Forced convection and thermal predictions of pulsating nanofluid flow over a backward facing step with a corrugated bottom wall

•Pulsating nanofluid flow over a BFS with a corrugated bottom wall was studied.•Average Nu enhances as the Re, A, length and height of the corrugation increase.•Average Nusselt number versus Strouhal number shows a resonant type behavior.•An efficient method for thermal predictions was developed by...

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Veröffentlicht in:International journal of heat and mass transfer 2017-07, Vol.110, p.231-247
Hauptverfasser: Selimefendigil, Fatih, Öztop, Hakan F.
Format: Artikel
Sprache:eng
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Zusammenfassung:•Pulsating nanofluid flow over a BFS with a corrugated bottom wall was studied.•Average Nu enhances as the Re, A, length and height of the corrugation increase.•Average Nusselt number versus Strouhal number shows a resonant type behavior.•An efficient method for thermal predictions was developed by combined POD and ANN. In this study, laminar forced convection of pulsating nanofluid flow over a backward-facing step with a corrugated bottom wall was numerically examined by using finite volume method. Part of the bottom wall downstream of the step was corrugated and kept at constant temperature. Effects of Reynolds number, length and height of the surface corrugation wave, nanoparticle volume fraction, amplitude and frequency of flow pulsation on the fluid flow and heat transfer were numerically investigated. It was observed that average Nusselt number enhances as the Reynolds number, length and height of the corrugation wave increase. Average Nusselt number versus Strouhal number shows a resonant type behavior and flow pulsation amplitude increment results in heat transfer enhancement. Average heat transfer rate increases with the inclusion of the nanoparticles but the rate of enhancement depends on the nanoparticle solid volume fraction interval. An efficient computational strategy for the thermal performance prediction of the system was developed by using proper orthogonal decomposition and artificial neural networks.
ISSN:0017-9310
1879-2189
DOI:10.1016/j.ijheatmasstransfer.2017.03.010