Document IIF

Réseaux neuronaux adimensionnels basés sur des modèles pour l'évaluation de la performance d'un condenseur à tubes à ailettes.

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

Auteurs : YANG L., LI Z. Y., SHAO L. L., et al.

Type d'article : Article, Article de la RIF

Résumé

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.

Documents disponibles

Format PDF

Pages : 1-9

Disponible

  • Prix public

    20 €

  • Prix membre*

    Gratuit

* meilleur tarif applicable selon le type d'adhésion (voir le détail des avantages des adhésions individuelles et collectives)

Détails

  • Titre original : Model-based dimensionless neural networks for fin-and-tube condenser performance evaluation.
  • Identifiant de la fiche : 30012590
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 48
  • Date d'édition : 12/2014

Liens


Voir d'autres articles du même numéro (23)
Voir la source