Global consistent graph convolutional network for amplifying piezoelectric microelectromechanical sensor performance in fluid-dynamic environments

Abstract

Microelectromechanical System (MEMS) sensors composed of a bluff body and a polyvinylidene fluoride (PVDF) flexible piezoelectric flag, are highly sensitive to variations in fluid dynamics. However, the sensing capability of these systems is limited by factors such as complex vortex formation and inaccurate turbulence classification, especially under varying fluid speeds and bluff body geometries. To address these challenges, the Global Consistent Graph Convolutional Network for Amplifying Sensing Capability of Piezoelectric Microelectromechanical System Sensors in a Fluid-Dynamic System (GCGCN-PMEMS-FDS) is proposed for accurate turbulence classification. The system first inputs wind or fluid speed, inducing mechanical vibrations in piezoelectric flag, which displaces charge and generates voltage signals. These signals are then processed using the Morlet Scattering Transform (MST) to extract wind speed features, such as higher and lower wind speeds. The extracted features are fed into GCGCN to classify turbulence levels into low, moderate, and high. To validate the proposed method, experiments were conducted using various wind speeds and bluff body designs in a wind tunnel. Implemented in Python, the GCGCN-PMEMS-FDS approach demonstrated superior performance compared to existing methods, achieving higher accuracy of 99.92 % in turbulence classification, low computation time of 93 s compared with existing methods. These results highlight the effectiveness of the GCGCN-PMEMS-FDS method in enhancing sensing capabilities of MEMS sensors in fluid-dynamic systems.