Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine
In this work, a visualization experiment for rectangular channels was carried out to explore gas–liquid two-phase flow characteristics. Typical flow patterns, including bubble, elastic and mixed flows, were captured by direct imaging technology and the corresponding measurements with fluctuation cha...
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| Format: | Article |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/1907 |
| _version_ | 1797618254613577728 |
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| author | Kai Yang Jiajia Liu Min Wang Hua Wang Qingtai Xiao |
| author_facet | Kai Yang Jiajia Liu Min Wang Hua Wang Qingtai Xiao |
| author_sort | Kai Yang |
| collection | DOAJ |
| description | In this work, a visualization experiment for rectangular channels was carried out to explore gas–liquid two-phase flow characteristics. Typical flow patterns, including bubble, elastic and mixed flows, were captured by direct imaging technology and the corresponding measurements with fluctuation characteristics were recorded by using an electrical conductivity sensor. Time-domain and frequency-domain characteristics of the corresponding electrical conductivity measurements of each flow pattern were analyzed with a probability density function and a power spectral density curve. The results showed that the feature vectors can be constructed to reflect the time–frequency characteristics of conductivity measurements successfully by introducing the quantized characteristic parameters, including the maximum power of the frequency, the standard deviation of the power spectral density, and the range of the power distribution. Furthermore, the overall recognition rate of the four flow patterns measured by the method was 93.33% based on the support vector machine, and the intelligent two-phase flow-pattern identification method can provide a new technical support for the online recognition of gas–liquid two-phase flow patterns in rectangular channels. It may thus be concluded that this method should be of great significance to ensure the safe and efficient operation of relevant industrial production systems. |
| first_indexed | 2024-03-11T08:10:31Z |
| format | Article |
| id | doaj.art-158251ccfca34034b89f39cb2610e780 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:10:31Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-158251ccfca34034b89f39cb2610e7802023-11-16T23:07:43ZengMDPI AGSensors1424-82202023-02-01234190710.3390/s23041907Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector MachineKai Yang0Jiajia Liu1Min Wang2Hua Wang3Qingtai Xiao4State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming 650093, ChinaState Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming 650093, ChinaDepartment of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, TX 78249-0634, USAState Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming 650093, ChinaState Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming 650093, ChinaIn this work, a visualization experiment for rectangular channels was carried out to explore gas–liquid two-phase flow characteristics. Typical flow patterns, including bubble, elastic and mixed flows, were captured by direct imaging technology and the corresponding measurements with fluctuation characteristics were recorded by using an electrical conductivity sensor. Time-domain and frequency-domain characteristics of the corresponding electrical conductivity measurements of each flow pattern were analyzed with a probability density function and a power spectral density curve. The results showed that the feature vectors can be constructed to reflect the time–frequency characteristics of conductivity measurements successfully by introducing the quantized characteristic parameters, including the maximum power of the frequency, the standard deviation of the power spectral density, and the range of the power distribution. Furthermore, the overall recognition rate of the four flow patterns measured by the method was 93.33% based on the support vector machine, and the intelligent two-phase flow-pattern identification method can provide a new technical support for the online recognition of gas–liquid two-phase flow patterns in rectangular channels. It may thus be concluded that this method should be of great significance to ensure the safe and efficient operation of relevant industrial production systems.https://www.mdpi.com/1424-8220/23/4/1907gas–liquidflow patternrectangular channelconductivitysupport vector machine |
| spellingShingle | Kai Yang Jiajia Liu Min Wang Hua Wang Qingtai Xiao Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine Sensors gas–liquid flow pattern rectangular channel conductivity support vector machine |
| title | Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine |
| title_full | Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine |
| title_fullStr | Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine |
| title_full_unstemmed | Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine |
| title_short | Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine |
| title_sort | identifying flow patterns in a narrow channel via feature extraction of conductivity measurements with a support vector machine |
| topic | gas–liquid flow pattern rectangular channel conductivity support vector machine |
| url | https://www.mdpi.com/1424-8220/23/4/1907 |
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