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Leakage identification and correlation coefficient method for industrial workshop production process combining with computational fluid dynamics
https://orcid.org/0000-0001-5673-7384
https://orcid.org/0000-0002-1929-3128
https://orcid.org/0000-0003-1285-2334
Identifying leakage sources in industrial factory production is crucial to improving air quality, ensuring people’s health and safety and preventing safety accidents. In this study, a method for leakage source identification in industrial factories combining with computational fluid dynamics (CFD) and correlation coefficient was proposed and validated. The study first experimentally validated the numerical methods, which were fundamental to the leakage identification method. Then impacts of leakage sources, sensor errors and number of sensors on the source identification results were evaluated. The results showed that the identification accuracy could be significantly improved by refining the step size of the coefficient ar in this method. When the number of leakage sources was unknown, the accuracy of this method in identifying the number and location of leakages was 93.5%. The computation time spent on source identification depended on the maximum number of leakage sources. Using four sensors with errors were enough to identify the number of unknown leakage sources. The number of leakage sources did not exceed three at the same time. Overall, coupled CFD and correlation coefficients method could effectively identify the number, location and intensity of leakages.
Leakage identification and correlation coefficient method for industrial workshop production process combining with computational fluid dynamics
https://orcid.org/0000-0001-5673-7384
https://orcid.org/0000-0002-1929-3128
https://orcid.org/0000-0003-1285-2334
Identifying leakage sources in industrial factory production is crucial to improving air quality, ensuring people’s health and safety and preventing safety accidents. In this study, a method for leakage source identification in industrial factories combining with computational fluid dynamics (CFD) and correlation coefficient was proposed and validated. The study first experimentally validated the numerical methods, which were fundamental to the leakage identification method. Then impacts of leakage sources, sensor errors and number of sensors on the source identification results were evaluated. The results showed that the identification accuracy could be significantly improved by refining the step size of the coefficient ar in this method. When the number of leakage sources was unknown, the accuracy of this method in identifying the number and location of leakages was 93.5%. The computation time spent on source identification depended on the maximum number of leakage sources. Using four sensors with errors were enough to identify the number of unknown leakage sources. The number of leakage sources did not exceed three at the same time. Overall, coupled CFD and correlation coefficients method could effectively identify the number, location and intensity of leakages.
Leakage identification and correlation coefficient method for industrial workshop production process combining with computational fluid dynamics
https://orcid.org/0000-0001-5673-7384
https://orcid.org/0000-0002-1929-3128
https://orcid.org/0000-0003-1285-2334
Identifying leakage sources in industrial factory production is crucial to improving air quality, ensuring people’s health and safety and preventing safety accidents. In this study, a method for leakage source identification in industrial factories combining with computational fluid dynamics (CFD) and correlation coefficient was proposed and validated. The study first experimentally validated the numerical methods, which were fundamental to the leakage identification method. Then impacts of leakage sources, sensor errors and number of sensors on the source identification results were evaluated. The results showed that the identification accuracy could be significantly improved by refining the step size of the coefficient ar in this method. When the number of leakage sources was unknown, the accuracy of this method in identifying the number and location of leakages was 93.5%. The computation time spent on source identification depended on the maximum number of leakage sources. Using four sensors with errors were enough to identify the number of unknown leakage sources. The number of leakage sources did not exceed three at the same time. Overall, coupled CFD and correlation coefficients method could effectively identify the number, location and intensity of leakages.
Leakage identification and correlation coefficient method for industrial workshop production process combining with computational fluid dynamics
Wang, Yukun (Autor:in) / Liu, Fei (Autor:in) / Long, Zhengwei (Autor:in) / Liu, Wei (Autor:in)
Indoor and Built Environment ; 34 ; 192-209
01.01.2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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