IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i13p5185-d1187523.html
   My bibliography  Save this article

A Novel Approach for Reducing Feature Space Dimensionality and Developing a Universal Machine Learning Model for Coated Tubes in Cross-Flow Heat Exchangers

Author

Listed:
  • Mahyar Jahaninasab

    (Advanced Simulation and Computing Laboratory (ASCL), Imam Khomeini International University, Qazvin 34148-96818, Iran)

  • Ehsan Taheran

    (Advanced Simulation and Computing Laboratory (ASCL), Imam Khomeini International University, Qazvin 34148-96818, Iran)

  • S. Alireza Zarabadi

    (Advanced Simulation and Computing Laboratory (ASCL), Imam Khomeini International University, Qazvin 34148-96818, Iran)

  • Mohammadreza Aghaei

    (Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway
    Department of Sustainable Systems Engineering (INATECH), University of Freiburg, 79110 Freiburg im Breisgau, Germany)

  • Ali Rajabpour

    (Advanced Simulation and Computing Laboratory (ASCL), Imam Khomeini International University, Qazvin 34148-96818, Iran)

Abstract

In the thermal industry, one common way to transfer heat between hot tubes and cooling fluid is using cross-flow heat exchangers. For heat exchangers, microscale coatings are conventional safeguards for tubes from corrosion and dust accumulation. This study presents the hypothesis that incorporating domain knowledge based on governing equations can be beneficial for developing machine learning models for CFD results, given the available data. Additionally, this work proposes a novel approach for combining variables in heat exchangers and building machine learning models to forecast heat transfer in heat exchangers for turbulent flow. To develop these models, a dataset consisting of nearly 1000 cases was generated by varying different variables. The simulation results obtained from our study confirm that the proposed method would improve the coefficient of determination (R-squared) for trained models in unseen datasets. For the unseen data, the R-squared values for random forest, K-Nearest Neighbors, and support vector regression were determined to be 0.9810, 0.9037, and 0.9754, respectively. These results indicate the effectiveness and utility of our proposed model in predicting heat transfer in various types of heat exchangers.

Suggested Citation

  • Mahyar Jahaninasab & Ehsan Taheran & S. Alireza Zarabadi & Mohammadreza Aghaei & Ali Rajabpour, 2023. "A Novel Approach for Reducing Feature Space Dimensionality and Developing a Universal Machine Learning Model for Coated Tubes in Cross-Flow Heat Exchangers," Energies, MDPI, vol. 16(13), pages 1-13, July.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5185-:d:1187523
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/13/5185/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/13/5185/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    2. Kiriakos Alexiou & Efthimios G. Pariotis & Helen C. Leligou & Theodoros C. Zannis, 2022. "Towards Data-Driven Models in the Prediction of Ship Performance (Speed—Power) in Actual Seas: A Comparative Study between Modern Approaches," Energies, MDPI, vol. 15(16), pages 1-18, August.
    3. Amira Mohamed & Hatem Ibrahem & Rui Yang & Kibum Kim, 2022. "Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning," Energies, MDPI, vol. 15(18), pages 1-15, September.
    4. Krystian Góra & Paweł Smyczyński & Mateusz Kujawiński & Grzegorz Granosik, 2022. "Machine Learning in Creating Energy Consumption Model for UAV," Energies, MDPI, vol. 15(18), pages 1-19, September.
    5. Anna Matveeva & Aleksey Bychkov, 2022. "How to Train an Artificial Neural Network to Predict Higher Heating Values of Biofuel," Energies, MDPI, vol. 15(19), pages 1-13, September.
    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. Yajing Tian & Yuyang Wang & Shasha Yin & Jia Lu & Yu Hu, 2024. "Enabling High-Degree-of-Freedom Thermal Engineering Calculations via Lightweight Machine Learning," Energies, MDPI, vol. 17(16), pages 1-18, August.

    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. Binh Thai Pham & Chongchong Qi & Lanh Si Ho & Trung Nguyen-Thoi & Nadhir Al-Ansari & Manh Duc Nguyen & Huu Duy Nguyen & Hai-Bang Ly & Hiep Van Le & Indra Prakash, 2020. "A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    2. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    3. Jung-sik Hong & Hyeongyu Yeo & Nam-Wook Cho & Taeuk Ahn, 2018. "Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning," JRFM, MDPI, vol. 11(4), pages 1-13, October.
    4. Mohamed Zine & Fouzi Harrou & Mohammed Terbeche & Mohammed Bellahcene & Abdelkader Dairi & Ying Sun, 2023. "E-Learning Readiness Assessment Using Machine Learning Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
    5. repec:hal:spmain:info:hdl:2441/13thfd12aa8rmplfudlgvgahff is not listed on IDEAS
    6. Chen, Enhui & Stathopoulos, Amanda & Nie, Yu (Marco), 2022. "Transfer station choice in a multimodal transit system: An empirical study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 337-355.
    7. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    8. Lotfi Boudabsa & Damir Filipovi'c, 2022. "Ensemble learning for portfolio valuation and risk management," Papers 2204.05926, arXiv.org.
    9. Lorilla, Roxanne Suzette & Poirazidis, Konstantinos & Detsis, Vassilis & Kalogirou, Stamatis & Chalkias, Christos, 2020. "Socio-ecological determinants of multiple ecosystem services on the Mediterranean landscapes of the Ionian Islands (Greece)," Ecological Modelling, Elsevier, vol. 422(C).
    10. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1535-1546, June.
    11. Zeynep Ceylan & Abdulkadir Atalan, 2021. "Estimation of healthcare expenditure per capita of Turkey using artificial intelligence techniques with genetic algorithm‐based feature selection," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 279-290, March.
    12. Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
    13. Ilias Thomas & Alex M. Dickens & Jussi P. Posti & Endre Czeiter & Daniel Duberg & Tim Sinioja & Matilda Kråkström & Isabel R. A. Retel Helmrich & Kevin K. W. Wang & Andrew I. R. Maas & Ewout W. Steyer, 2022. "Serum metabolome associated with severity of acute traumatic brain injury," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    14. Lu, Xuefei & Baraldi, Piero & Zio, Enrico, 2020. "A data-driven framework for identifying important components in complex systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    15. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    16. Amini, Shahram & Elmore, Ryan & Öztekin, Özde & Strauss, Jack, 2021. "Can machines learn capital structure dynamics?," Journal of Corporate Finance, Elsevier, vol. 70(C).
    17. Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.
    18. Gilletly, Samuel D. & Jackson, Nicole D. & Staid, Andrea, 2023. "Evaluating the impact of wildfire smoke on solar photovoltaic production," Applied Energy, Elsevier, vol. 348(C).
    19. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    20. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    21. repec:hal:spmain:info:hdl:2441/20hflp7eqn97boh50no50tv67n is not listed on IDEAS
    22. Junqi Wang & Rundong Liu & Linfeng Zhang & Hussain Syed ASAD & Erlin Meng, 2019. "Triggering Optimal Control of Air Conditioning Systems by Event-Driven Mechanism: Comparing Direct and Indirect Approaches," Energies, MDPI, vol. 12(20), pages 1-20, 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:16:y:2023:i:13:p:5185-:d:1187523. 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.