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Experimental investigation and machine learning-based prediction of STHX performance with ethylene glycol–water blends and graphene nanoparticles

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Abstract

The investigation revolves around conducting experiments to examine the heat transport properties of an STHX while utilizing various combinations of ethylene glycol (EG) and deionized water (DW) coolants, namely EG50:DW50, EG60:DW40, EG40:DW60, and EG40:DW60:NP10 (0.1%). The experiments were conducted with two different baffle angles (10° and 20°) and a fixed flow rate of 18 LPM. From the experimental outcomes, it was found that the 20° baffle angle provided better effectiveness for all coolant combinations compared to the 10° baffle angle with the same input temperature. Among these, the combination of EG40:DW60 with graphene nanoparticles (0.1%) showed the maximum effectiveness, reaching up to 58.68%, at the 20° baffle angle. To anticipate the heat exchanger's effectiveness, three machine learning algorithms—Random Forest Regressor, Linear Regression, and XG Boost—were taken into account. The experimental data were separated into training and test datasets, and the machine learning model's performance was evaluated. The results indicated that, overall, the XG Boost algorithm outperformed the other two algorithms across all parameters. The XG Boost algorithm achieved a mean average error of 0.0205 and a Root Mean Squared Error of 0.0222, demonstrating its superior accuracy in predicting the heat exchanger's effectiveness.

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Abbreviations

MAE:

Mean Absolute Error

MSE:

Mean Squared Error

RMSE:

Root Mean Squared Error

STHX:

Shell and tube heat exchanger

EG:

Ethylene glycol

DW:

Deionized water

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Large Groups Project under grant number (RGP. 2/303/44).

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Authors and Affiliations

  1. Department of Mechanical Engineering, M S Sheshgiri College of Engineering and Technology, Belagavi, 590008, India

    Mallesh B. Sanjeevannavar

  2. Centre of Excellence in Material Science, School of Mechanical Engineering, KLE Technological University, Hubballi, Karnataka, 580031, India

    N. R. Banapurmath & A. M. Sajjan

  3. Department of Aeronautical Engineering, Karavali Institute of Technology, Mangalore, Karnataka, 575029, India

    V. Dananjaya Kumar

  4. Mechanical Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia

    Irfan Anjum Badruddin, T. M. Yunus Khan, Sarfaraz Kamangar & Syed Javed

  5. Industrial Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia

    Rahmath Ulla Baig

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  1. Mallesh B. Sanjeevannavar
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Contributions

Conceptualization was performed by MS and NB; data curation by TYK, SK, SJ, and RUB; formal analysis by VDK, IB, TYK, SK, SJ, and RUB; investigation by NB and AS; methodology by MS and VDK; resources by AS, TYK, SK, and RUB; software by MS and SJ; validation by VDK and IB; writing–original draft by NB and AS; writing—review & editing—by IB.

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Correspondence to N. R. Banapurmath.

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Sanjeevannavar, M.B., Banapurmath, N.R., Kumar, V.D. et al. Experimental investigation and machine learning-based prediction of STHX performance with ethylene glycol–water blends and graphene nanoparticles. J Therm Anal Calorim 149, 2969–2984 (2024). https://doi.org/10.1007/s10973-024-12890-6

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