Obtaining velocity and pressure distributions in natural convection flows using experimental temperature fields

Vinnichenko, Nikolay A.; Plaksina, Yulia Yu.; Pushtaev, Alexey V.; Uvarov, A. V.

doi:10.1016/j.applthermaleng.2022.118962

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…There are numerous methods that can be used to solve (or approximately solve) the governing equations while conforming to experimental measurements. Kalman filter [54,55], state observer [56], adjoint-variational [57,58], and hybrid simulation [59] algorithms have all been used to reconstruct flow fields with input from an experiment. For instance, local ensemble Kalman filter DA was employed to forecast temperature and velocity fields in a Rayleigh-Bénard convection cell from a set of experimental shadowgraphs [54].…”

Section: Physics-informed Bosmentioning

confidence: 99%

“…Variational techniques optimize a control vector, such as the initial flow state, to minimize an arbitrary data loss [57,58], and hybrid CFD simulations are conducted with one or more fields or parameters that are fixed by data. As an example, Vinnichenko et al [59] conducted a hybrid simulation of natural convection using a BOS-based estimate of the temperature field to determine the buoyancy term.…”

Section: Physics-informed Bosmentioning

confidence: 99%

“…Unfortunately, these DA techniques come at a high computational cost. Furthermore, all BOS DA reported to-date has employed either (a) idealized synthetic BOS data [55] or (b) conventional BOS reconstructions [31,59]. When the data loss, constraint, or forcing term in a DA algorithm includes reconstructions, as opposed to the measurement model and raw signal, the resultant fields are adversely affected by nonphysical reconstruction artifacts introduced by the BOS algorithm.…”

Section: Physics-informed Bosmentioning

confidence: 99%

See 2 more Smart Citations

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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