Reconstruction of large‐scale flow structures in a stirred tank from limited sensor data
Reconstruction of large‐scale flow structures in a stirred tank from limited sensor data
复制标题
根据有限的传感器数据重建搅拌槽中的大规模流动结构
DOI：
10.1002/aic.17348
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
George Papadakis
中科院分区：
文献类型：
--
作者：
K. Mikhaylov;S. Rigopoulos;George Papadakis
我们结合联合收割机的降阶建模和系统识别重建的Rushton叶轮叶片后面的大尺度涡结构的时间演变。我们进行了直接数值模拟，在雷诺数600，并采用适当的正交分解（POD）提取的主导模式和它们的时间系数。然后，我们应用的识别算法，N4SID，构建一个估计器，捕捉传感器点（输入）和POD系数（输出）的速度信号之间的关系。我们表明，第一对模式可以很好地重建使用的速度时间信号，即使是一个单一的传感器点。更大数量的点提高了精度和鲁棒性，并且还导致第二对POD模式的更好的重建。在Re = 500和700的流量在Re = 600的估计得出的应用程序表明，它是强大的相对于操作条件的变化。
We combine reduced order modeling and system identification to reconstruct the temporal evolution of large-scale vortical structures behind the blades of a Rushton impeller. We performed direct numerical simulations at Reynolds number 600 and employed proper orthogonal decomposition (POD) to extract the dominant modes and their temporal coefficients. We then applied the identification algorithm, N4SID, to construct an estimator that captures the relation between the velocity signals at sensor points (input) and the POD coefficients (output). We show that the first pair of modes can be very well reconstructed using the velocity time signal from even a single sensor point. A larger number of points improves accuracy and robustness and also leads to better reconstruction for the second pair of POD modes. Application of the estimator derived at Re = 600 to the flows at Re = 500 and 700 shows that it is robust with respect to changes in operating conditions.