@ARTICLE{Hanus_Robert_Evaluation_2021,
 author={Hanus, Robert and Zych, Marcin and Mosorov, Volodymyr and Golijanek-Jędrzejczyk, Anna and Jaszczur, Marek and Andruszkiewicz, Artur},
 volume={vol. 28},
 number={No 1},
 journal={Metrology and Measurement Systems},
 pages={145-159},
 howpublished={online},
 year={2021},
 publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation},
 abstract={Liquid-gas flows in pipelines appear in many industrial processes, e.g. in the nuclear, mining, and oil industry. The gamma-absorption technique is one of the methods that can be successfully applied to study such flows. This paper presents the use of the gamma-absorption method to determine the water-air flow parameters in a horizontal pipeline. Three flow types were studied in this work: plug, transitional plug-bubble, and bubble one. In the research, a radiometric set consisting of two Am-241 sources and two NaI(TI) scintillation detectors have been applied. Based on the analysis of the signals from both scintillation detectors, the gas phase velocity was calculated using the cross-correlation method (CCM). The signal from one detector was used to determine the void fraction and to recognise the flow regime. In the latter case, a Multi-Layer Perceptron-type artificial neural network (ANN) was applied. To reduce the number of signal features, the principal component analysis (PCA) was used. The expanded uncertainties of gas velocity and void fraction obtained for the flow types studied in this paper did not exceed 4.3% and 7.4% respectively. All three types of analyzed flows were recognised with 100% accuracy. Results of the experiments confirm the usefulness of the gamma-ray absorption method in combination with radiometric signal analysis by CCM and ANN with PCA for comprehensive analysis of liquid-gas flow in the pipeline.},
 type={Article},
 title={Evaluation of liquid-gas flow in pipeline using gamma-ray absorption technique and advanced signal processing},
 URL={http://www.czasopisma.pan.pl/Content/118926/PDF/art09.pdf},
 doi={10.24425/mms.2021.135997},
 keywords={two-phase flow, void fraction, gamma-ray absorption, flow regime identification, artificial neural network},
}