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Réseaux neuronaux artificiels pour la prévision des performances des systèmes de conditionnement d'air automobiles.

Artificial neural networks for automotive air-conditioning systems performance prediction.

Auteurs : KAMAR H. M., AHMAD R., KAMSAH N. B., et al.

Type d'article : Article

Résumé

In this study, ANN model for a standard air-conditioning system for a passenger car was developed to predict the cooling capacity, compressor power input and the coefficient of performance (COP) of the automotive air-conditioning (AAC) system. This paper describes the development of an experimental rig for generating the required data. The experimental rig was operated at steady-state conditions while varying the compressor speed, air temperature at evaporator inlet, air temperature at condenser inlet and air velocity at evaporator inlet. Using these data, the network using Lavenberg-Marquardt (LM) variant was optimized for 4-3-3 (neurons in input-hidden-output layers) configuration. The developed ANN model for the AAC system shows good performance with an error index in the range of 0.65 -1.65%, mean square error (MSE) between 1.09 x 100000 and 9.05 x 100000 and the root mean square error (RMSE) in the range of 0.33-0.95%. Moreover, the correlation which relates the predicted outputs of the ANN model to the experimental results has a high coefficient in predicting the AAC system performance.

Détails

  • Titre original : Artificial neural networks for automotive air-conditioning systems performance prediction.
  • Identifiant de la fiche : 30006016
  • Langues : Anglais
  • Source : Applied Thermal Engineering - vol. 50 - n. 1
  • Date d'édition : 01/2013
  • DOI : http://dx.doi.org/10.1016/j.applthermaleng.2012.05.032

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