Underwater gas flow measurement based on adaptive passive acoustic characteristic frequency extraction. (31st August 2021)

• Record Type: Journal Article
• Authors: Zhang, Yu; Feng, Zhu; Rui, Xiaobo; Wang, Bingpu; Feng, Hao; Huang, Xinjing
• Is Part Of: Chemical engineering science. Volume 240(2021)
• Publication Date: 2021-08-31
• Subjects: CEEMD -- Adaptive spectrum matching -- Characteristic frequency extraction -- Flow measurement; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2021.116663
• Languages: English
• ISSNs: 0009-2509

Abstract

Highlights: Gas flow measurement with low signal-to-noise ratio and high-speed overlap. Selection of bubble oscillation mode by adaptive spectrum matching. Segmentation of overlapped signals by normalized energy window. Gas escape experiment at constant speed and variable speed. Abstract: The measurement of underwater escaping gas flow is of great significance to global climate change and industrial process state transition. The acoustic signals of a low signal-to-noise ratio and high-speed escape of gas are very complicated, and traditional filtering methods and the Minnaert equation are difficult to apply. In this article, an adaptive passive acoustic characteristic frequency extraction (APACFE) method is proposed. It can measure the flow under constant and variable speed conditions. The signal-to-noise ratio and measurement stability can be improved through complementary ensemble empirical mode decomposition and adaptive frequency matching. The normalized energy window is used to determine the time period of each bubble to ensure the accuracy of the frequency identification. The accuracy of APACFE was verified by simulation analysis and flow measurement experiments. The results show that the proposed APACFE method has better adaptability and can accurately measure the flow rate. Under constant speed conditions, the measurement error was controlled to within 1%. This study provides insight into low signal-to-noise ratio and high-speed escaping gas flow measurement technology and provides a reference for global climate change and industrial process state monitoring.