基于实验数据的失速翼型上流动的数据驱动稀疏重建

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
D. Carter, Francis De Voogt, R. Soares, B. Ganapathisubramani
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引用次数: 18

摘要

最近的研究表明,利用稀疏传感器与适当的正交分解(POD)相结合,可以产生各种流动的全速度场的数据驱动重建。本工作研究了这些技术应用于停滞的NACA 0012机翼在$ {Re}_c= 75000 $,攻角$ \alpha ={12}^{\circ} $上的保真度,这是用平面时间分辨粒子图像测速仪实验测量的。与以往的许多研究相反,由于分离区域的湍流,流动没有任何主导的脱落频率,并且表现出广泛的奇异值。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)进行非线性细化。研究发现,如果稀疏传感器避开了全局POD基上方差较小的区域,则扩展POD激发的线性重建效果不如压缩感知方法。无论使用哪种线性方法,非线性SNN在改进重建方面都具有惊人的相似性能。进一步讨论了稀疏传感器重建分离湍流测量的能力,并提出了未来工作的方向。使用有限的数据子集进行全域信息的稀疏重建与以数据为中心的工程应用广泛相关;从用有限的像素重建人脸到用有限的传感器预测层流和湍流流场。本文研究的重点是后一种与航空航天及相关工业中主动流量控制高度相关的例子。从稀疏测量中获得流场重构有多种数据驱动的方法,从线性无监督适当正交分解到使用非线性监督神经网络。这些方法在高湍流流场以及实验得到的流场中的可行性仍然是一个开放的研究领域。本研究揭示了这些技术创建时不变库的能力,该库可以仅从稀疏测量中预测流的瞬时状态(前提是这些状态在应用训练数据的范围内)。所提出的线性方法,以及神经网络架构,为稀疏感知和状态估计应用的未来努力提供了良好的特征框架:特别是对于高度非线性的底层系统,如湍流。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data
Abstract 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}^{\circ } $ 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. Impact Statement Sparse reconstruction of full-field information using a limited subset of data is widely relevant to data-centric engineering applications; from reconstructing human faces with limited pixels to predicting laminar and turbulent flow fields from limited sensors. The focus of the present study is of the latter example with high relevance to active flow control in aerospace and related industry. There are multiple data-driven methodologies for obtaining flow field reconstructions from sparse measurements ranging from the linear unsupervised proper orthogonal decomposition to the use of nonlinear supervised NNs. The feasibility of such methods to flow fields that are highly turbulent as well as obtained via experiment remains an open area of research. The present study reveals the capability of these techniques to create a time-invariant library that can predict instantaneous states of the flow from sparse measurements alone (provided that these states are within the bounds of the applied training data). The proposed linear methods, as well as the NN architecture, provide well-characterized frameworks for future efforts in sparse sensing and state estimation applications: particularly for highly nonlinear underlying systems such as turbulent flow.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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