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dc.contributor.authorOmri, Mohamed
dc.contributor.authorBarrau, Jérôme
dc.contributor.authorSaber, Abdel-halim
dc.description.abstractSpallart and Allmaras is a one-equation turbulence model developed for predicting complex aerodynamic flows. It will be of great interest to extend its application to predict other kind of flows in presence of heat and mass transfer. Specifically we will demonstrate, by considering two applications, that this one equation model may replace the two equation models in several heat transfer applications such as HVAC, electronics cooling and fire propagation in buildings. This will reduce computing time and numerical stiffness in CFD calculations. The first application is the 2D natural turbulent convection in a cavity differentially heated; which was considered as a benchmark case before applying CFD in any HVAC configuration. The exact configuration proposed by Ampofo, was then modeled and the radiation effect was studied. The second application proposed by Riera was the numerical prediction of a heat exchanger based on hybrid jet impingement/micro-channel; it was used for managing high heat-flux thermal of power electronics devices. Those applications deal with low and high fluxes transfer respectively and allow demonstrating clearly the capacity of the one equation model to predict confined turbulent natural/ forced convection and jet-impingement heat transfer.ca_ES
dc.description.sponsorshipThis project was funded by the deanship of Scientific Research (DSR) at king Abdulaziz University, Jeddah, under Grant no: G-568-305-1436.ca_ES
dc.relation.isformatofReproducció del document publicat a:
dc.relation.ispartofRevista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, 2020, vol. 36, núm. 2, p. 27ca_ES
dc.rightscc-by-nc-sa (c) Omri, Mohamed et al., 2020ca_ES
dc.subjectNatural convectionca_ES
dc.titleAssessment of one equation model for natural convection and the prediction of impingement heat transferca_ES

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cc-by-nc-sa (c) Omri, Mohamed et al., 2020
Except where otherwise noted, this item's license is described as cc-by-nc-sa (c) Omri, Mohamed et al., 2020