The prediction of heat transfer and fluid characteristics for equilateral triangular bodies in tandem arrangement by artificial neural networks

Manay E.Güneş S.Akçadirci E.Özceyhan V.Çakir U.Çomakli O.20.04.20192019-04-2020.04.20192019-04-2020121303-9709https://hdl.handle.net/20.500.12403/904The objective of this study is to investigate the effect of the spacing between equilateral dual triangular bodies symmetrically placed into the channel axis under steady state conditions on heat transfer and fluid characteristics by using artificial neural networks (ANN). The Back Propagation (BP) training algorithm was applied to train the model. The successful application proved that ANN model can be used for predicting the Nusselt number and skin friction coefficient as a convenient and effective method. The distribution of local Nusselt number, skin friction coefficient along the channel wall and overall enhancement ratio of all investigated cases are presented.eninfo:eu-repo/semantics/closedAccessArtificial neural networkHeat transfer enhancementTandem arrangementTriangular bodyBodies in tandemChannel axisChannel wallEnhancement ratiosHeat Transfer enhancementLocal Nusselt numberSkin friction coefficientSteady-state conditionTandem arrangementTraining algorithmsTriangular bodyFrictionNusselt numberPipe flowSkin frictionNeural networksArtificial neural networkHeat transfer enhancementTandem arrangementTriangular bodyBodies in tandemChannel axisChannel wallEnhancement ratiosHeat Transfer enhancementLocal Nusselt numberSkin friction coefficientSteady-state conditionTandem arrangementTraining algorithmsTriangular bodyFrictionNusselt numberPipe flowSkin frictionNeural networksThe prediction of heat transfer and fluid characteristics for equilateral triangular bodies in tandem arrangement by artificial neural networksArticle2525055172-s2.0-84860135371Q3WOS:000421136900028N/A