| Titre : | Fin-and-tube condenser performance evaluation using neural networks (2010) |
| Titre original : | Evaluation de la performance d'un condenseur à tubes ailetés à l'aide de réseaux neuronaux |
| Auteurs : | Ling-Xiao Zhao, Auteur ; Chun-Lu Zhang, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | International journal of refrigeration (Vol. 33 N° 3, Mai 2010) |
| Article en page(s) : | pp. 625-634 |
| Note générale : | Génie Mécanique |
| Langues : | Anglais |
| Index. décimale : | 621.5 (Energie pneumatique. Machinerie et outils. Réfrigération) |
| Tags : | Heat exchanger Air-cooled condenser Simulation Performance Neural network |
| Résumé : | The paper presents neural network approach to performance evaluation of the fin-and-tube air-cooled condensers which are widely used in air-conditioning and refrigeration systems. Inputs of the neural network include refrigerant and air-flow rates, refrigerant inlet temperature and saturated temperature, and entering air dry-bulb temperature. Outputs of the neural network consist of the heating capacity and the pressure drops on both refrigerant and air sides. The multi-input multi-output (MIMO) neural network is separated into multi-input single-output (MISO) neural networks for training. Afterwards, the trained MISO neural networks are combined into a MIMO neural network, which indicates that the number of training data sets is determined by the biggest MISO neural network not the whole MIMO network. Compared with a validated first-principle model, the standard deviations of neural network models are less than 1.9%, and all errors fall into ±5%. |
| DEWEY : | 621.5 |
| ISSN : | 0140-7007 |
| En ligne : | http://www.sciencedirect.com/science/article/pii/S0140700709002722 |
