Performance predictions using Artificial Neural Network for isobutane flow in non-adiabatic capillary tubes / M. Heimel in International journal of refrigeration, Vol. 38 (Février 2014)  

Public ISBD [article]  inInternational journal of refrigeration > Vol. 38 (Février 2014) . - pp. 281–289 Titre : Performance predictions using Artificial Neural Network for isobutane flow in non-adiabatic capillary tubes Titre original : Prévisions de la performance de l'écoulement d'isobutane dans un tube capillaire non-adiabatique utilisant un réseau neuronal artificiel Type de document : texte imprimé Auteurs : M. Heimel, Auteur ; W. Lang, Auteur ; R. Almbauer, Auteur Année de publication : 2014 Article en page(s) : pp. 281–289 Note générale : Refrigeration Langues : Anglais (eng) Mots-clés : Capillary tube  Artificial Neural Network  Heat exchanger  Isobutane  Non-adiabatic 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. En ligne : http://www.sciencedirect.com/science/article/pii/S0140700713002260 [article] 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 utilisant un réseau neuronal artificiel [texte imprimé] / M. Heimel, Auteur ; W. Lang, Auteur ; R. Almbauer, Auteur . - 2014 . - pp. 281–289. Refrigeration Langues : Anglais (eng) in International journal of refrigeration > Vol. 38 (Février 2014) . - pp. 281–289 Mots-clés : Capillary tube  Artificial Neural Network  Heat exchanger  Isobutane  Non-adiabatic 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. En ligne : http://www.sciencedirect.com/science/article/pii/S0140700713002260

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