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

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.