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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
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    References listed on IDEAS

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    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.
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    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.

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