IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i6p1970-d766671.html
   My bibliography  Save this article

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Author

Listed:
  • Jose Loyola-Fuentes

    (Hexxcell Ltd., Foundry Building, 77 Fulham Palace Rd, London W6 8AF, UK)

  • Luca Pietrasanta

    (Advanced Engineering Centre, School of Architecture, Technology and Engineering, University of Brighton, Lewes Rd, Brighton BN2 4AT, UK)

  • Marco Marengo

    (Advanced Engineering Centre, School of Architecture, Technology and Engineering, University of Brighton, Lewes Rd, Brighton BN2 4AT, UK)

  • Francesco Coletti

    (Hexxcell Ltd., Foundry Building, 77 Fulham Palace Rd, London W6 8AF, UK
    Department of Chemical Engineering, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK)

Abstract

Owing to their simple construction, cost effectiveness, and high thermal efficiency, pulsating heat pipes (PHPs) are growing in popularity as cooling devices for electronic equipment. While PHPs can be very resilient as passive cooling systems, their operation relies on the establishment and persistence of slug/plug flow as the dominant flow regime. It is, therefore, paramount to predict the flow regime accurately as a function of various operating parameters and design geometry. Flow pattern maps that capture flow regimes as a function of nondimensional numbers (e.g., Froude, Weber, and Bond numbers) have been proposed in the literature. However, the prediction of flow patterns based on deterministic models is a challenging task that relies on the ability of explaining the very complex underlying phenomena or the ability to measure parameters, such as the bubble acceleration, which are very difficult to know beforehand. In contrast, machine learning algorithms require limited a priori knowledge of the system and offer an alternative approach for classifying flow regimes. In this work, experimental data collected for two working fluids (ethanol and FC-72) in a PHP at different gravity and power input levels, were used to train three different classification algorithms (namely K-nearest neighbors, random forest, and multilayer perceptron). The data were previously labeled via visual classification using the experimental results. A comparison of the resulting classification accuracy was carried out via confusion matrices and calculation of accuracy scores. The algorithm presenting the highest classification performance was selected for the development of a flow pattern map, which accurately indicated the flow pattern transition boundaries between slug/plug and annular flows. Results indicate that, once experimental data are available, the proposed machine learning approach could help in reducing the uncertainty in the classification of flow patterns and improve the predictions of the flow regimes.

Suggested Citation

  • Jose Loyola-Fuentes & Luca Pietrasanta & Marco Marengo & Francesco Coletti, 2022. "Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes," Energies, MDPI, vol. 15(6), pages 1-20, March.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:1970-:d:766671
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/6/1970/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/6/1970/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alhuyi Nazari, Mohammad & Ahmadi, Mohammad H. & Ghasempour, Roghayeh & Shafii, Mohammad Behshad, 2018. "How to improve the thermal performance of pulsating heat pipes: A review on working fluid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 630-638.
    2. Nicola Jones, 2018. "How to stop data centres from gobbling up the world’s electricity," Nature, Nature, vol. 561(7722), pages 163-166, September.
    3. Nine, Md J. & Tanshen, Md. Riyad & Munkhbayar, B. & Chung, Hanshik & Jeong, Hyomin, 2014. "Analysis of pressure fluctuations to evaluate thermal performance of oscillating heat pipe," Energy, Elsevier, vol. 70(C), pages 135-142.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bin Yang & Xin Zhu & Boan Wei & Minzhang Liu & Yifan Li & Zhihan Lv & Faming Wang, 2023. "Computer Vision and Machine Learning Methods for Heat Transfer and Fluid Flow in Complex Structural Microchannels: A Review," Energies, MDPI, vol. 16(3), pages 1-24, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ana Salomé García-Muñiz & María Rosalía Vicente, 2021. "The Effects of Informational Feedback on the Energy Consumption of Online Services: Some Evidence for the European Union," Energies, MDPI, vol. 14(10), pages 1-14, May.
    2. Fridgen, Gilbert & Keller, Robert & Körner, Marc-Fabian & Schöpf, Michael, 2020. "A holistic view on sector coupling," Energy Policy, Elsevier, vol. 147(C).
    3. Muhammad Fahad & Arsalan Shahid & Ravi Reddy Manumachu & Alexey Lastovetsky, 2019. "A Comparative Study of Methods for Measurement of Energy of Computing," Energies, MDPI, vol. 12(11), pages 1-42, June.
    4. John Martinovic & Markus Hähnel & Guntram Scheithauer & Waltenegus Dargie, 2022. "An introduction to stochastic bin packing-based server consolidation with conflicts," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 296-331, July.
    5. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    6. Salil Bharany & Sandeep Sharma & Osamah Ibrahim Khalaf & Ghaida Muttashar Abdulsahib & Abeer S. Al Humaimeedy & Theyazn H. H. Aldhyani & Mashael Maashi & Hasan Alkahtani, 2022. "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, MDPI, vol. 14(10), pages 1-89, May.
    7. Xu, Yanyan & Xue, Yanqin & Qi, Hong & Cai, Weihua, 2021. "An updated review on working fluids, operation mechanisms, and applications of pulsating heat pipes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. Evgeny Burnaev & Evgeny Mironov & Aleksei Shpilman & Maxim Mironenko & Dmitry Katalevsky, 2023. "Practical AI Cases for Solving ESG Challenges," Sustainability, MDPI, vol. 15(17), pages 1-15, August.
    9. Ye, Fei & Ouyang, You & Li, Yina, 2023. "Digital investment and environmental performance: The mediating roles of production efficiency and green innovation," International Journal of Production Economics, Elsevier, vol. 259(C).
    10. Chen, Sirui & Li, Peng & Ji, Haoran & Yu, Hao & Yan, Jinyue & Wu, Jianzhong & Wang, Chengshan, 2021. "Operational flexibility of active distribution networks with the potential from data centers," Applied Energy, Elsevier, vol. 293(C).
    11. Xiaohuan Zhao & Yue Zhu & Hailiang Li, 2022. "Micro-Channel Oscillating Heat Pipe Energy Conversion Approach of Battery Heat Dissipation Improvement: A Review," Energies, MDPI, vol. 15(19), pages 1-29, October.
    12. Dwivedi, Yogesh K. & Hughes, Laurie & Kar, Arpan Kumar & Baabdullah, Abdullah M. & Grover, Purva & Abbas, Roba & Andreini, Daniela & Abumoghli, Iyad & Barlette, Yves & Bunker, Deborah & Chandra Kruse,, 2022. "Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action," International Journal of Information Management, Elsevier, vol. 63(C).
    13. Han, Hua & Cui, Xiaoyu & Zhu, Yue & Xu, Tianxiao & Sui, Yuan & Sun, Shende, 2016. "Experimental study on a closed-loop pulsating heat pipe (CLPHP) charged with water-based binary zeotropes and the corresponding pure fluids," Energy, Elsevier, vol. 109(C), pages 724-736.
    14. Gilbert Fridgen & Marc-Fabian Körner & Steffen Walters & Martin Weibelzahl, 2021. "Not All Doom and Gloom: How Energy-Intensive and Temporally Flexible Data Center Applications May Actually Promote Renewable Energy Sources," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(3), pages 243-256, June.
    15. Ramezanizadeh, Mahdi & Ahmadi, Mohammad Hossein & Nazari, Mohammad Alhuyi & Sadeghzadeh, Milad & Chen, Lingen, 2019. "A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    16. Ce Chi & Kaixuan Ji & Penglei Song & Avinab Marahatta & Shikui Zhang & Fa Zhang & Dehui Qiu & Zhiyong Liu, 2021. "Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-32, April.
    17. Etienne Romsom & Kathryn McPhail, 2020. "The energy transition in Asia: Country priorities, fuel types, and energy decisions," WIDER Working Paper Series wp-2020-48, World Institute for Development Economic Research (UNU-WIDER).
    18. Gilmore, Nicholas & Koskinen, Ilpo & van Gennip, Domenique & Paget, Greta & Burr, Patrick A. & Obbard, Edward G. & Daiyan, Rahman & Sproul, Alistair & Kay, Merlinde & Lennon, Alison & Konstantinou, Ge, 2022. "Clean energy futures: An Australian based foresight study," Energy, Elsevier, vol. 260(C).
    19. Jiyong Park & Kunsoo Han & Byungtae Lee, 2023. "Green Cloud? An Empirical Analysis of Cloud Computing and Energy Efficiency," Management Science, INFORMS, vol. 69(3), pages 1639-1664, March.
    20. Xu Wang & Pallav Purohit, 2022. "Transitioning to low-GWP alternatives with enhanced energy efficiency in cooling non-residential buildings of China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(7), pages 1-28, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:1970-:d:766671. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.