Estimation of unsteady flow fields around flight vehicles may improve flow interactions and lead to enhanced vehicle performance. Although flow-field representations can be very high-dimensional, their dynamics can have low-order representations and may be estimated using a few, appropriately placed measurements. This paper presents a sensor-selection framework for the intended application of data-driven, flow-field estimation. This framework combines data-driven modeling, steady-state Kalman Filter design, and a sparsification technique for sequential selection of sensors. This paper also uses the sensor selection framework to design sensor arrays that can perform well across a variety of operating conditions. Flow estimation results on numerical data show that the proposed framework produces arrays that are highly effective at flow-field estimation for the flow behind and an airfoil at a high angle of attack using embedded pressure sensors. Analysis of the flow fields reveals that paths of impinging stagnation points along the airfoil's surface during a shedding period of the flow are highly informative locations for placement of pressure sensors.

翻译：飞行器周围非定常流场的估计可改善流动相互作用并提升飞行器性能。尽管流场表征的维度可能极高，但其动力学特性存在低阶表示，可通过少量、合理布置的传感器进行估计。本文提出面向流场数据驱动估计应用的传感器选择框架。该框架融合数据驱动建模、稳态卡尔曼滤波器设计及用于顺序选择传感器的稀疏化技术。本文还利用该传感器选择框架设计能在多种运行条件下均表现良好的传感器阵列。数值数据上的流场估计结果表明，所提框架能生成基于嵌入压力传感器对高攻角机翼后方流场进行高效估计的传感器阵列。流场分析揭示：在流动的一个脱落周期内，沿机翼表面运动的驻点驻留路径是布置压力传感器信息量最丰富的位置。