Measuring porous media velocity fields and grain bed architecture with a quantitative PLIF-based technique
Measuring porous media velocity fields and grain bed architecture with a quantitative PLIF-based technique
复制标题
使用基于 PLIF 的定量技术测量多孔介质速度场和颗粒床结构
DOI：
10.1088/1361-6501/acfb2b
复制
发表时间：
2023
影响因子：
2.4
通讯作者：
Tonina, Daniele
中科院分区：
文献类型：
--
作者：
Hilliard, Brandon;Budwig, Ralph;Skifton, Richard S.;Durgesh, Vibhav;Reeder, William J.;Bhattarai, Bishal;Martin, Benjamin T.;Xing, Tao;Tonina, Daniele
多孔介质流动在自然和人为系统中都很常见。在实验室环境中绘制这些流动具有挑战性，通常需要非侵入式测量技术，例如与折射率匹配（RIM）相结合的粒子图像测速（PIV）。RIM耦合的PIV可以通过分析中性浮力微米级粒子的运动来绘制透明固体周围的速度场。在多孔介质中使用这种技术可能是有问题的，因为种子颗粒粘附在颗粒上，这导致颗粒床失去透明度并可能阻碍孔隙流动。另一种非侵入式光学技术，平面激光诱导荧光（PLIF），可以与RIM配对，并且没有这种限制，因为使用荧光染料代替颗粒，但它主要用于定性流动可视化。在这里，我们提出了一个定量PLIF为基础的方法来映射多孔介质流场和多孔介质结构。速度场是通过跟踪多孔介质内的荧光染料羽流前缘的平流为主的运动而获得的。我们还提出了一个自动跟踪算法，量化的二维速度分量的羽流通过空间移动在欧拉和拉格朗日框架。我们将此算法应用到三个数据集：一个合成数据集和两个实验室实验。该算法的性能通过其结果与参考数据之间的残差的平均值（偏倚误差，B）和标准差（随机误差，SD）报告。对于合成数据，该算法在欧拉框架下产生的最大误差分别为B & SD= 32%和23%，在拉格朗日框架下产生的最大误差分别为B & SD=-0.04%和3.9%。小规模的实验室实验数据需要欧拉框架，产生的误差为B & SD=− 0.5% & 33%。用拉格朗日框架对大规模实验数据进行了计算，误差为B & SD= 5% & 44%。映射的多孔介质结构示出了重建已知尺寸的校准颗粒的可忽略的误差。
Porous media flows are common in both natural and anthropogenic systems. Mapping these flows in a laboratory setting is challenging and often requires non-intrusive measurement techniques, such as particle image velocimetry (PIV) coupled with refractive index matching (RIM). RIM-coupled PIV allows the mapping of velocity fields around transparent solids by analyzing the movement of neutrally buoyant micron-sized seeding particles. The use of this technique in a porous medium can be problematic because seeding particles adhere to grains, which causes the grain bed to lose transparency and can obstruct pore flows. Another non-intrusive optical technique, planar laser-induced fluorescence (PLIF), can be paired with RIM and does not have this limitation because fluorescent dye is used instead of particles, but it has been chiefly used for qualitative flow visualization. Here, we propose a quantitative PLIF-based methodology to map both porous media flow fields and porous media architecture. Velocity fields are obtained by tracking the advection-dominated movement of the fluorescent dye plume front within a porous medium. We also propose an automatic tracking algorithm that quantifies 2D velocity components as the plume moves through space in both an Eulerian and a Lagrangian framework. We apply this algorithm to three data sets: a synthetic data set and two laboratory experiments. Performance of this algorithm is reported by the mean (bias error, B) and standard deviation (random error, SD) of the residuals between its results and the reference data. For the synthetic data, the algorithm produces maximum errors of B & SD= 32% & 23% in the Eulerian framework, respectively, and B & SD=− 0.04% & 3.9% in the Lagrangian framework. The small-scale laboratory experimental data requires the Eulerian framework and produce errors of B & SD=− 0.5% & 33%. The Lagrangian framework is used on the large-scale laboratory experimental data and produces errors of B & SD= 5% & 44%. Mapping the porous media architecture shows negligible error for reconstructing calibration grains of known dimensions.
登录
查看更多内容
一种基于物理的分层正则化数据驱动模型，用于预测穿过多孔介质的流体流动
DOI：
10.1016/j.jcp.2021.110526
发表时间：
2021
期刊：
J. Comput. Phys.
影响因子：
--
作者：
Kun Wang;Yu Chen;M. Mehana;N. Lubbers;K. Bennett;Q. Kang;H. Viswanathan;T. Germann
通讯作者：
T. Germann
影响因子：
4.7
作者：
Javier E. Santos;Duo Xu;H. Jo;C. Landry;M. Prodanović;M. Pyrcz
通讯作者：
Javier E. Santos;Duo Xu;H. Jo;C. Landry;M. Prodanović;M. Pyrcz
DOI：
10.1103/physreve.90.013025
发表时间：
2014-07
期刊：
Physical review. E, Statistical, nonlinear, and soft matter physics
影响因子：
--
作者：
Frank Wirner;Christian Scholz;C. Bechinger
通讯作者：
Frank Wirner;Christian Scholz;C. Bechinger
新西兰怀塔基河奇努克鲑鱼 (Oncorhynchus tshawytscha) 胚胎的 Redd 特征及其生存意义
DOI：
10.1080/00288330.1993.9516585
发表时间：
1993
影响因子：
1.6
作者：
K. Deverall;J. Kelso;G. James
通讯作者：
G. James
影响因子：
3.2
作者：
S. Rubol;D. Tonina;L. Vincent;Jill A. Sohm;W. Basham;R. Budwig;P. Savalia;E. Kanso;D. Capone;K. Nealson
通讯作者：
S. Rubol;D. Tonina;L. Vincent;Jill A. Sohm;W. Basham;R. Budwig;P. Savalia;E. Kanso;D. Capone;K. Nealson