Identification of Upward Two-Phase Flow Regime Transition Region under High-Pressure Condition Using Deep Learning-Reference-Cited by-同舟云学术

Identification of Upward Two-Phase Flow Regime Transition Region under High-Pressure Condition Using Deep Learning

Published:2021-03-15 Issue:1 Volume:35 Page:93-100
ISSN:0914-2843
Container-title:JAPANESE JOURNAL OF MULTIPHASE FLOW
language:en
Short-container-title:JAPANESE JOURNAL OF MULTIPHASE FLOW

Author:

SHIBATA Naoto,MIWA Shuichiro,SAWA Kazuhiro,TAKAHASHI Masahiro,MURAYAMA Tetsuro,TENMA Norio

Publisher

The Japanese Society for Multiphase Flow

Subject

General Medicine

Link

https://www.jstage.jst.go.jp/article/jjmf/35/1/35_2021.014/_pdf

Reference11 articles.

1. [1] Zhou, Y., Chen, F. and Sun, B., Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine. Chinese Journal of Chemical Engineering, Vol. 16, 832-840 (2008).

2. [2] Shanthi, C. and Pappa, N., An Artificial Intelligence Based Improved Classification of Two-Phase Flow Patterns with Feature Extracted from Acquired Images. ISA Transactions, Vol. 68, 425-432 (2017).

3. [3] Kabayama, K., Minote, K., Yoshida, K., Sakamoto, Y., Tane, S., Nakajima, Y., Kobayashi, H. and Sato, T., Study on Flow Pattern Discrimination of Gas-Liquid Two-Phase Flow. Space Transportation Symposium FY2017, Paper No. 029.

4. [4] Sasaki, T., Miwa, S. and Sawa, K., Study of Two-Phase Flow Regime Identification Using Image Recognition. Multiphase Flow Symposium 2019, Paper No. D232.

5. [5] Hu, M., Visual Pattern Recognition by Moment Invariants. IRE Transactions on Information Theory, Vol. 8, 179-187, 1057692(1962).

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