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Comparaison entre la modélisation d’un compresseur à pistons grâce à un réseau neuronal [artificiel] et un modèle physique.

A comparison between the modeling of a reciprocating compressor using artificial neural network and physical model.

Auteurs : BELMAN-FLORES J. M., LEDESMA S., BARROSO-MALDONADO J. M., et al.

Type d'article : Article, Article de la RIF

Résumé

This article presents the development, validation, and comparison of two methods for modeling a reciprocating compressor. Initially, the physical mode is based on eight internal sub-processes that incorporate infinitesimal displacements according to the piston movement. Next, the analysis and modeling of the compressor through the application of artificial neural networks are presented. The input variables are: suction pressure, suction temperature, discharge pressure, and compressor rotation speed. The output parameters are: refrigerant mass flow rate, discharge temperature, and energy consumption. Both models are validated with experimental data for the refrigerants R1234yf and R134a; computer simulations show that mean relative errors are below ±10% with the physical model, and below ±1% when artificial neural networks are used. Additionally, the performance of the models is evaluated through the computation of the squared absolute error. Finally, these models are used to compute an energy comparison between both refrigerants.

Documents disponibles

Format PDF

Pages : 144-156

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 : A comparison between the modeling of a reciprocating compressor using artificial neural network and physical model.
  • Identifiant de la fiche : 30016287
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 59
  • Date d'édition : 11/2015

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