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

Yongho Seong, Changhyup Park, Jinho Choi and Ilsik Jang
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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

Energies, 2020, vol. 13, issue 4, 1-12

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.

Keywords: surrogate model; deep neural network; multiphase flow; horizontal pipe; liquid holdup; pressure gradient (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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