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

Ahmet Selim Dalkiliç

doi:10.18186/thermal.540367

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 (Nuavg) and forced convection’s Nusselt numbers (Nuo). 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 Nuavg and Nuo 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 (GrT) 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.&nbsp;

Highlights

Mixed convection conditions occur when both forced and free convection have no negligible effects on flow
New correlations were formed upon acquiring Artificial Neural Network (ANN) data using dimensionless numbers of Reynolds number, Grashof number, Prandtl number, Bond number, Darcy friction factor, isothermal friction factor in forced convection, ratio of dynamic viscosities, and Parlatan et al.’s friction factor, and dependency analysis has been performed among inputs to determine the parameters’ importance
Dimensionless numbers are commonly used in many scientific areas

Summary

INTRODUCTION

Mixed convection conditions occur when both forced and free convection have no negligible effects on flow. Voicu et al [8] numerically investigated the temperature and velocity profile of aqueous glycol solution in a vertical double pipe parallel heat exchanger under laminar mixed convection condition They found out the Richardson number has influence over the velocity and temperature profile of the inner flow. Kang and Chung [9] made an experimental study to determine the influence of height-diameter ratio of a vertical tube over buoyancy effect They recommended using the heated length as the characteristic length for the Grashof number because diameter was found to be inappropriate and cannot respond to the variety of the results due to length change. New correlations were formed upon acquiring ANN data using dimensionless numbers of Reynolds number, Grashof number, Prandtl number, Bond number, Darcy friction factor, isothermal friction factor in forced convection, ratio of dynamic viscosities, and Parlatan et al.’s friction factor, and dependency analysis has been performed among inputs to determine the parameters’ importance. It should be noted that extensive knowledge of the use of ANN methods on the single- and two-phase flows, method of least squares, error analyses’ calculation procedure including R square error, proportional error, and Mean square error can be seen from authors’ previous publications [12-19]

RESULTS AND DISCUSSION

80 Correlation 5

CONCLUSION