1. Artificial Neural Networks for Gas‐Liquid Flow Regime Classification in Small Channels.
- Author
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Haase, Stefan, May, Henry, Hiller, Andreas, and Schubert, Markus
- Subjects
- *
ARTIFICIAL neural networks , *ANNULAR flow , *CLASSIFICATION - Abstract
The reliable design of multiphase micro‐structured apparatus requires a precise knowledge of the internal flow regime. Previous research indicated that classifiers based on artificial neural networks (ANN) are relatively simple to develop and provide a reasonable accuracy when trained with data for specific inlet designs. This paper introduces advanced ANN classifiers capable of predicting all relevant flow regimes regardless of the inlet design with a recall of 94 % and above for Taylor, churn, dispersed, rivulet, and parallel flows, between 89 % and 94 % for annular and bubbly flows, and 83 % for Taylor‐annular flow. These classifiers were trained and validated by using more than 13,000 experimental data points extracted from 97 flow maps. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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