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, 43:184 | Cite as

Thermal performance prediction models for a pulsating heat pipe using Artificial Neural Network (ANN) and Regression/Correlation Analysis (RCA)

  • Vipul M Patel
  • Hemantkumar B Mehta
Article
  • 66 Downloads

Abstract

Pulsating heat pipe (PHP) is one of the prominent research areas in the family of heat pipes. Heat transfer and fluid flow mechanism associated with PHP are quite involved. The analytical models are simple in nature and limited in scope and applicability. The regression models and Artificial Neural Network (ANN) are also limited to a number of input parameters, their ranges and accuracy. The present paper discusses the thermal performance prediction models of a PHP based on ANN and RCA approach. Totally 1652 experimental data are collected from the literature (2003–2017). Nine major influencing input variables are considered for the first time to develop the prediction models. Feed-forward back-propagation neural network is developed and verified. Backward regression analysis is used in RCA-based regression model. Linear and power-law regression correlations are developed for input heat flux in terms of dimensionless Kutateladze (Ku) number, which is a function of Jakob number (Ja), Morton number (Mo), Bond number (Bo), Prandtl number (Pr) and geometry of a PHP. The prediction accuracy of present regression models (R2 = 0.95) is observed to be better as compared with literature-based correlations.

Keywords

PHP prediction models ANN RCA Kutateladze (Ku) number 

Nomenclature

Ku

Kutateladze number

Ja

Jakob number

Mo

Morton number

Bo

bond number

Pr

Prandtl number

Rth

thermal resistance (K/W)

Yp

predicted output by the regression model

hlv

latent heat of vaporization (J/kg)

Cp

specific heat at constant pressure (J/kg K)

R2

coefficient of determination

\( R_{adj}^{2} \)

adjusted coefficient of determination

D

diameter (mm)

L

length (mm)

K

thermal conductivity (W/m K)

N

number of turns

Q

heat input (W)

Y

real output

X

input parameter

A

coefficient

T

temperature

R

coefficient of correlation

x

input data

w

weight

b

bias

g

gravitational acceleration (m/s2)

e

error

N

number of observations

K

number of predictors

Abbreviations

FR

filling ratio

WF

working fluid

ANN

artificial neural network

RCA

regression/correlation analysis

Purelin

linear transfer function

tansig

tangent sigmoid function

logsig

log-sigmoid transfer function

RBF

radial basis function

MSE

mean square error

SSE

sum squared error

SST

sum squared total

t-stat

t-statistic

MARD

mean absolute relative deviation

Greek symbols

μ

viscosity (Pa s)

ρ

density (kg/m3)

σ

surface tension (N/m)

θ

orientation

Subscripts

l

liquid

e

evaporator

c

condenser

i

inner

o

outer

v

vapour

Notes

Funding

The funding was provided by SVNIT, Surat (Grant No. Dean(R&C)/1503/2013-14).

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Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringSardar Vallabhbhai National Institute of TechnologySuratIndia

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