Partager

Document IIF

Modélisation de la performance d’un condenseur à tube aileté par réseau neuronal et méthodologie de surface de réponse.

Fin-and-tube condenser performance modeling with neural network and response surface methodology.

Auteurs : LI Z. Y., SHAO L. L., ZHANG C. L.

Type d'article : Article, Article de la RIF

Résumé

This paper presents a new approach of combining response surface methodology and neural network for performance evaluation of fin-and-tube air-cooled condensers which are widely used in refrigeration, air-conditioning and heat pump systems. Box–Behnken design (BBD) and Central Composite design (CCD) are applied to collect a small dataset for neural network training, respectively. It turns out that 41 sets of data are collected for heating capacity and refrigerant pressure drop, and 9 sets of data are collected for air pressure drop. Additional 2000+?sets of data are served as the test data. Compared with the test data, for the heating capacity, the average deviation (A.D.), standard deviation (S.D.) and coefficient of determination (R2) of trained neural network are -0.43%, 0.98% and 0.9996, respectively; for the refrigerant pressure drop, those are -2.09%, 4.98% and 0.996, respectively; and for the air pressure drop, those are 0.11%, 1.96% and 0.992, respectively. Classical quadratic polynomial response surface models were also included for reference. By comparison, the developed neural networks gave much better results. Moreover, the proposed method can remarkably downsize the neural network training dataset and mitigate the over-fitting risk.

Documents disponibles

Format PDF

Pages : 124-134

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 : Fin-and-tube condenser performance modeling with neural network and response surface methodology.
  • Identifiant de la fiche : 30016284
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 59
  • Date d'édition : 11/2015

Liens


Voir d'autres articles du même numéro (27)
Voir la source

Indexation