NUMERICAL ANALYSES ON THE PREDICTION OF NUSSELT NUMBERS FOR UPWARD AND DOWNWARD FLOWS OF WATER IN A SMOOTH PIPE: EFFECTS OF BUOYANCY AND PROPERTY VARIATIONS

Year 2019, Volume: 5 Issue: 3, 166 - 180, 14.03.2019

Ahmet Selim Dalkılıç

https://doi.org/10.18186/thermal.540367

Cited By: 3

Abstract

This paper includes the Artificial Neural Network (ANN) solution as
one of the numerical analyses to investigate the buoyancy and property
variation effects calculating Nusselt numbers during the upward and downward
flow of water in a smooth pipe. Available data in the literature (Parlatan et
al.) has been used in the analyses to show ANN’s success ratio of
predictability on the measured pipe length’s averaged Nusselt numbers (Nu_avg)
and forced convection’s Nusselt numbers (Nu_o). Mixed convective flow
conditions were valid for Reynolds numbers ranging from 4000 to 9000 with Bond
numbers smaller than 1.3. Dimensionless values of Reynolds number, Grashof
number, Prandtl number, Bond number, Darcy friction factor, isothermal friction
factor in forced convection, ratio of dynamic viscosities, and a Parlatan et
al.’s friction factor were the inputs while Nu_avg and Nu_o were
the outputs of ANN analyses. All data was properly separated for test/training/validation processes. The ANNs
performances were determined by way of relative error criteria with the practice
of unknown test sets. As a result of analyses, outputs were predicted within
the deviation of ±5% accurately, new correlations were proposed using the
inputs, and importance of inputs on the outputs were emphasized according to
dependency analyses showing the importance of buoyancy influence (Gr_T)
and the effects of temperature-dependent viscosity variations under mixed
convection conditions in aiding and opposing transition and turbulent flow of
water in a test tube. 

Keywords

Natural Convection, Single Phase Flow, Buoyancy and Property Variation, Friction Factor, Nusselt Number

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