2 research outputs found
Towards real-time reconstruction of velocity fluctuations in turbulent channel flow
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. 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 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 help
enable real-time reconstructions of turbulent velocity fluctuations in an
analogous experimental setting.Comment: 36 pages, 22 figures, accepted by Physical Review Fluid