IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i4p968-d323449.html
   My bibliography  Save this article

Surrogate Model with a Deep Neural Network to Evaluate Gas–Liquid Flow in a Horizontal Pipe

Author

Listed:
  • Yongho Seong

    (Department of Energy and Resources Engineering, Kangwon National University, Chuncheon, Kangwon 24341, Korea)

  • Changhyup Park

    (Department of Energy and Resources Engineering, Kangwon National University, Chuncheon, Kangwon 24341, Korea)

  • Jinho Choi

    (Naval & Energy System R&D Institute, Daewoo Shipbuilding & Marine Engineering Co., Ltd., Siheung, Gyeonggi 15011, Korea)

  • Ilsik Jang

    (Department of Energy and Resources Engineering, Chosun University, Gwangju 61425, Korea)

Abstract

This study developed a data-driven surrogate model based on a deep neural network (DNN) to evaluate gas–liquid multiphase flow occurring in horizontal pipes. It estimated the liquid holdup and pressure gradient under a slip condition and different flow patterns, i.e., slug, annular, stratified flow, etc. The inputs of the surrogate modelling were related to the fluid properties and the dynamic data, e.g., superficial velocities at the inlet, while the outputs were the liquid holdup and pressure gradient observed at the outlet. The case study determined the optimal number of hidden neurons by considering the processing time and the validation error. A total of 350 experimental data were used: 279 for supervised training, 31 for validating the training performance, and 40 unknown data, not used in training and validation, were examined to forecast the liquid holdup and pressure gradient. The liquid holdups were estimated within less than 8.08% of the mean absolute percentage error, while the error of the pressure gradient was 23.76%. The R 2 values confirmed the reliability of the developed model, showing 0.89 for liquid holdups and 0.98 for pressure gradients. The DNN-based surrogate model can be applicable to estimate liquid holdup and pressure gradients in a more realistic manner with a small amount of computating resources.

Suggested Citation

  • Yongho Seong & Changhyup Park & Jinho Choi & Ilsik Jang, 2020. "Surrogate Model with a Deep Neural Network to Evaluate Gas–Liquid Flow in a Horizontal Pipe," Energies, MDPI, vol. 13(4), pages 1-12, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:968-:d:323449
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/4/968/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/4/968/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jinho Choi & Eduardo Pereyra & Cem Sarica & Changhyup Park & Joe M. Kang, 2012. "An Efficient Drift-Flux Closure Relationship to Estimate Liquid Holdups of Gas-Liquid Two-Phase Flow in Pipes," Energies, MDPI, vol. 5(12), pages 1-13, December.
    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. Matthew E. Wilhelm & Chenyu Wang & Matthew D. Stuber, 2023. "Convex and concave envelopes of artificial neural network activation functions for deterministic global optimization," Journal of Global Optimization, Springer, vol. 85(3), pages 569-594, March.
    2. Suryeom Jo & Changhyup Park & Dong-Woo Ryu & Seongin Ahn, 2021. "Adaptive Surrogate Estimation with Spatial Features Using a Deep Convolutional Autoencoder for CO 2 Geological Sequestration," Energies, MDPI, vol. 14(2), pages 1-19, January.

    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. Tea-Woo Kim & Nam-Sub Woo & Sang-Mok Han & Young-Ju Kim, 2020. "Optimization and Extended Applicability of Simplified Slug Flow Model for Liquid-Gas Flow in Horizontal and Near Horizontal Pipes," Energies, MDPI, vol. 13(4), pages 1-27, February.
    2. Yaxin Liu & Eric R. Upchurch & Evren M. Ozbayoglu, 2021. "Experimental Study of Single Taylor Bubble Rising in Stagnant and Downward Flowing Non-Newtonian Fluids in Inclined Pipes," Energies, MDPI, vol. 14(3), pages 1-28, January.
    3. Ralph Eismann & Sebastian Hummel & Federico Giovannetti, 2021. "A Thermal-Hydraulic Model for the Stagnation of Solar Thermal Systems with Flat-Plate Collector Arrays," Energies, MDPI, vol. 14(3), pages 1-39, January.

    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:13:y:2020:i:4:p:968-:d:323449. 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.