论文标题
在汽车模型上使用时间平均压力敏感的油漆数据来优化稀疏传感器放置，以估算风向和表面压力分布
Optimization of Sparse Sensor Placement for Estimation of Wind Direction and Surface Pressure Distribution Using Time-Averaged Pressure-Sensitive Paint Data on Automobile Model
论文作者
论文摘要
这项研究提出了一种通过使用数据驱动的优化稀疏压力传感器来预测针对简单汽车模型（AHMED模型）的风向和表面压力分布的方法。选择了稀疏压力传感器对在艾哈迈德模型上的位置，以根据各种偏航角的时间平均表面压力分布数据库来估计偏航角和压力分布的重建，而假定模型的左侧和右侧的对称传感器。通过压力敏感的油漆测量获得表面压力分布。应用了基于贪婪算法的三种稀疏传感器选择算法，并优化了传感器位置。比较并评估了三种算法的偏航角和压力分布的传感器位置和估计精度。结果表明，一些优化的传感器可以准确预测偏航角和压力分布。
This study proposes a method for predicting the wind direction against the simple automobile model (Ahmed model) and the surface pressure distributions on it by using data-driven optimized sparse pressure sensors. Positions of sparse pressure sensor pairs on the Ahmed model were selected for estimation of the yaw angle and reconstruction of pressure distributions based on the time-averaged surface pressure distributions database of various yaw angles, whereas the symmetric sensors in the left and right sides of the model were assumed. The surface pressure distributions were obtained by pressure-sensitive paint measurements. Three algorithms for sparse sensor selection based on the greedy algorithm were applied, and the sensor positions were optimized. The sensor positions and estimation accuracy of yaw angle and pressure distributions of three algorithms were compared and evaluated. The results show that a few optimized sensors can accurately predict the yaw angle and the pressure distributions.
