A data assimilation model for wall pressure-driven mean flow reconstruction

2022 ◽  
Vol 34 (1) ◽  
pp. 015101
Author(s):  
Sen Li ◽  
Chuangxin He ◽  
Yingzheng Liu
Keyword(s):  
Data Assimilation ◽  
Wall Pressure ◽  
Mean Flow ◽  
Flow Reconstruction ◽  
Assimilation Model ◽  
Pressure Driven

scholarly journals Ionospheric dynamics and drivers obtained from a physics-based data assimilation model

Radio Science ◽  
2009 ◽  
Vol 44 (1) ◽  
pp. n/a-n/a ◽  
Author(s):  
Ludger Scherliess ◽  
Donald C. Thompson ◽  
Robert W. Schunk
Keyword(s):  
Data Assimilation ◽  
Assimilation Model ◽  
Ionospheric Dynamics

Turbulent Wall-Pressure Fluctuations: A New Model for Off-Axis Cross-Spectral Density

1997 ◽  
Vol 119 (2) ◽  
pp. 277-280 ◽  
Author(s):  
B. A. Singer
Keyword(s):  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
Spectral Density ◽  
Flow Direction ◽  
Wall Pressure ◽  
Mean Flow ◽  
New Approach ◽  
Cross Spectral Density ◽  
The Mean ◽  
The Cross

Models for the distribution of the wall-pressure under a turbulent boundary layer often estimate the coherence of the cross-spectral density in terms of a product of two coherence functions. One such function describes the coherence as a function of separation distance in the mean-flow direction, the other function describes the coherence in the cross-stream direction. Analysis of data from a large-eddy simulation of a turbulent boundary layer reveals that this approximation dramatically underpredicts the coherence for separation directions that are neither aligned with nor perpendicular to the mean-flow direction. These models fail even when the coherence functions in the directions parallel and perpendicular to the mean flow are known exactly. A new approach for combining the parallel and perpendicular coherence functions is presented. The new approach results in vastly improved approximations for the coherence.

Reynolds-number dependence of wall-pressure fluctuations in a pressure-induced turbulent separation bubble

2017 ◽  
Vol 833 ◽  
pp. 563-598 ◽  
Author(s):  
Hiroyuki Abe
Keyword(s):  
Reynolds Number ◽  
Pressure Gradient ◽  
Large Scale ◽  
Separation Bubble ◽  
Pressure Fluctuations ◽  
Local Maximum ◽  
Wall Pressure ◽  
Mean Flow ◽  
Frequency Spectra ◽  
Wall Pressure Fluctuations

Direct numerical simulations are used to examine the behaviour of wall-pressure fluctuations $p_{w}$ in a flat-plate turbulent boundary layer with large adverse and favourable pressure gradients, involving separation and reattachment. The Reynolds number $Re_{\unicode[STIX]{x1D703}}$ based on momentum thickness is equal to 300, 600 and 900. Particular attention is given to effects of Reynolds number on root-mean-square (r.m.s.) values, frequency/power spectra and instantaneous fields. The possible scaling laws are also examined as compared with the existing direct numerical simulation and experimental data. The r.m.s. value of $p_{w}$ normalized by the local maximum Reynolds shear stress $-\unicode[STIX]{x1D70C}\overline{uv}_{max}$ (Simpson et al. J. Fluid Mech. vol. 177, 1987, pp. 167–186; Na & Moin J. Fluid Mech. vol. 377, 1998b, pp. 347–373) leads to near plateau (i.e. $p_{w\,rms}/-\unicode[STIX]{x1D70C}\overline{uv}_{max}=2.5\sim 3$) in the adverse pressure gradient and separated regions in which the frequency spectra exhibit good collapse at low frequencies. The magnitude of $p_{w\,rms}/-\unicode[STIX]{x1D70C}\overline{uv}_{max}$ is however reduced down to 1.8 near reattachment where good collapse is also obtained with normalization by the local maximum wall-normal Reynolds stress $\unicode[STIX]{x1D70C}\overline{vv}_{max}$. Near reattachment, $p_{w\,rms}/-\unicode[STIX]{x1D70C}\overline{vv}_{max}=1.2$ is attained unambiguously independently of the Reynolds number and pressure gradient. The present magnitude (1.2) is smaller than (1.35) obtained for step-induced separation by Ji & Wang (J. Fluid Mech. vol. 712, 2012, pp. 471–504). The reason for this difference is intrinsically associated with convective nature of a pressure-induced separation bubble near reattachment where the magnitude of $p_{w\,rms}$ depends essentially on the favourable pressure gradient. The resulting mean flow acceleration leads to delay of the r.m.s. peak after reattachment. Attention is also given to structures of $p_{w}$. It is shown that large-scale spanwise rollers of low pressure fluctuations are formed above the bubble, whilst changing to large-scale streamwise elongated structures after reattachment. These large-scale structures become more prominent with increasing $Re_{\unicode[STIX]{x1D703}}$ and affect $p_{w}$ significantly.

Towards Practical Flow Sensing and Control via POD and LSE Based Low-Dimensional Tools (Keynote Paper)

2002 ◽  
Author(s):  
Jeffrey Taylor ◽  
M. N. Glauser
Keyword(s):  
Velocity Field ◽  
Control Strategies ◽  
Adverse Pressure Gradient ◽  
Wall Pressure ◽  
Mean Flow ◽  
Flow Sensing ◽  
Active Feedback ◽  
Low Dimensional ◽  
And Control ◽  
Proper Orthogonal

We present the application of Proper Orthogonal Decomposition (POD) and Linear Stochastic Estimation (LSE) based low-dimensional methods to the flow over a backward facing ramp with an adjustable flap above the ramp which allows for dynamic variation of the adverse pressure gradient. There is a range of flap angles where the flow is incipiently separated so that this relatively simple experiment can be used to flush out ideas for active feedback separation control strategies. The study utilized a combination of PIV and multi-point wall pressure measurements to estimate the full velocity field (mean plus fluctuating) from a modified complementary technique. Specifically we want to identify a low-dimensional mean flow to observe when the profiles are inflectionary, i.e., the incipient condition, just from wall pressure. We demonstrate via this method, that a reasonable estimate of the low dimensional full velocity field can be obtained. This is important for practical active feedback flow control strategies since from wall pressure we can estimate the state of the flow without resorting to probes in the flow.

Terminator field-aligned currents: A new finding from the Ionospheric Dynamics and Electrodynamics Data Assimilation Model

2014 ◽  
Vol 119 (6) ◽  
pp. 4752-4757 ◽  
Author(s):  
L. Zhu ◽  
R. W. Schunk ◽  
V. Eccles ◽  
L. Scherliess ◽  
J. J. Sojka ◽  
...  
Keyword(s):  
Data Assimilation ◽  
New Finding ◽  
Assimilation Model ◽  
Ionospheric Dynamics
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