Improved Methods of Optimized Sparse Sensing for Yaw Angle Estimation and Surface Pressure Distribution Reconstruction Using Pressure-Sensitive Paint Data of Ground Vehicle

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View Video Presentation: https://doi.org/10.2514/6.2023-1944.vid This study proposes improved estimation frameworks for the wind direction (yaw angle) against a simple automobile model and the top surface pressure distribution from sparse sensors. The surface pressure distributions of the Ahmed model were measured by pressure-sensitive paint as training data. The estimation model for yaw angle estimation was constructed based on the least-squares estimation, and the estimation model for the pressure distribution reconstruction was constructed based on the proper orthogonal decomposition basis and estimated mode coefficients. The mode coefficients were estimated based on the least-squares estimation and the Bayesian estimation. Moreover, the sensor positions were optimized by three different algorithms. As the result, the method based on the Bayesian estimation was effective for the pressure distribution reconstruction. In addition, the algorithm based on Bayesian estimation was effective for both the yaw angle estimation and the pressure distribution reconstruction. These results will contribute to improving vehicle performance and safety through vehicle control based on the state of the flow around the vehicle measured by the sparse sensors.

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