Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data
Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data
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DOI：
10.1017/dce.2021.5
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发表时间：
2021-01-01
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影响因子：
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通讯作者：
Ganapathisubramani, Bharathram
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文献类型：
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作者：
Carter, Douglas W.;De Voogt, Francis;Ganapathisubramani, Bharathram
最近的工作表明，使用稀疏传感器结合适当的正交分解（POD），以产生数据驱动的重建的全速度场在各种流量。目前的工作调查的保真度，这些技术应用于失速NACA 0012翼型在Re-c = 75,000在攻角α = 12度，实验测量使用平面时间分辨粒子图像测速仪。与许多以前的研究相比，流动是没有任何主导脱落频率，并表现出广泛的奇异值，由于在分离区的湍流。提出了几种基于经典压缩感知和扩展POD方法的线性状态估计重建方法，以及通过使用浅神经网络（SNN）的非线性细化。结果发现，线性重建的启发扩展POD是劣于压缩感知方法提供的稀疏传感器避免跨全球POD基础上的小方差的流动区域。无论使用何种线性方法，非线性SNN在重建的细化方面都具有惊人的相似性能。稀疏传感器重建分离湍流测量的能力进行了进一步讨论，并提出了今后工作的方向。
Recent work has demonstrated the use of sparse sensors in combination with the proper orthogonal decomposition (POD) to produce data-driven reconstructions of the full velocity fields in a variety of flows. The present work investigates the fidelity of such techniques applied to a stalled NACA 0012 aerofoil at Re-c = 75,000 at an angle of attack alpha = 12 degrees as measured experimentally using planar time-resolved particle image velocimetry. In contrast to many previous studies, the flow is absent of any dominant shedding frequency and exhibits a broad range of singular values due to the turbulence in the separated region. Several reconstruction methodologies for linear state estimation based on classical compressed sensing and extended POD methodologies are presented as well as nonlinear refinement through the use of a shallow neural network (SNN). It is found that the linear reconstructions inspired by the extended POD are inferior to the compressed sensing approach provided that the sparse sensors avoid regions of the flow with small variance across the global POD basis. Regardless of the linear method used, the nonlinear SNN gives strikingly similar performance in its refinement of the reconstructions. The capability of sparse sensors to reconstruct separated turbulent flow measurements is further discussed and directions for future work suggested.