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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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.
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