Model-based multi-sensor fusion for reconstructing wall-bounded turbulence

Wang, Mengying; Krishna, C. Vamsi; Luhar, Mitul; Hemati, Maziar S.

doi:10.1007/s00162-021-00586-8

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…LSE-based techniques are often used with data-driven [21,22] and equation-based [23,24] models to augment flow reconstructions. Other methods use simplified governing models to produce forward and backward estimates that augment reconstructions from low-and multi-resolution temporal measurements [25,26]. One central challenge in these estimation techniques is addressing the nonlinearity of the governing equations.…”

Section: B Reconstruction Techniquesmentioning

confidence: 99%

Towards Real-Time Reconstruction of Velocity Fluctuations in Turbulent Channel Flow

Arun¹,

Bae²,

McKeon³

2023

Preprint

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We develop a framework for efficient streaming reconstructions of turbulent velocity fluctuations from limited sensor measurements with the goal of enabling real-time applications. The reconstruction process is simplified by computing linear estimators using flow statistics from an initial training period and evaluating their performance during a subsequent testing period with data obtained from direct numerical simulation (DNS). We address cases where (i) no, (ii) limited, and (iii) full-field training data are available using estimators based on (i) resolvent modes, (ii) resolvent-based estimation, and (iii) spectral proper orthogonal decomposition modes. During training, we introduce blockwise inversion to accurately and efficiently compute the resolvent operator in an interpretable manner. During testing, we enable efficient streaming reconstructions by using a temporal sliding discrete Fourier transform to recursively update Fourier coefficients using incoming measurements. We use this framework to reconstruct with minimal time delay the turbulent velocity fluctuations in a minimal channel at 𝑅𝑒 𝜏 ≈ 186 from sparse planar measurements. We evaluate reconstruction accuracy in the context of the extent of data required and thereby identify potential use cases for each estimator. The reconstructions capture large portions of the dynamics from relatively few measurement planes when the linear estimators are computed with sufficient fidelity. We also evaluate the efficiency of our reconstructions and show that the present framework has the potential to enable real-time reconstructions of turbulent velocity fluctuations in an analogous experimental setting.

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Section: B Reconstruction Techniquesmentioning

confidence: 99%

Towards Real-Time Reconstruction of Velocity Fluctuations in Turbulent Channel Flow

Arun¹,

Bae²,

McKeon³

2023

Preprint

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“…Typically, this is achieved using a combination of PIV and hot-wire-anemometry (HWA) measurements. 16,17 Others rely on a proper orthogonal decomposition (POD) 18,19 in conjunction with linear stochastic estimation, 20 and more recently, some approaches successfully exploit machine learning methods. 6,21,22 While machine learning always poses the question about the universality, curation, and limits of the training dataset, the multi-sensor approach requires additional effort for the instrumentation, which must be implemented beforehand to make use of the method.…”

Section: Introductionmentioning

confidence: 99%

Advection-based temporal reconstruction technique for turbulent velocity fields

et al. 2023

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Characterization of turbulent flows is challenging due to the interaction of widespread spatiotemporal scales. Experimental techniques such as Particle Image Velocimetry (PIV) can be used to obtain spatially resolved flow measurements, however, these systems often suffer from limited acquisition rates. The present work investigates the ability of an advection-based flow reconstruction technique to increase the temporal resolution of turbulent flow data without any prior knowledge of the flow physics. A semi-Lagrangian technique is suggested to obtain fluid trajectories through a forward and backward integration of the available spatiotemporal data. The estimates are then fused using a temporal weighting scheme to yield velocity fields at intermediate times. The performance of the method is verified against three-dimensional direct numerical simulation (DNS) data of a plane jet at Re = 10000. Extracting time series data from the spatially and temporally resolved DNS results, five test cases with artificially lowered sampling frequencies were generated. Spectral analysis revealed that the characteristic frequency found in shear-layer dominated flows can be obtained even for the most extreme case. Additionally, spectral information up to two orders of magnitude beyond the Nyquist criteria is successfully recovered throughout the entire spatial domain -- surpassing the performance of previously introduced methods. The maximum spectral reconstruction error of the suggested method, defined as net energy loss or gain, fell within the bounds of [-5, 3]%, with a corresponding global energy difference in the range of [-2, 1]%. Furthermore, the spatially averaged reconstruction error for the velocity fluctuations was bound by [6 +/- 6]%.

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