conditions and a light computational load using machine learning. Conventional prediction methods require labor-intensive CAD generation and computationally intensive CFD analysis performed by skilled operators. As a first step, this study attempted to reduce the training load by updating a DNN model trained on a large amount of CFD results for R32 with transfer learning using a small amount of new refrigerant CFD results. A transfer learning model with the input layer updated for R1234yf was able to capture the trends of the CFD results, while a similar model for R290 did not yield satisfactory results, which is estimated to be due to significantly different physical properties related to the output compared to those of the base model.

Documents disponibles

Format PDF

Pages : 8 p.

Disponible

  • Prix public

    20 €

  • Prix membre*

    Gratuit

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Détails

  • Titre original : Investigation of refrigerant leakage behavior prediction using machine learning.
  • Identifiant de la fiche : 30032720
  • Langues : Anglais
  • Source : 16th IIR-Gustav Lorentzen Conference on Natural Refrigerants (GL2024). Proceedings. University of Maryland, College Park, Maryland, USA, August 11-14 2024
  • Date d'édition : 08/2024
  • DOI : http://dx.doi.org/10.18462/iir.gl2024.1207

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