The DOI matches its Crossref record.
A model for predicting flow regime transitions in horizontal and near horizontal gas‐liquid flow
Abstract
Models are presented for determining flow regime transitions in two‐phase gas‐liquid flow. The mechanisms for transition are based on physical concepts and are fully predictive in that no flow regime transitions are used in their development. A generalized flow regime map based on this theory is presented.
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Citations
Cited by
Ong, C. L., & Thome, J. R. (2011) · Experimental Thermal and Fluid Science · doi:10.1016/j.expthermflusci.2010.08.004 · Cited by 365
Two-Phase Flow Patterns and Flow-Pattern Maps: Fundamentals and Applications
Cheng, L., Ribatski, G., & Thome, J. R. (2008) · Applied Mechanics Reviews · doi:10.1115/1.2955990 · Cited by 316
Sakamoto, Y., Kobayashi, H., Naruo, Y., Takesaki, Y., Nakajima, Y., Kabayama, K., & Sato, T. (2021) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2020.12.038 · Cited by 12
Trincado, R., Ortega, I., & Sieres, J. (2026) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2026.132709
Metadata
- Authors
- Taitel, Y., & Dukler, A. E.
- Venue
- AIChE Journal
- Publisher
- Wiley
- Published
- 1976-01
- Volume
- 22
- Issue
- 1
- Pages
- 47-55
- ISSN
- 0001-1541 (ISSN-L)
- 1547-5905
- OpenAlex
- W2076422580
- Cited by
- 2,915
- References
- 0
- FWCI
- 19.48
- Type
- journal article
- Language
- en
- Open access
- closed
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: A model for predicting flow regime transitions in horizontal and near horizontal gas‐liquid flow. https://app.cochat.ai/papers/10.1002/aic.690220105
- The paper
- Taitel, Y., & Dukler, A. E. (1976). A model for predicting flow regime transitions in horizontal and near horizontal gas‐liquid flow. AIChE Journal. https://doi.org/10.1002/aic.690220105
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
