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:飞行器周围的非定常流动场估计可能会改善流动相互作用并导致提高航空器性能。虽然流场表示可以非常高维,但其动力学可以具有低阶表示,并且可以使用少量适当放置的测量值进行估计。本文介绍了一种旨在数据驱动流场估计应用的传感器选择框架。该框架结合了数据驱动建模、稳态卡尔曼滤波器设计和一个用于序列传感器选择的稀疏化技术。本文还使用传感器选择框架设计了可以在各种操作条件下表现良好的传感器阵列。在数值数据上的流估计结果表明,该框架产生的传感器阵列在使用嵌入式压力传感器进行后方气流和高迎角下的翼型流场估计方面非常有效。流场分析表明:流体在流动脱落期间穿过翼型表面的停滞点路径是放置压力传感器的高信息位置。