Flow Reconstruction Around a Surface-Mounted Prism from Sparse Velocity and/or Scalar Measurements Using a Combination of POD and a Data-Driven Estimator
Flow Reconstruction Around a Surface-Mounted Prism from Sparse Velocity and/or Scalar Measurements Using a Combination of POD and a Data-Driven Estimator
复制标题
结合使用 POD 和数据驱动估算器，通过稀疏速度和/或标量测量重建表面安装棱镜周围的流动
DOI：
10.1007/s10494-023-00417-2
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Lu S
中科院分区：
文献类型：
--
作者：
Lu S
提出了一种数据驱动的基于稀疏速度和/或标量测量的流重建算法。该算法被应用于二维壁挂式正方形棱镜周围的流动。为了降低问题的维度，对流场和标量场的快照进行了处理，得到了POD模式及其时间系数。然后利用系统辨识算法建立了一个降阶、线性、动态的流动和标量动力学系统。然后应用最优估计理论推导出卡尔曼估值器，用于从稀疏测量中预测POD模式的时间系数。对流场和标量谱的分析表明，流场在标量上留下了足迹，因此从标量浓度测量中提取速度是有意义的。结果表明，用很少的传感器就可以很好地重建流动统计(雷诺应力)和瞬时流型(对于所考虑的情况，即使是单个标量传感器也能得到非常满意的结果)。在一个条件下得到的卡尔曼估计器能够以可接受的精度重建附近两个非设计条件下的流场。还需要进一步的工作来评估算法在更复杂的三维流动中的性能。
A data-driven algorithm is proposed for flow reconstruction from sparse velocity and/or scalar measurements. The algorithm is applied to the flow around a two-dimensional, wall-mounted, square prism. To reduce the problem dimensionality, snapshots of flow and scalar fields are processed to derive POD modes and their time coefficients. Then a system identification algorithm is employed to build a reduced order, linear, dynamical system for the flow and scalar dynamics. Optimal estimation theory is subsequently applied to derive a Kalman estimator to predict the time coefficients of the POD modes from sparse measurements. Analysis of the flow and scalar spectra demonstrate that the flow field leaves its footprint on the scalar, thus extracting velocity from scalar concentration measurements is meaningful. The results show that remarkably good reconstruction of the flow statistics (Reynolds stresses) and instantaneous flow patterns can be obtained using a very small number of sensors (even a single scalar sensor yields very satisfactory results for the case considered). The Kalman estimator derived at one condition is able to reconstruct with acceptable accuracy the flow fields at two nearby off-design conditions. Further work is needed to assess the performance of the algorithm in more complex, three-dimensional, flows.
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DOI：
10.1016/j.cma.2020.113603
发表时间：
2020-05
影响因子：
7.2
作者：
Minglang Yin;Xiaoning Zheng;J. Humphrey;G. Karniadakis
通讯作者：
Minglang Yin;Xiaoning Zheng;J. Humphrey;G. Karniadakis
旁路跃迁边界层中的尺度间能量传递分析
影响因子：
3.7
作者：
H. Yao;J. Mollicone;G. Papadakis
通讯作者：
G. Papadakis
影响因子：
3.7
作者：
J. Inigo;D. Sipp;P. Schmid
通讯作者：
P. Schmid
影响因子：
4.1
作者：
Raissi, M.;Perdikaris, P.;Karniadakis, G. E.
通讯作者：
Karniadakis, G. E.
根据有限的传感器数据重建搅拌槽中的大规模流动结构
影响因子：
3.7
作者：
K. Mikhaylov;S. Rigopoulos;George Papadakis
通讯作者：
George Papadakis