Systematic Application of Background-Oriented Schlieren for Isolator Shock Train Visualization

Geerts, Jonathan S.; Yu, Kenneth H.

doi:10.2514/1.j054991

Cited by 14 publications

(5 citation statements)

References 17 publications

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“…The boundary conditions on the top and bottom surfaces are applied as no-slip isothermal walls, with wall temperature / 1 = 1.676. Periodic conditions are applied in the direction which mimics an infinite-span arrangement (note that resolving sidewalls can still have a significant effect on the structure of the shock train [4,25,26]). The boundary layer turbulence is generated using a synthetic turbulence generation method at the inflow plane.…”

Section: A Methodologymentioning

confidence: 99%

Shock Train Response to High-Frequency Backpressure Forcing

Gillespie

Sandham

2022

AIAA Journal

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Numerical simulations are performed to study the effect of back pressure disturbances on a shock train. The basic problem consists of a shock train within a constant-area channel with a = 2 inflow and a back pressure applied to the outlet. By subjecting the shock train to different fixed back pressures, it is shown that the shock train length varies linearly with back pressure, while the shock spacing is conserved. Through back pressure step forcing it is shown that the response of the shocks, after an initial lag, is largely determined by the magnitude of the step change. Step increases in back pressure cause upstream shock movements with higher shock pressures. The opposite response occurs with back pressure decreases. With sinusoidal back pressure forcing the shock train oscillates at the applied forcing frequency. The forcing

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Section: A Methodologymentioning

confidence: 99%

Shock Train Response to High-Frequency Backpressure Forcing

Gillespie

Sandham

2022

AIAA Journal

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“…Deflection sensing is typically conducted using a cross-correlation [3,14,15] or optical flow (OF) [16][17][18] algorithm. Cross-correlation methods identify the displacement of a multi-pixel window from the reference image to the distorted image.…”

Section: Introductionmentioning

confidence: 99%

Estimating density, velocity, and pressure fields in supersonic flow using physics-informed BOS

Molnar¹,

Venkatakrishnan²,

Schmidt³

et al. 2022

Preprint

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We report a new workflow for background-oriented schlieren (BOS), termed "physics-informed BOS," to extract density, velocity, and pressure fields from a pair of reference and distorted images. Our method uses a physics-informed neural network (PINN) to produce flow fields that simultaneously satisfy the measurement data and governing equations. For the high-speed flows of interest in this work, we specify a physics loss based on the Euler and irrotationality equations. BOS is a quantitative fluid visualization technique that is used to characterize high-speed flows. Images of a background pattern, positioned behind the target flow, are processed using computer vision and tomography algorithms to determine the density field. Crucially, BOS features a series of ill-posed inverse problems that require supplemental information (i.e., in addition to the images) to accurately reconstruct the flow. Current BOS workflows rely upon interpolation of the images or a penalty term to promote a globally-or piecewise-smooth solution. However, these algorithms are invariably incompatible with the flow physics, leading to errors in the density field. Physics-informed BOS directly reconstructs all the flow fields using a PINN that includes the BOS measurement model and governing equations. This procedure improves the accuracy of density estimates and also yields velocity and pressure data, which was not previously available. We demonstrate our approach by reconstructing synthetic data that corresponds to analytical and numerical phantoms as well as experimental measurements. Our physics-informed reconstructions are significantly more accurate than conventional BOS estimates. Further, to the best of our knowledge, this work represents the first use of a PINN to reconstruct a supersonic flow from experimental data of any kind.

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“…An abbreviated list of examples of studies using these algorithms can be found in refs. [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Optical Flow Analysis for Background-Oriented Schlieren Image Processing

Schmidt

Woike

2021

AIAA Journal

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A wavelet-based optical flow analysis (wOFA) method for processing background oriented schlieren (BOS) images is presented and demonstrated on synthetic and experimental data. Optical flow is inherently well-suited to BOS, since the background pattern and lighting conditions are specified and controlled by the user, and can be chosen to play to the strengths of optical flow processing. Analysis of the synthetic BOS data show that a 2D sinusoidal background *

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Systematic Application of Background-Oriented Schlieren for Isolator Shock Train Visualization

Cited by 14 publications

References 17 publications

Shock Train Response to High-Frequency Backpressure Forcing

Shock Train Response to High-Frequency Backpressure Forcing

Estimating density, velocity, and pressure fields in supersonic flow using physics-informed BOS

Wavelet-Based Optical Flow Analysis for Background-Oriented Schlieren Image Processing

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