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Débit massique du frigorigène à l'intérieur de tubes capillaires adiabatiques et courts : extension à l'écoulement du CO2 transcritique.

Modified neural network correlation of refrigerant mass flow rates through adiabatic capillary and short tubes: extension to CO2 transcritical flow.

Auteurs : YANG L., ZHANG C. L.

Type d'article : Article, Article de la RIF

Résumé

This paper presents a modified dimensionless neural network correlation of refrigerant mass flow rates through adiabatic capillary tubes and short tube orifices. In particular, CO2 transcritical flow is taken into account. The definition of neural network input and output dimensionless parameters is grounded on the homogeneous equilibrium model and extended to supercritical inlet conditions. 2000 sets of experimental mass flow-rate data of R12, R22, R134a, R404A, R407C, R410A, R600a and CO2 (R744) in the open literature covering capillary and short tube geometries, subcritical and supercritical inlet conditions are collected for neural network training and testing. The comparison between the trained neural network and experimental data reports 0.65% average and 8.2% standard deviations; 85% data fall into plus or minus 10% error band. Particularly for CO2, the average and standard deviations are -2.5 and 6.0%, respectively. 90% data fall into plus or minus 10% error band.

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Pages : pp. 1293-1301

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    20 €

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Détails

  • Titre original : Modified neural network correlation of refrigerant mass flow rates through adiabatic capillary and short tubes: extension to CO2 transcritical flow.
  • Identifiant de la fiche : 2009-1897
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 32 - n. 6
  • Date d'édition : 09/2009

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