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ANFIS Modeling of Boiling Heat Transfer over Tube Bundles

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 817))

Abstract

The article describes the application of artificial intelligence technique artificial neuro-fuzzy inference system (ANFIS) to predict the flow boiling heat transfer coefficient for distilled water on individual row in plain tube bundles. The variation of row-wise heat transfer coefficients is discussed with respect to the operating conditions such as mass flux, heat flux, and pitch to distance. A semi-empirical correlation is also formulated to predict the flow boiling Nusselt number taking the Peclet number, Froude number, and pitch-to-diameter ratio as inputs. The experimental data are predicted with ±15% accuracy by the semi-empirical correlation, whereas the ANFIS model is capable to predict within a maximum error of ±10%.

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar, 751024, India

    Abhilas Swain

  2. School of Mechanical Sciences, IIT Bhubaneswar, Jatni, 752050, India

    Mihir Kumar Das

Authors
  1. Abhilas Swain
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  2. Mihir Kumar Das
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Corresponding author

Correspondence to Abhilas Swain .

Editor information

Editors and Affiliations

  1. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  2. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  3. Department of Mathematics and Computer Science, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya Nagar

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  5. School of Basic Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, India

    Akshay Kumar Ojha

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Swain, A., Das, M.K. (2019). ANFIS Modeling of Boiling Heat Transfer over Tube Bundles. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_34

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