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Calcul des propriétés thermodynamiques de mélanges ammoniac-eau à l'aide de réseaux de neurones artificiels.

Computing thermodynamic properties of ammonia–water mixtures using artificial neural networks.

Auteurs : GOYAL A., GARIMELLA S.

Type d'article : Article, Article de la RIF

Résumé

Artificial neural networks (ANN) provide a computationally efficient pathway for solving complex non-linear problems. Cascaded ANN are used to compute thermodynamic properties of the ammonia–water mixture working-pair commonly used in vapor absorption heat pumps. Thermodynamic property routines can affect the accuracy and pose a significant computational bottleneck for steady-state and transient cycle simulations of ammonia–water. The properties computed using the proposed method agree within 0.5% of equation of state data over a wide range of operating parameters. It is observed that the ANN based property routines, developed as explicit functions, offer ~60% decrease in computational time over currently used property routines. As a case study, the property calculation modules developed using ANN are employed in simulating the dynamic response of a representative ammonia-water condenser for a vapor absorption cycle. The models predict the transient behavior accurately with an ~80% computational speedup compared to conventional property routines.

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Pages : 315-325

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Détails

  • Titre original : Computing thermodynamic properties of ammonia–water mixtures using artificial neural networks.
  • Identifiant de la fiche : 30025639
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 100
  • Date d'édition : 04/2019
  • DOI : http://dx.doi.org/10.1016/j.ijrefrig.2019.02.011

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