Local flow analysis and management for digital twins of control valves

Aliyu, Qingyun, Aliyu, Aliyu, Mishra, Rakesh and , (2021) Local flow analysis and management for digital twins of control valves. In: International Conference on Maintenance and Intelligent Asset Management (ICMIAM), December 2021, Ballarat, Australia.

Full content URL: https://doi.org/10.1109/ICMIAM54662.2021.9715225

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


Since about 2002, Industry 4.0, and Digital Twin (DT) research as we know it today, enjoyed increasing attention in academia and industry. The research on the DTs of control valves is currently not as mature as in other critical fields. It is however of utmost importance given the role control valves play in the process, nuclear, and petroleum industries. As a common and essential part of the pipeline system, valves are used to regulate fluid flow within pipelines to achieve a desired flow condition. However, the local flow characteristics within the valve domain are difficult to determine experimentally because of complex geometry and inaccessibility of the flow domain. The application of DT technology in pipelines allows real-time fluid flow data transmission both at global and local levels, and performance monitoring of the system as well as for predicting remaining useful life. Big data analytics and machine learning can be leveraged to improve valve system performance prediction as well as to improve safety. This paper describes initial work carried out on developing a control valve digital twin which incorporates tools for monitoring local flow conditions in the control valve. Computational Fluid Dynamics (CFD) is used to determine the internal characteristics of the fluid inside the valve, and the data are analysed and managed through the development of a DT of the valve system.

Keywords:Control valves, Fluid mechanics, Digital Twins
Subjects:H Engineering > H800 Chemical, Process and Energy Engineering
H Engineering > H300 Mechanical Engineering
Divisions:College of Science > School of Engineering
ID Code:48394
Deposited On:07 Mar 2022 11:53

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