Abstract
Two objective approaches for characterizing the interfacial structures and identifying flow regimes in two-phase flow are presented. The first approach is based on the use of a multi-sensor probe technique to get detailed local information. Performance of a newly designed four-sensor conductivity probe was investigated in a vertical test section with 50.8 mm i.d. The experimental data is categorized into two groups in view of their interfacial structures. Local information on the void fraction, interfacial area concentration. Sauter mean diameter, interface velocity, etc., was obtained successfully. The experimental data demonstrate well the characteristics of the two groups of bubbles. The second approach is based on the application of a non-intrusive impedance void-meter and neural networks. An advanced non-intrusive impedance void-meter provides input signals to neural networks which are used to identify vertical flow regimes. Both supervised and self-organizing neural network learning paradigms performed flow regime identification successfully. The methodology presented holds considerable promise for multiphase flow diagnostic and measurement applications.
Original language | English (US) |
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Pages (from-to) | 205-212 |
Number of pages | 8 |
Journal | American Society of Mechanical Engineers, Heat Transfer Division, (Publication) HTD |
Volume | 361-5 |
State | Published - 1998 |
Event | Proceedings of the 1998 ASME International Mechanical Engineering Congress and Exposition - Anaheim, CA, USA Duration: Nov 15 1998 → Nov 20 1998 |
All Science Journal Classification (ASJC) codes
- Mechanical Engineering
- Fluid Flow and Transfer Processes