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ANN Modeling of Thermal Conductivity and Viscosity of MXene-Based Aqueous IoNanofluid

  • Nanoparticle-enhanced Ionic Liquids
  • Published:
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Abstract

Research shows that due to enhanced properties IoNanofluids have the potential of being used as heat transfer fluids (HTFs). A significant amount of experimental work has been done to determine the thermophysical and rheological properties of IoNanofluids; however, the number of intelligent models is still limited. In this work, we have experimentally determined the thermal conductivity and viscosity of MXene-doped [MMIM][DMP] ionic liquid. The size of the MXene nanoflakes was determined to be less than 100 nm. The concentration was varied from 0.05 mass% to 0.2 mass%, whereas the temperature varied from 19 °C to 60 °C. The maximum thermal conductivity enhancement of 1.48 was achieved at 0.2 mass% and 30 °C temperature. For viscosity, the maximum relative viscosity of 1.145 was obtained at 0.2 mass% and 23 °C temperature. After the experimental data for thermal conductivity and viscosity were obtained, two multiple linear regression (MLR) models were developed. The MLR models’ performances were found to be poor, which further called for the development of more accurate models. Then two feedforward multilayer perceptron models were developed. The Levenberg–Marquardt algorithm was used to train the models. The optimum models had 4 and 10 neurons for thermal conductivity and viscosity model, respectively. The values of statistical indices showed the models to be well-fit models. Further, relative deviations values were also accessed for training data and testing data, which further showed the models to be well fit.

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Data Availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

HTF:

Heat transfer fluid

IL:

Ionic liquid

NEIL:

Nanoparticle-enhanced ionic liquid

INF:

IoNanofluid

MWCNTs:

Multiwalled carbon nanotubes

GNP:

Graphene nanoplatelets

EG:

Ethylene glycol

ANFIS:

Adaptive neuro fuzzy inference system

ARD:

Average relative deviation

ANN:

Artificial neural network

SVR:

Support vector regression

MLR:

Multiple linear regression

LM:

Levenberg–Marquardt

MSE:

Mean square error

MAE:

Mean absolute error

RD:

Relative deviation

TCR:

Thermal conductivity ratio

RV:

Relative viscosity

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Authors and Affiliations

  1. Sustainable Energy & Acoustics Research Lab, Mechanical Engineering Department, AMU, Aligarh, 202002, India

    Naman Parashar & Syed Mohd Yahya

  2. Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia

    Navid Aslfattahi

  3. Research Center for Nano-Materials and Energy Technology (RCNMET), School of Science and Technology, Sunway University, Bandar Sunway, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia

    R. Saidur

  4. Department of Engineering, Lancaster University, Lancaster, LA1 4YW, UK

    R. Saidur

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  1. Naman Parashar
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Contributions

NP: Modeling, Draft editing, Typesetting; NA: Experimental analysis, Draft editing; SMY: Draft editing, Proofreading, Supervision; RS: Draft editing, Proofreading, Supervision.

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Correspondence to Syed Mohd Yahya.

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Parashar, N., Aslfattahi, N., Yahya, S.M. et al. ANN Modeling of Thermal Conductivity and Viscosity of MXene-Based Aqueous IoNanofluid. Int J Thermophys 42, 24 (2021). https://doi.org/10.1007/s10765-020-02779-5

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