Share

Mixtures flow boiling: modelling heat transfer through artificial neural networks.

Author(s) : SCALABRIN G., CONDOSTA M., MARCHI P.

Type of article: Article

Summary

The flow boiling inside horizontal smooth tubes is studied for mixtures and a new modelling technique is proposed for the heat transfer. In a former work, the flow boiling of pure fluids inside smooth tubes was studied with artificial neural networks (ANN), obtaining individual heat transfer equations in a totally heuristic mode from the direct regression of experimental data. In the present work the method is extended to mixtures. Also in this case the correlation architectures considered for the ANN functions were based on the directly-accessible physical quantities controlling the phenomenon. Two modelling architectures are set up: a first one with the controlling physical quantities as independent variables and a second one where the inputs are the individual heat transfer values of the pure components and the mixture composition. The validation of both the obtained models demonstrate an accurate and substantially equivalent representation of the available heat transfer data with a considerable increase of prediction capability with respect to the conventional methods. The multilayer feed forward neural network equations both in terms of physical variables and in terms of components heat transfer models and composition have obtained very satisfactory accuracies. The modelling technique allows to avoid the need for any thermodynamic and transport property model for the target mixture and, moreover, the method can be used to check the consistency of new data sets before using them for processing. [Reprinted with permission from Elsevier. Copyright, 2006].

Details

  • Original title: Mixtures flow boiling: modelling heat transfer through artificial neural networks.
  • Record ID : 2006-2258
  • Languages: English
  • Source: International Journal of thermal Sciences - vol. 45 - n. 7
  • Publication date: 2006/07

Links


See other articles in this issue (2)
See the source

Indexing