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Performance predictions using Artificial Neural Network for isobutane flow in non-adiabatic capillary tubes.

Prévisions de la performance de l'écoulement d'isobutane dans un tube capillaire non-adiabatique, [grâce à] un réseau neuronal artificiel.

Auteurs : HEIMEL M., LANG W., ALMBAUER R.

Type d'article : Article, Article de la RIF

Résumé

This work presents an Artificial Neural Network (ANN) model of non-adiabatic capillary tubes for isobutane (R600a) as refrigerant. The basis therefore is data obtained by a 1d homogeneous model which has been validated by own measurements and measurements from literature. With this method it is possible to account for choked, non-choked, and also for two-phase inlet conditions, whereas most of the correlations reported in literature are not capable of predicting mass flow rates for non-choked and two-phase inlet conditions. The presented models are valid for a broad range of input parameters in respect to domestic applications – the mass flow rates range from 0 to 5 kg h-1, inlet pressure is from saturation pressure at ambient conditions up to 10 bar, the inlet quality is from 0.5 (capillary) and 0.7 (suction line) to 0 and subcooling (capillary) and superheating (suction line) from 0 K to 30 K.

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Pages : 281-289

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Détails

  • Titre original : Performance predictions using Artificial Neural Network for isobutane flow in non-adiabatic capillary tubes.
  • Identifiant de la fiche : 30010372
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 38
  • Date d'édition : 02/2014

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