Performance improvement of the self-power control valve based on digital twin technology

Jinyong Ju, Yudong Xie, Jiazhen Han, Yong Wang and Haibo Wang

Energy, 2024, vol. 300, issue C

Abstract: Providing stable energy for intelligent pipe network systems is a complex problem. Ball valves are widely used in intelligent pipe network systems, and a self-power control valve is designed by integrating a flow-matching wheel in the ball valve. While the spool is throttling the fluid, a part of the energy can be extracted from the fluid and converted into the power. In order to optimize the energy capturing characteristics of the self-power control valve, the shape of the flow-matching wheel must be designed to form a precise match with the valve cavity. In this paper, a lightweight digital twin of the self-power control valve is developed using an agent model. Based on this, an optimized design method for the flow-matching wheel that can dynamically take into account the operating conditions of the valve in real time, is proposed by combining the agent model with the dynamic data from multiple sensor sources. The research results show that after the twin optimization of the flow-matching wheel, the average value of the energy capturing efficiency is improved by 3.21 % compared to the pre-optimization; the average value of the output power is improved by 20.57 % compared to the pre-optimization.

Keywords: Digital twin; Optimization method; Self-power control valve; Flow-matching wheel (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422401380X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:300:y:2024:i:c:s036054422401380x

DOI: 10.1016/j.energy.2024.131607

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().