Optimization of Structural Parameters and Cavitation Suppression in Control Valves Based on P-WOA

Li, W.; Li, S.; Hou, J.; Yang, L.; Tian, Y.

doi:10.47176/jafm.18.8.3310

Optimization of Structural Parameters and Cavitation Suppression in Control Valves Based on P-WOA

Document Type : Regular Article

Authors

¹ School of Petrochemical Engineering, Lanzhou University of Technology, Lanzhou 730050, PR China

² Machinery Industry Pump Special Valve Engineering Research Center, Lanzhou 730050, PR China

10.47176/jafm.18.8.3310

Abstract

A control valve is a critical component in water supply systems, controlling pressure, flow, and direction. However, cavitation, caused by low pressure in the valve cavity, disrupts flow and leads to cavitation damage, vibration, and noise, affecting valve reliability and system stability. In this study an optimization method for controlling valve structural parameters is proposed to reduce cavitation-induced flow resistance and enhance flow capacity. Using the orifice throttling principle, the continuity equation, and Bernoulli's equation, the relationship between cavitation-induced resistance and flow rate is analyzed. Numerical calculations reveal that cavitation is most severe at a 40% valve opening, which is further studied. Boosting method integrates reinforcement learning PPO with the whale optimization algorithm (WOA) to form the P-WOA model. SOLIDWORKS and CFD software are used for parametric modeling, and a control valve structural parameter database is created using Latin hypercube sampling. The database is input into the P-WOA model for training to find optimal valve parameters. These solutions are then globally optimized by the WOA. The simulation and experimental results show that the P-WOA-optimized valve parameters significantly reduce cavitation (the gas volume fraction is reduced by 99.8% compared with the original and 59.6% compared with the PPO optimization) and improve the flow capacity. This proves the effectiveness of the P-WOA model and provides a new structural optimization solution for reducing cavitation in engineering.

Keywords

Main Subjects

Artificial Intelligence in Fluid Mechanics

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