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. Erik Champion & Hafizur Rahaman, 2019. "3D Digital Heritage Models as Sustainable Scholarly Resources," Sustainability, MDPI, vol. 11(8), pages 1-8, April.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. Stefano Bianchini & Giacomo Damioli & Claudia Ghisetti, 2023. "The environmental effects of the “twin” green and digital transition in European regions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 877-918, April.
    9. 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).
    10. Spinato, Giulia & Borhani, Navid & Thome, John R., 2015. "Understanding the self-sustained oscillating two-phase flow motion in a closed loop pulsating heat pipe," Energy, Elsevier, vol. 90(P1), pages 889-899.
    11. 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.
    12. Pier Giacomo Cardinali & Pietro De Giovanni, 2022. "Responsible digitalization through digital technologies and green practices," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 29(4), pages 984-995, July.
    13. Khanjari, Ali & Mahmoodi, Esmail & Ahmadi, Mohammad Hossien, 2020. "Energy and exergy analyzing of a wind turbine in free stream and wind tunnel in CFD domain based on actuator disc technique," Renewable Energy, Elsevier, vol. 160(C), pages 231-249.
    14. Khokhriakov, Semyon & Manumachu, Ravi Reddy & Lastovetsky, Alexey, 2020. "Multicore processor computing is not energy proportional: An opportunity for bi-objective optimization for energy and performance," Applied Energy, Elsevier, vol. 268(C).
    15. Saket Kaushal & A. Aadhi & Anthony Roberge & Roberto Morandotti & Raman Kashyap & José Azaña, 2023. "All-fibre phase filters with 1-GHz resolution for high-speed passive optical logic processing," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    16. Bourgeois Guillaume & Duthil Benjamin & Courboulay Vincent, 2022. "Review of the Impact of IT on the Environment and Solution with a Detailed Assessment of the Associated Gray Literature," Sustainability, MDPI, vol. 14(4), pages 1-19, February.
    17. Ji, Haoran & Chen, Sirui & Yu, Hao & Li, Peng & Yan, Jinyue & Song, Jieying & Wang, Chengshan, 2022. "Robust operation for minimizing power consumption of data centers with flexible substation integration," Energy, Elsevier, vol. 248(C).
    18. Solène Guenat & Phil Purnell & Zoe G. Davies & Maximilian Nawrath & Lindsay C. Stringer & Giridhara Rathnaiah Babu & Muniyandi Balasubramanian & Erica E. F. Ballantyne & Bhuvana Kolar Bylappa & Bei Ch, 2022. "Meeting sustainable development goals via robotics and autonomous systems," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    19. Li, Jian & Jurasz, Jakub & Li, Hailong & Tao, Wen-Quan & Duan, Yuanyuan & Yan, Jinyue, 2020. "A new indicator for a fair comparison on the energy performance of data centers," Applied Energy, Elsevier, vol. 276(C).
    20. Andrea A. Cortinois & Anne‐Emanuelle Birn, 2021. "What’s Technology Got to Do With It? Power, Politics, and Health Equity Beyond Technological Triumphalism," Global Policy, London School of Economics and Political Science, vol. 12(S6), pages 75-79, July.

    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.