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Nanofluids thermal conductivity prediction applying a novel hybrid data-driven model validated using Monte Carlo-based sensitivity analysis

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Abstract

Accurate estimation of the thermal conductivity of nanofluids plays a key role in industrial heat transfer applications. Currently available experimental and empirical relationships can be used to estimate thermal conductivity. However, since the environmental conditions and properties of the nanofluids constituents are not considered these models cannot provide the expected accuracy and reliability for researchers. In this research, a robust hybrid artificial intelligence model was developed to accurately predict wide variety of relative thermal conductivity of nanofluids. In the new approach, the improved simulated annealing (ISA) was used to optimize the parameters of the least-squares support vector machine (LSSVM-ISA). The predictive model was developed using a data bank, consist of 1800 experimental data points for nanofluids from 32 references. The volume fraction, average size and thermal conductivity of nanoparticles, temperature and thermal conductivity of base fluid were selected as influent parameters and relative thermal conductivity was chosen as the output variable. In addition, the obtained results from the LSSVM-ISA were compared with the results of the radial basis function neural network (RBF-NN), K-nearest neighbors (KNN), and various existing experimental correlations models. The statistical analysis shows that the performance of the proposed hybrid predictor model for testing stage (R = 0.993, RMSE = 0.0207) is more reliable and efficient than those of the RBF-NN (R = 0.970, RMSE = 0.0416 W/m K), KNN (R = 0.931, RMSE = 0.068 W/m K) and all of the existing empirical correlations for estimating thermal conductivity of wide variety types of nanofluids. Finally, robustness and convergence analysis were conducted to evaluate the model reliability. A comprehensive sensitivity analysis using Monte Carlo simulation was carried out to identify the most significant variables of the developed models affecting the thermal conductivity predictions of nanofluids.

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Abbreviations

C MC(N):

Mean convergence function

D p :

Particle diameter, nm

I A :

Index of agreement

k bf :

Thermal conductivity of base fluid, w/m K

k p :

Thermal conductivity of nanoparticle, w/m K

K r :

Relative thermal conductivity (–)

MAPE:

Mean absolute percentage error

NC MC(N):

Normalized convergence function

R :

Correlation coefficient

RAE:

Relative absolute error

RMSE:

Root mean square error, w/m K

T :

Temperature, °K

\(\rho\) :

Density of the nanoparticles, g/cm3

\(\phi\) :

Nanoparticle volume fraction (%)

bf:

Base fluid

i:

Nanoparticle ID

nf:

Nanofluid

np:

Nanoparticle

p:

Particles

r:

Relative

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Authors and Affiliations

  1. Faculty of Engineering, Shohadaye Hoveizeh University of Technology, Dasht-e Azadegan, Iran

    Ali Naseri, Mehdi Jamei & Mohammad Behbahani

  2. Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran

    Iman Ahmadianfar

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  1. Ali Naseri
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Naseri, A., Jamei, M., Ahmadianfar, I. et al. Nanofluids thermal conductivity prediction applying a novel hybrid data-driven model validated using Monte Carlo-based sensitivity analysis. Engineering with Computers 38 (Suppl 1), 815–839 (2022). https://doi.org/10.1007/s00366-020-01163-z

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  • DOI: https://doi.org/10.1007/s00366-020-01163-z

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