Galler J N, Rival D E
Mechanical and Materials Engineering Department, Queen's University, Kingston, Ontario, Canada.
Bioinspir Biomim. 2023 Feb 2;18(2). doi: 10.1088/1748-3190/acb02d.
The effective natural transport of seeds in turbulent atmospheric flows is found across a myriad of shapes and sizes. However, to develop a sensitive passive sensor required for large-scale () flow tracking measurements, systems suffer from inertial lag due to the increased size and mass needed for optical visibility, or by carrying a sensor payload, such as an inertial measurement unit (IMU). While IMU-based flow sensing is promising for beyond visual line-of-sight applications, the size and mass of the sensor platform results in reduced flow fidelity and, hence, measurement error. Thus, to extract otherwise inaccessible flow information, a flow-physics-based tracer correction is developed through the application of a low-order unsteady aerodynamic model, inspired by the added-mass concept. The technique is evaluated using a sensor equipped with an IMU and magnetometer. A spherical sensor platform, selected for its symmetric geometry, was subject to two canonical test cases including an axial gust as well as the vortex shedding generated behind a cylinder. Using the measured sensor velocity and acceleration as inputs, an energized-mass-based dynamic model is used to back-calculate the instantaneous flow velocity from the sensor measurements. The sensor is also tracked optically via a high-speed camera while collecting the inertial data onboard. For the 1D test case (axial gust), the true (local) wind speed was estimated from the energized-mass-based model and validated against particle image velocimetry measurements, exhibiting good agreement with a maximum error of 10%. For the cylinder wake (second test case), the model-based correction enabled the extraction of the velocity oscillation amplitude and vortex-shedding frequency, which would have otherwise been inaccessible. The results of this study suggest that inertial (i.e. large and heavy) IMU-based flow sensors are viable for the extraction of Lagrangian tracking at large atmospheric scales and within highly-transient (turbulent) environments when coupled with a robust dynamic model for inertial correction.
在各种形状和大小的物体中，都能发现种子在湍流大气流动中的有效自然传输。然而，要开发用于大规模（）流动跟踪测量所需的灵敏无源传感器，系统会因光学可见性所需的尺寸和质量增加，或者因携带诸如惯性测量单元（IMU）等传感器载荷而存在惯性滞后。虽然基于IMU的流量传感在超视距应用中很有前景，但传感器平台的尺寸和质量会导致流量保真度降低，从而产生测量误差。因此，为了提取其他难以获取的流动信息，受附加质量概念启发，通过应用低阶非定常空气动力学模型，开发了一种基于流动物理的示踪剂校正方法。该技术使用配备有IMU和磁力计的传感器进行评估。选择球形传感器平台是因其对称几何形状，使其经历了两个典型测试案例，包括轴向阵风以及圆柱体后方产生的涡旋脱落。以测量的传感器速度和加速度作为输入，基于激励质量的动态模型用于从传感器测量值中反算瞬时流速。在机载收集惯性数据的同时，还通过高速摄像机对传感器进行光学跟踪。对于一维测试案例（轴向阵风），基于激励质量的模型估计了真实（局部）风速，并与粒子图像测速测量结果进行了验证，最大误差为10%，显示出良好的一致性。对于圆柱体尾流（第二个测试案例），基于模型的校正能够提取速度振荡幅度和涡旋脱落频率，否则这些信息将无法获取。这项研究的结果表明，基于惯性（即大型且重型）IMU的流量传感器在与用于惯性校正的强大动态模型相结合时，对于在大尺度大气和高度瞬态（湍流）环境中提取拉格朗日跟踪信息是可行的。
