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Identification of Gas-Liquid Flow Regimes Using a Non-intrusive Doppler Ultrasonic Sensor and Virtual Flow Regime Maps

Nnabuifea, Somtochukwu Godfrey, Pilario, Karl Ezra S., Lao, Liyun, Cao, Yi, Shafiee, Mahmood (2019) Identification of Gas-Liquid Flow Regimes Using a Non-intrusive Doppler Ultrasonic Sensor and Virtual Flow Regime Maps. Flow Measurement and Instrumentation, 68 (101568). pp. 1-9. ISSN 0955-5986. (doi:10.1016/j.flowmeasinst.2019.05.002) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

The accurate prediction of flow regimes is vital for the analysis of behaviour and operation of gas/liquid two-phase systems in industrial processes. This paper investigates the feasibility of a non-radioactive and non-intrusive method for the objective identification of two-phase gas/liquid flow regimes using a Doppler ultrasonic sensor and machine learning approaches. The experimental data is acquired from a 16.2-m long S-shaped riser, connected to a 40-m horizontal pipe with an internal diameter of 50.4 mm. The tests cover the bubbly, slug, churn and annular flow regimes. The power spectral density (PSD) method is applied to the flow modulated ultrasound signals in order to extract frequency-domain features of the two-phase flow. Principal Component Analysis (PCA) is then used to reduce the dimensionality of the data so as to enable visualisation in the form of a virtual flow regime map. Finally, a support vector machine (SVM) is deployed to develop an objective classifier in the reduced space. The classifier attained 85.7% accuracy on training samples and 84.6% accuracy on test samples. Our approach has shown the success of the ultrasound sensor, PCA-SVM, and virtual flow regime maps for objective two-phase flow regime classification on pipeline-riser systems, which is beneficial to operators in industrial practice. The use of a non-radioactive and non-intrusive sensor also makes it more favorable than other existing techniques.

Item Type: Article
DOI/Identification number: 10.1016/j.flowmeasinst.2019.05.002
Uncontrolled keywords: Doppler shift; PCAS-shaped riser; Support Vector Machine (SVM)
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Depositing User: Mahmoud Shafiee
Date Deposited: 23 Jan 2020 17:13 UTC
Last Modified: 24 Jan 2020 12:12 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/79732 (The current URI for this page, for reference purposes)
Pilario, Karl Ezra S.: https://orcid.org/0000-0001-5448-0909
Cao, Yi: https://orcid.org/0000-0003-2360-1485
Shafiee, Mahmood: https://orcid.org/0000-0002-6122-5719
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