Abstract
In the two-phase flow measurements a method involving the absorption of gamma radiation can be applied among others. Analysis of the signals from the scintillation probes can be used to determine the number of flow parameters and to recognize flow structure. Three types of flow regimes as plug, bubble, and transitional plug – bubble flows were considered in this work. The article shows how features of the signals in the time and frequency domain can be used to build the artificial neural network (ANN) to recognize the structure of the gas-liquid flow in a horizontal pipeline. In order to reduce the number of signal features the principal component analysis (PCA) was used. It was found that the reduction of signals features allows for building a network with better performance.
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Details
- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Published in:
-
EPJ Web of Conferences
no. 143,
pages 1 - 4,
ISSN: 2100-014X - Title of issue:
- 11th International Conference on Experimental Fluid Mechanics (EFM) strony 1 - 4
- ISSN:
- 2100-014X
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Hanus R., Zych M., Petryka L., Świsulski D., Strzępowicz A..: Application of ANN and PCA to two-phase flow evaluation using radioisotopes, W: 11th International Conference on Experimental Fluid Mechanics (EFM), 2017, EDP Sciences,.
- DOI:
- Digital Object Identifier (open in new tab) 10.1051/epjconf/201714302033
- Verified by:
- Gdańsk University of Technology
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