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Atomization and Sprays
Facteur d'impact: 1.262 Facteur d'impact sur 5 ans: 1.518 SJR: 0.814 SNIP: 1.18 CiteScore™: 1.6

ISSN Imprimer: 1044-5110
ISSN En ligne: 1936-2684

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Atomization and Sprays

DOI: 10.1615/AtomizSpr.v12.i4.10
pages 359-386

APPLICATION OF ARTIFICIAL NEURAL NETWORKS MODELING TO SPRAYS AND SPRAY IMPINGEMENT HEAT TRANSFER

M. A. Aamir
Department of Mechanical, Aerospace and Manufacturing Engineering, UMIST, Manchester, United Kingdom
M. M. Awais
Department of Mechanical Engineering, Imperial College, London, United Kingdom
A. Paul Watkins
Energy and Multiphysics Research Group, School of Mechanical, Aerospace, and Civil Engineer- ing, University of Manchester, United Kingdom

RÉSUMÉ

Artificial neural networks (ANN) models have been developed and applied to free propane sprays and to water spray cooling heat flux predictions. For the propane spray conditions the ANN model is trained against the computational fluid dynamics (CFD) results and verified against experimental data for drop diameter at the centreline 95 mm from the nozzle. It is shown that an ANN model trained on CFD gives results comparable to the CFD predictions and that it can therefore be employed online in industry to investigate and limit the consequences of a depressurization accident. When enough experimental data are present, as in the spray cooling case, the ANN model can be welt trained and proves to be an alternative numerical modeling technique to CFD, with the numerical predictions comparable to the CFD predictions, but in real-time mode.


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