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

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ANN Modeling of Thermal Conductivity and Viscosity of MXene-Based Aqueous IoNanofluid. / Parashar, N.; Aslfattahi, N.; Yahya, S.M. et al.
In: International Journal of Thermophysics, Vol. 42, No. 2, 24, 11.01.2021.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Parashar, N, Aslfattahi, N, Yahya, SM & Saidur, R 2021, 'ANN Modeling of Thermal Conductivity and Viscosity of MXene-Based Aqueous IoNanofluid', International Journal of Thermophysics, vol. 42, no. 2, 24. https://doi.org/10.1007/s10765-020-02779-5

APA

Parashar, N., Aslfattahi, N., Yahya, S. M., & Saidur, R. (2021). ANN Modeling of Thermal Conductivity and Viscosity of MXene-Based Aqueous IoNanofluid. International Journal of Thermophysics, 42(2), Article 24. https://doi.org/10.1007/s10765-020-02779-5

Vancouver

Parashar N, Aslfattahi N, Yahya SM, Saidur R. ANN Modeling of Thermal Conductivity and Viscosity of MXene-Based Aqueous IoNanofluid. International Journal of Thermophysics. 2021 Jan 11;42(2):24. doi: 10.1007/s10765-020-02779-5

Author

Parashar, N. ; Aslfattahi, N. ; Yahya, S.M. et al. / ANN Modeling of Thermal Conductivity and Viscosity of MXene-Based Aqueous IoNanofluid. In: International Journal of Thermophysics. 2021 ; Vol. 42, No. 2.

Bibtex

@article{a4fb716f8332409887b89c39767ade7b,
title = "ANN Modeling of Thermal Conductivity and Viscosity of MXene-Based Aqueous IoNanofluid",
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{\textquoteright} 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. ",
keywords = "1,3-Dimethyl imidazolium dimethyl-phosphate, Aqueous ionic liquid, Levenberg–Marquardt algorithm, MXene, Thermal conductivity, Viscosity, Heat transfer, Ionic liquids, Linear regression, Multilayer neural networks, Well testing, Enhanced properties, Feed-forward multilayer perceptron, Marquardt algorithm, Multiple linear regression models, Relative deviations, Rheological property, Statistical indices, Thermal conductivity enhancement",
author = "N. Parashar and N. Aslfattahi and S.M. Yahya and R. Saidur",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/s10765-020-02779-5",
year = "2021",
month = jan,
day = "11",
doi = "10.1007/s10765-020-02779-5",
language = "English",
volume = "42",
journal = "International Journal of Thermophysics",
number = "2",

}

RIS

TY - JOUR

T1 - ANN Modeling of Thermal Conductivity and Viscosity of MXene-Based Aqueous IoNanofluid

AU - Parashar, N.

AU - Aslfattahi, N.

AU - Yahya, S.M.

AU - Saidur, R.

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s10765-020-02779-5

PY - 2021/1/11

Y1 - 2021/1/11

N2 - 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.

AB - 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.

KW - 1,3-Dimethyl imidazolium dimethyl-phosphate

KW - Aqueous ionic liquid

KW - Levenberg–Marquardt algorithm

KW - MXene

KW - Thermal conductivity

KW - Viscosity

KW - Heat transfer

KW - Ionic liquids

KW - Linear regression

KW - Multilayer neural networks

KW - Well testing

KW - Enhanced properties

KW - Feed-forward multilayer perceptron

KW - Marquardt algorithm

KW - Multiple linear regression models

KW - Relative deviations

KW - Rheological property

KW - Statistical indices

KW - Thermal conductivity enhancement

U2 - 10.1007/s10765-020-02779-5

DO - 10.1007/s10765-020-02779-5

M3 - Journal article

VL - 42

JO - International Journal of Thermophysics

JF - International Journal of Thermophysics

IS - 2

M1 - 24

ER -