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A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach

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Abstract

In this study, a model is proposed by applying the least squares support vector machine (LSSVM). In addition, genetic algorithm is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. In addition to temperature and concentration of nanoparticles, the parameters which are used in most of the modeling procedures for thermal conductivity, the effect of particle size is considered. By considering the size of nanoparticles as one of the input variables, a more comprehensive model is obtained which is applicable for wider ranges of influential factor on the thermal conductivity of the nanofluid. The coefficient of determination (R2) for the introduced model is equal to 0.9902, and the mean squared error is 8.64 × 10−4 for the thermal conductivity ratio of Al2O3/EG.

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran

    Mohammad Hossein Ahmadi

  2. Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran

    Mohammad Ali Ahmadi

  3. Renewable Energy and Environmental Engineering Department, University of Tehran, Tehran, Iran

    Mohammad Alhuyi Nazari & Roghayeh Ghasempour

  4. Fluid Mechanics, Thermal Engineering and Multiphase Flow Research Laboratory (FUTURE Laboratory), Department of Mechanical Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, 10140, Thailand

    Omid Mahian

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Ahmadi, M.H., Ahmadi, M.A., Nazari, M.A. et al. A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach. J Therm Anal Calorim 135, 271–281 (2019). https://doi.org/10.1007/s10973-018-7035-z

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