Study on Flow Regime Classification of Gas-Liquid Two-Phase Flow with Bidirectional LSTM Networks-Reference-Cited by-同舟云学术

Study on Flow Regime Classification of Gas-Liquid Two-Phase Flow with Bidirectional LSTM Networks

Published:2022 Issue:0 Volume:21 Page:68-76
ISSN:1884-0477
Container-title:AEROSPACE TECHNOLOGY JAPAN, THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES
language:en
Short-container-title:AEROSPACE TECHNOLOGY JAPAN, THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES

Author:

Nakao Keigo¹,Sakano Yukari²,Sakamoto Yuki²,Kabayama Koki³,Inoue Yusuke⁴,Sato Tetsuya⁵

Affiliation:

1. 連絡先著者（Corresponding author）：n-keitora83@ruri.waseda. jp

2. 宇宙航空研究開発機構

3. 現日本郵船（株）

4. 現東京ガス（株）

5. 早稲田大学

Publisher

Japan Society for Aeronautical and Space Sciences

Subject

Ocean Engineering

Link

https://www.jstage.jst.go.jp/article/astj/21/0/21_JSASS-D-21-00052/_pdf

Reference26 articles.

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2. 2) Ezzatabadipour, M., Singh, P., Robinson, M. D., Guillen-Rondon, P. and Torres, C.: Deep Learning as a Tool to Predict Flow Patterns in Two-Phase Flow, arXiv:1705.07117 [cs.LG], 2017.

3. 3) Hu, D., Li, J., Liu, Y. and Li, Y.: Flow Adversarial Networks: Flowrate Prediction for Gas-Liquid Multiphase Flows Across Different Domains, IEEE Transactions on Neural Networks and Learning Systems, 31 (2019), pp. 475-487.

4. 4) Lecun, Y., Bottu, L., Bengio, Y. and Hffiner, P.: Gradient-based Learning Applied to Document Recognition, Proceedings of the IEEE, 86, 11 (1998), pp. 2278-2324.

5. 5) Krizhevsky, A., Sutskever, I. and Hinton, G. E.: ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems, 25 (2012), pp. 1097-1105.