State observer data assimilation for RANS with time-averaged 3D-PIV data

Saredi, Edoardo; Ramesh, Nikhilesh Tumuluru; Sciacchitano, Andrea; Scarano, Fulvio

doi:10.1016/j.compfluid.2020.104827

Cited by 14 publications

(11 citation statements)

References 29 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%

“…A similar framework was developed at ONERA by Ali et al [55], who repeatedly solved the RANS equations and sequentially updated turbulence model parameters with a Kalman filter to match synthetic BOS measurements. State observer methods incorporate proportional or proportional-integral feedback, based on measurements of one or more fields, into the governing equations [56]. 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.…”

Section: Physics-informed Bosmentioning

confidence: 99%

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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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Section: Physics-informed Bosmentioning

confidence: 99%

Section: Physics-informed Bosmentioning

confidence: 99%

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

Molnar¹,

Venkatakrishnan²,

Schmidt³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The inherent difficulty in calculating adequately real complex flows is directly linked to the uncertainty with regard to the boundary conditions [23]. The creation of a state observer for the integration of three-dimensional PIV velocity measurements into a CFD simulation has recently been accomplished [24].…”

Section: Introductionmentioning

confidence: 99%

Calculation of the Pressure Field for Turbulent Flow around a Surface-Mounted Cube Using the SIMPLE Algorithm and PIV Data

Pallas

Bouris

2022

Fluids

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The calculation of the pressure field on and around solid bodies exposed to external flow is of paramount importance to a number of engineering applications. However, conventional pressure measurement techniques are inherently linked to problems principally caused by their point-wise and/or intrusive nature. In the present paper, we attempt to calculate a time-averaged two-dimensional pressure field by integrating PIV (particle image velocimetry) velocity measurements into a CFD code and modifying them by the respective correction step of the SIMPLE algorithm. Boundary conditions are applied from the PIV data as a three-layer area of constant velocities adjacent to the boundaries. A novel characteristic of the approach is the straightforward inclusion of the Reynolds stresses into the source terms of the momentum equations, calculated directly from the PIV statistics. The methodology is applied to three regions of the symmetry plane parallel to the main boundary layer flow past a surface-mounted cube. In spite of findings of deviations from the planar 2D flow assumption, the derived pressure fields and the adjusted velocity fields are found to be reliable, while the intrinsic turbulent nature of the flow is considered without modelling the Reynolds stresses.

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“…Nudging may also be categorised as a special case of the proportional-integral-derivative (PID) controller that only includes the proportional contribution. The benefits of considering the temporal integral and derivative of the discrepancies between observations and predictions have been recently investigated by Neeteson & Rival (2020) and Saredi et al (2021).…”

Section: Introductionmentioning

confidence: 99%

“…To efficiently handle experimental data of higher Reynolds number flows, Nakao, Kawashima & Kagawa (2009) considered relying on unsteady RANS (URANS) modelling to perform nudging. Recently, steady nudging in conjunction with RANS was performed by Saredi et al (2021) based on three-dimensional PIV measurements of the mean flow around a wall-mounted bluff obstacle. Nudging proved to be effective in correcting deficiencies in the RANS prediction, such as the overestimation of the extent of the recirculation region past the obstacle.…”

Section: Introductionmentioning

confidence: 99%

Nudging-based data assimilation of the turbulent flow around a square cylinder

et al. 2022

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We investigate the estimation of the turbulent flow around a canonical square cylinder at $Re=22\,000$ based on temporally resolved but spatially sparse velocity data and solving the unsteady Reynolds-averaged Navier–Stokes (URANS) equations. Flow reconstruction from sparse data is achieved through the application of a nudging data assimilation technique. It involves the introduction of a feedback control term in the momentum equations which allows us to drive URANS predictions towards reference data, which are here extracted from a direct numerical simulation. Such a data assimilation approach induces negligible supplementary computational cost compared with that of a standard URANS simulation. The influence of the spatial resolution of the reference data on the reconstruction performances is systematically investigated. Using a spacing of the order of one cylinder length between data points, we already observe synchronisation of the low-frequency vortex shedding between the full reference flow and the one that is estimated by URANS. The present data assimilation procedure allows us to compensate for deficiencies in standard URANS calculations and leads to a significant decrease in temporal and spectral errors as computed by spectral proper orthogonal decomposition. Furthermore, high accuracy in terms of mean-flow prediction by URANS is achieved. When considering spacings between measurements that are of the order of the wavelength of the Kelvin–Helmholtz vortices, such phenomena in the shear layers at the top and bottom of the cylinder are correctly estimated, while they are not self-sustained in standard URANS. The influence of the structure of the feedback control term in the data assimilation procedure is also investigated.

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State observer data assimilation for RANS with time-averaged 3D-PIV data

Cited by 14 publications

References 29 publications

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

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

Calculation of the Pressure Field for Turbulent Flow around a Surface-Mounted Cube Using the SIMPLE Algorithm and PIV Data

Nudging-based data assimilation of the turbulent flow around a square cylinder

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