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Prediction of CHF in concentric-tube open thermosiphon using artificial neural network and genetic algorithm

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Abstract

In this paper, an artificial neural network (ANN) for predicting critical heat flux (CHF) of concentric-tube open thermosiphon has been trained successfully based on the experimental data from the literature. The dimensionless input parameters of the ANN are density ratio, ρ l/ρ v; the ratio of the heated tube length to the inner diameter of the outer tube, L/D i; the ratio of frictional area, d i/(D i + d o); and the ratio of equivalent heated diameter to characteristic bubble size, D he/[σ/g(ρ l−ρ v)]0.5, the output is Kutateladze number, Ku. The predicted values of ANN are found to be in reasonable agreement with the actual values from the experiments with a mean relative error (MRE) of 8.46%. New correlations for predicting CHF were also proposed by using genetic algorithm (GA) and succeeded to correlate the existing CHF data with better accuracy than the existing empirical correlations.

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Abbreviations

D he :

Equivalent heated diameter, (D 2i −d 2o )/D i

D i :

Inner diameter of the outer heated tube (mm)

d i :

Inner diameter of the inner unheated tube (mm)

d o :

Outer diameter of the inner unheated tube (mm)

d o,opt :

Optimum diameter of the inner tube (mm)

ei:

Experimental value

f :

Objective function

g :

Gravitational acceleration (m/s2)

H fg :

Latent heat of vaporization (kJ/kg)

ku :

Kutateladze number, \( ku = [q_{\text{CHF}} /\rho_{\text{v}} H_{\text{fg}} ]/\sqrt[4]{{\sigma g\left( {\rho_{\text{l}} - \rho_{\text{v}} } \right)/\rho_{\text{v}}^{2} }} \)

\( \overline{{ku_{j} }} \) :

Predictive value of ku

L :

Heated tube length (mm)

MRE:

Mean relative error

N :

Number of CHF data

P :

System pressure (MPa)

pi:

ANN predicting value

q co :

CHF for saturated boiling (MW/m2)

R :

Correlation coefficient

RMS:

Root mean square error

ρ :

Density (kg/m3)

σ :

Surface tension (N/m1)

ANN:

Predicted by ANN

cal:

Calculated

EXP:

Experimental

he:

Heated equivalent

fg:

Liquid–vapor phase-change

i:

Inner

l:

Liquid

o:

Outer

v:

Vapor

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Authors and Affiliations

  1. State Key Laboratory of Multiphase Flow in Power Engineering, Department of Nuclear Science and Technology, Xi’an Jiaotong University, 710049, Xi’an, China

    R. H. Chen, G. H. Su & S. Z. Qiu

  2. Department of Applied Quantum Physics and Nuclear Engineering, Kyushu University, Fukuoka, Japan

    Kenji Fukuda

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  1. R. H. Chen
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  2. G. H. Su
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  3. S. Z. Qiu
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  4. Kenji Fukuda
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Correspondence to G. H. Su.

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Chen, R.H., Su, G.H., Qiu, S.Z. et al. Prediction of CHF in concentric-tube open thermosiphon using artificial neural network and genetic algorithm. Heat Mass Transfer 46, 345–353 (2010). https://doi.org/10.1007/s00231-010-0575-9

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  • DOI: https://doi.org/10.1007/s00231-010-0575-9

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