Relations between vortex structures and optimized sparse sensor location over flow around ahmed model

抄録

The aerodynamic characteristics of the Ahmed model at different yaw angles reveal valuable information about vortex dynamics and surface pressure behavior. This study utilizes stereo-particle-image-velocimetry measurement of flow around the Ahmed model, and clarifies detailed flow patterns at yaw angles of 10, 15 and 20 degrees, which enable us to comprehensively analyze vortex interactions and their effects on aerodynamic pressure on the top surface of the Ahmed model. An advanced vortex detection algorithm was employed, and the characteristics of the side-edge separated vortex such as size and circulation, which are vital to understanding the airflow over the models surfaces, were identified and quantified. Additionally, the relationship of vortices and optimized pressure sensors by the Bayesian D-optimality greedy (BDG) algorithm was investigated. The analyses illustrate that the BDG algorithm strategically locates sensors in areas significantly affected by dynamic pressure changes due to vortex activity and the geometry of the model, whereas this optimized placement of sensors enhances the quality of data collection and the precision of aerodynamic estimations. The outcomes of this research are particularly relevant to the development of autonomous vehicles, where precise sensor placement is crucial for vehicle stability and efficiency. The results of the present study indicate that properly aligned sensor placements beneath vortex paths can improve sensor-based monitoring and control systems. Further research is necessary to validate these initial findings, which could lead to promising improvements in aerodynamic strategies for enhancing the stability and efficiency of smart vehicles.