Development and characterization of a passive, bio-inspired flow-tracking sensor
Development and characterization of a passive, bio-inspired flow-tracking sensor
复制标题
被动仿生流量跟踪传感器的开发和表征
DOI：
--
复制
发表时间：
2023
影响因子：
3.4
通讯作者：
D. Rival
中科院分区：
文献类型：
--
作者：
J. Galler;D. Rival
种子在湍流大气流中的有效自然传输是通过无数的形状和大小来发现的。然而，为了开发大规模（原位）流跟踪测量所需的灵敏无源传感器，系统由于光学可见性所需的增加的尺寸和质量或通过携带传感器有效载荷（诸如惯性测量单元（IMU））而遭受惯性滞后。虽然基于IMU的流量传感对于超越视觉视线的应用很有前途，但传感器平台的尺寸和质量会导致流量保真度降低，从而导致测量误差。因此，提取否则无法访问的流量信息，基于流动物理的示踪剂校正是通过应用低阶非定常空气动力学模型，灵感来自附加质量的概念。该技术使用配备有IMU和磁力计的传感器进行评估。一个球形的传感器平台，选择其对称的几何形状，是两个典型的测试情况下，包括轴向阵风以及圆柱体后面产生的旋涡脱落。使用测量的传感器速度和加速度作为输入，基于能量质量的动态模型用于从传感器测量值反算瞬时流速。传感器还通过高速摄像机进行光学跟踪，同时收集机载惯性数据。对于一维测试用例（轴向阵风），真实的（本地）风速估计的能量质量为基础的模型和验证粒子图像测速测量，表现出良好的协议，最大误差为10%。对于圆柱体尾流（第二个测试用例），基于模型的校正能够提取速度振荡幅度和旋涡脱落频率，否则无法获得。这项研究的结果表明，惯性（即大而重）惯性测量单元为基础的流量传感器是可行的拉格朗日跟踪提取在大大气尺度和高度瞬态（湍流）的环境中，再加上一个强大的动态模型惯性校正。
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 (in situ) 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.
登录
查看更多内容
影响因子：
64.8
作者：
Iyer, Vikram;Gaensbauer, Hans;Gollakota, Shyamnath
通讯作者：
Gollakota, Shyamnath
影响因子：
64.8
作者：
Kim, Bong Hoon;Li, Kan;Rogers, John A.
通讯作者：
Rogers, John A.
影响因子：
16.6
作者：
M. Serra;Pratik Sathe;I. Rypina;A. Kirincich;S. Ross;Pierre FJ Lermusiaux;Arthur Allen;T. Peacock;G. Haller
通讯作者：
M. Serra;Pratik Sathe;I. Rypina;A. Kirincich;S. Ross;Pierre FJ Lermusiaux;Arthur Allen;T. Peacock;G. Haller
全尺寸垂直轴风力发电机的近尾流结构
影响因子：
3.7
作者：
Wei, Nathaniel J.;Brownstein, Ian D.;Cardona, Jennifer L.;Howland, Michael F.;Dabiri, John O.
通讯作者：
Dabiri, John O.
影响因子：
3.9
作者：
Gonzalez-Rocha, Javier;De Wekker, Stephan F. J.;Woolsey, Craig A.
通讯作者：
Woolsey, Craig A.