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Regression-Based Empirical Modeling of Thermal Conductivity of CuO-Water Nanofluid using Data-Driven Techniques

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Abstract

In this work, an empirical correlation to predict the thermal conductivity of CuO-water nanofluid is developed. The prime novelty of this work is to include the size of the nanoparticles and to utilize the techniques of artificial intelligence on this problem. The experimentation is carried out for the following operating range: working temperature between 302 K to 323 K, particle volume fraction between 0.1 % and 0.4 %, and a particle diameter of 40 nm and 80 nm. The results of the experimentation are benchmarked with the standard properties of water. Afterwards, three different data-driven techniques (SRM, GMDH and ANN) are applied for the correlation development of thermal conductivity. It is reported that GMDH of third polynomial power is the most appropriate yielding an R2 of 0.99973, SSE of 2.208834e−06, and MSE of 1.004e−08. Extensive external validation is also carried out on these techniques to ensure the correctness of the methodology. The results of these surrogate models are compared with other models based on their performance indices of regression. Another comparative study has shown that the prediction capability of our proposed regression model has a minimum deviation of ~ 0.35 % and a maximum deviation of ~ 3.7 %.

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Abbreviations

\( \alpha \) :

Thermal diffusivity [m2∙s−1]

\( \beta \) :

Coefficient of thermal expansion [K−1]

\( \sigma \) :

Standard deviation [W∙m−1∙K−1]

\( \phi \) :

Fraction [%]

\( \nu \) :

Kinematic viscosity [m2∙s−1]

\( d \) :

Diameter [m]

\( g \) :

Standard acceleration due to gravity [m∙s−2]

\( k \) :

Thermal conductivity [W∙m−1∙K−1]

\( L \) :

Length [m]

\( Q \) :

Heat transfer [W]

\( Ra \) :

Rayleigh number [-]

\( R \) :

Radius [m]

\( T \) :

Temperature [K]

\( nf \) :

Nanofluid

\( p \) :

Particle

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Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes por Anillo Periférico Norte, Apdo., Cordemex, Postal 150, Mérida, Yucatán, Mexico

    Rasikh Tariq

  2. Department of Mechanical Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan

    Yasir Hussain

  3. Department of Mechanical Engineering, International Islamic University, Islamabad, Pakistan

    Nadeem Ahmed Sheikh

  4. Department of Mechanical Engineering, Faculty of Engineering and Technology, HITEC University, Taxila, 47080, Pakistan

    Kamran Afaq

  5. Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Kingdom of Saudi Arabia

    Hafiz Muhammad Ali

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  1. Rasikh Tariq
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  3. Nadeem Ahmed Sheikh
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  5. Hafiz Muhammad Ali
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Correspondence to Rasikh Tariq or Hafiz Muhammad Ali.

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Tariq, R., Hussain, Y., Sheikh, N.A. et al. Regression-Based Empirical Modeling of Thermal Conductivity of CuO-Water Nanofluid using Data-Driven Techniques. Int J Thermophys 41, 43 (2020). https://doi.org/10.1007/s10765-020-2619-9

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