gmaniatis88/Maniatis-et-al-2020: Maniatis_et_al_v1.0

In this repository you can find the data presented in the manuscript: Maniatis et al.,: "Inertial drag and lift forces for coarse grains on rough alluvial beds measured using in-grain accelerometers", Earth Surface Dynamics, accepted in late 2020. There are two datasets: The first is for the laboratory experiments performed in the flume facilities of the University of Glasgow (2013-2015) for both the spherical and the ellipsoid of the sensors. A whole laboratory run for the sphere and and an example of how the ellispoid behaved close to and at entrainment is shown here: https://www.youtube.com/playlist?list=PLzxFBJ2sEYx7PaRsbkuxqpw0bXcDPfZe2. In the .zip files, the time-series are included as used in the analysis for the calculation of impulses in the paper. The time series used at this step do not include the 10s cessation period (are cut at the point where the sensor stopped moving). Complete experimental runs for each enclosure (one for the sphere and one for the ellipsoid, including the cessation period) are also provided in the Github repository (https://github.com/gmaniatis88/Maniatis-et-al-2020 ) as separate files, for the better understanding of the laboratory experimental procedure (Sphere_full_run_lab.csv and Ellipsoid_1_full_run_lab.csv). The Sphere_full_run_lab.csv for the sphere was also used in the Figures 3 and B1 of the paper. The second dataset corresponds to the field experiments performed in River Erlenbach (May 2018) and is for the ellipsoid only. Those runs include seconds from the cessation because the transport was very short (and noisy) and it would be very difficult to interpret the motion otherwise. Finally, note that for those experiments the final and transformed Drag and Lift inertial forces are provided, as discussed in the paper, and not the raw sensor data. Notes: A lot of sensors have the facility for "on-board filtering" for deriving body frame linear accelerations. The model described in the paper starts a step before that in order to explain the physics of the accelerometer measurement in relation to sediment transport. In this context, the raw accelerometer measurements provided for the lab experiments are the body frame accelerations before gravity compensation (for relevant calibration, see thesis Maniatis 2016, chapter 6). The initial oriantation of the sensor for each experiment is defined in the paper. The orientation quaternions were approximated using the auxiliary solution of Valenti et al., 2015 for the b-i quaternion as defined in the paper (Appendix A1). In addition, the Yei sensor provides that facility and includes a correction from the magnetometer deployed in the sensor using a Kalman Filter based fusion algorithm. This was used to cross-compare the solution and define the noise threshold for the accelerometer. References: Maniatis, G.: Eulerian-Lagrangian definition of coarse bed-load transport: theory and verification with low-cost inertial measurement units, Ph.D. thesis, University of Glasgow, 2016. Valenti, R.G., Dryanovski, I. and Xiao, J., 2015. Keeping a good attitude: A quaternion-based orientation filter for IMUs and MARGs. Sensors, 15(8), pp.19302-19330.本仓库包含Maniatis等人发表于《地球表面动力学（Earth Surface Dynamics）》、2020年末接收的论文《粗糙冲积床粗颗粒惯性拖曳力与升力——基于颗粒内置加速度计（in-grain accelerometers）的测量》所呈现的实验数据。 本数据集包含两部分： 第一部分为格拉斯哥大学水槽实验装置（flume facilities）于2013-2015年开展的室内实验数据，涵盖球形与椭球形传感器两类工况。此处展示了球形颗粒完整实验过程，以及椭球形颗粒在接近泥沙起动（entrainment）与泥沙起动瞬间的运动表现示例：https://www.youtube.com/playlist?list=PLzxFBJ2sEYx7PaRsbkuxqpw0bXcDPfZe2。压缩包内包含论文中用于冲量（impulse）计算分析的时间序列数据，该阶段的时序数据已剔除10秒的静止时段（在传感器停止运动的节点处截断）。为便于理解室内实验流程，本GitHub仓库（https://github.com/gmaniatis88/Maniatis-et-al-2020 ）还单独提供了各工况的完整实验记录（含静止时段）：Sphere_full_run_lab.csv（球形颗粒）与Ellipsoid_1_full_run_lab.csv（椭球形颗粒）。其中球形颗粒的Sphere_full_run_lab.csv数据集还被用于论文中的图3与附录图B1。 第二部分数据集对应2018年5月在艾伦巴赫河（River Erlenbach）开展的野外实验数据，仅涵盖椭球形颗粒工况。由于该实验的输运过程极短且噪声较强，若截断静止时段将难以解释运动过程，因此这些实验记录包含静止时段的数据。需特别说明的是，本数据集提供的是经论文中所述方法转换后的拖曳力与升力惯性力数据，而非原始传感器采集数据。 补充说明：多数传感器具备板载滤波（on-board filtering）功能，可用于获取载体坐标系（body frame）下的线加速度。本文提出的模型起始于该滤波步骤之前，旨在阐释与推移质输运（bed-load transport）相关的加速度计测量物理机制。就此而言，室内实验提供的原始加速度计数据为未进行重力补偿（gravity compensation）的载体坐标系加速度（相关校准方法参见Maniatis 2016年博士论文第6章）。每项实验的传感器初始方位已在论文中给出，方位四元数（orientation quaternions）采用Valenti等人2015年提出的辅助解法进行近似，对应论文附录A1中定义的b-i四元数。此外，Yei传感器具备该功能，并基于卡尔曼滤波（Kalman Filter）融合算法对传感器内置磁力计的校正结果进行了处理，该结果被用于交叉验证求解结果并确定加速度计的噪声阈值。 参考文献： 1. Maniatis, G.: 《粗颗粒推移质输运的欧拉-拉格朗日定义：理论与低成本惯性测量单元（IMU）的验证（Eulerian-Lagrangian definition of coarse bed-load transport: theory and verification with low-cost inertial measurement units）》，博士论文，格拉斯哥大学，2016。 2. Valenti, R.G., Dryanovski, I. 和 Xiao, J., 2015. 保持良好姿态：面向惯性测量单元（IMU）与磁强计-陀螺仪-加速度计组合（MARG）的四元数方位滤波器（Keeping a good attitude: A quaternion-based orientation filter for IMUs and MARGs）. 传感器（Sensors）, 15(8), pp.19302-19330。