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Titre :
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A neural network approach for evaluation of surface heat transfer coefficient
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Auteurs :
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S. Sreekanth ;
H. Ramaswamy ;
S. Sablani ;
S. Prasher
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Type de document :
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article/chapitre/communication
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Année de publication :
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1999
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Format :
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p. 329-348
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Langues:
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= Anglais
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Catégories :
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SCIENCES FONDAMENTALES ET APPLIQUEES
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Mots-clés:
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COEFFICIENT DE TRANSFERT DE CHALEUR
;
RESEAU DE NEURONES
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Résumé :
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An artificial neural network (ANN) approach for tackling the inverse heat conduction problems was explored - specifically for the determination of surface heat transfer coefficient at the liquid-solid interface using the temperature profile information within the solid. Although the concept is quite generic, the specific cases considered have a particular relevance to food process engineering applications. The concept was tested with two geometric shapes: a sphere and a finite cylinder, the former representing the simplest geometry and the latter representing a cross product of an infinite cylinder and an infinite plate. In developing the ANN model, two approaches were used. In the first one, the ANN model was trained to predict the surface convective heat transfer function, Blot number (Bi) from the slope coefficient (m) of temperature ratio curve under varying boundary conditions. The associated mean relative prediction errors were as high as 5.5% with a standard deviation of 8%. In the second ANN approach, m was related to tan(-1) (Bi) which significantly improved the model's predictive performance.
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Source :
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Journal of Food Processing and Preservation, vol.23
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