| Titre : | Performance predictions using Artificial Neural Network for isobutane flow in non-adiabatic capillary tubes (2014) |
| Titre original : | Prévisions de la performance de l'écoulement d'isobutane dans un tube capillaire non-adiabatique utilisant un réseau neuronal artificiel |
| Auteurs : | M. Heimel, Auteur ; W. Lang, Auteur ; R. Almbauer, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | International journal of refrigeration (Vol. 38, Février 2014) |
| Article en page(s) : | pp. 281–289 |
| Note générale : | Refrigeration |
| Langues : | Anglais |
| Tags : | 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 |
