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A review on the applications of intelligence methods in predicting thermal conductivity of nanofluids

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Abstract

Intelligence methods, including Artificial Neural Networks (ANNs) and Support Vector Machine, are among the popular approaches for modeling the engineering systems with high accuracy. Nanofluid’s thermal conductivity depends on several factors such as the dimensions of nanoparticles, their concentration, synthesis method and temperature. Intelligence methods are appropriate tools to precisely estimate nanofluids’ thermal conductivity. Different methods and structures are used for the modeling of this property. In the present article, the related studies, using intelligence methods in thermal conductivity estimation, are comprehensively reviewed. According to the literature review, the accuracy of the predictive models has an association with their structure, utilized functions, selected input variables and employed algorithm. For instance, compared with mathematical correlations, obtained by curve fitting, ANNs are more accurate. Moreover, it is concluded that the structure of the NN, including numbers of hidden layer and neurons, can noticeably influence their performance. In the reviewed articles, trial and error are performed to distinguish the most favorable structure of ANNs. Due to the dependency of the models on the input variable, considering all the factors affecting the nanofluid’s thermal conductivity results in higher precision of the models.

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Acknowledgements

The work of Ioan Pop has been supported from the grant PN-III-P4-ID-PCE-2016-0036, UEFISCDI, Romania.

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Authors and Affiliations

  1. Aerospace Engineering Department, Shahid Sattari Aeronautical University of Science and Technology, Tehran, Iran

    Mahdi Ramezanizadeh & Mohammad Alhuyi Nazari

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

    Mohammad Hossein Ahmadi

  3. Dipartimento di Ingegneria e Architettura, Università degli Studi di Parma, Parco Area delle Scienze 181/A, 43124, Parma, Italy

    Giulio Lorenzini

  4. Department of Mathematics, Babeş-Bolyai University, 400084, Cluj-Napoca, Romania

    Ioan Pop

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  1. Mahdi Ramezanizadeh
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  2. Mohammad Alhuyi Nazari
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  3. Mohammad Hossein Ahmadi
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  4. Giulio Lorenzini
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  5. Ioan Pop
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Correspondence to Mohammad Hossein Ahmadi.

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Ramezanizadeh, M., Alhuyi Nazari, M., Ahmadi, M.H. et al. A review on the applications of intelligence methods in predicting thermal conductivity of nanofluids. J Therm Anal Calorim 138, 827–843 (2019). https://doi.org/10.1007/s10973-019-08154-3

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