Bluff Body Size Parameters and Vortex Flowmeter Performance: A Big Data-Based Modeling and Machine Learning Methodology

This study investigates the correlation between bluff body parameters and vortex flowmeter performance through big data modeling and machine learning techniques. Vortex flowmeters are widely used in industry due to their high accuracy and minimal pressure loss. Nonetheless, optimizing their design remains challenging due to the complex relationship between input and output parameters. Symmetry in bluff body design is crucial for vortex formation and stability. In this study, Latin Hypercube Sampling (LHS) was employed to generate 10,000 symmetry bluff bodies, and efficient serial simulations were conducted using Ansys Fluent, significantly reducing computational costs compared to traditional CFD methods. A regression model was developed using scikit-learn to map eight geometric parameters to eight performance indicators, achieving excellent fitting accuracy with residuals far smaller than the simulation accuracy of ANSYS Fluent. Through Grey Relational Analysis (GRA), objective function analysis, and in conjunction with CFD contour maps, this study has analyzed the relationships between input and output parameters and their impact on the Karman vortex street. This work has significantly improved the speed of big data collection and provided a solid theoretical foundation for data-driven optimization through big data analysis. In addition, the improvement of existing machine learning methods has achieved high-precision prediction and system parameter optimization, promoting the design of vortex flowmeters.