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

Molnar, Joseph P.; Venkatakrishnan, L.; Schmidt, Bryan E.; Sipkens, Timothy A.; Grauer, Samuel J.

doi:10.1007/s00348-022-03554-y

Cited by 28 publications

(6 citation statements)

References 74 publications

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“…[59][60][61]. However, we find that adaptive techniques yield marginal benefits in the presence of realistic noise [58,62].…”

Section: Particle Extraction and Trackingmentioning

confidence: 75%

“…While PINNs can be used to solve well-posed forward problems, they are especially useful in the context of inverse analysis [50]. 9 Numerous papers have shown the potential of PINNs to regularize reconstructions and recover latent states of a flow [58,62]. Some noteworthy examples of PINN-based velocimetry have come from Han and coworkers [48], who tested flow around a car's side mirror using LaVision's 4D Lagrangian robotic PTV system; Di Leoni et al [102], who reconstructed the shear layer behind a backward-facing step in a water tunnel using sparse STB-based particle tracks; Wang et al [47], who employed a PINN to improve tomographic PIV measurements of the 3D wake behind a hemisphere; and Soto et al [49], who enhanced the temporal resolution of a 2D PIV test using a PINN to fuse synthetic PIV snapshots with fast point probe data.…”

Section: A4 Physics-informed Machine Learningmentioning

confidence: 99%

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Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Zhou

Hong

et al. 2023

Meas. Sci. Technol.

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Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization and tracking of particles is a key source of error in PTV, especially for single camera defocusing, plenoptic imaging, and digital in-line holography (DIH) measurements. To address this, we developed stochastic particle advection velocimetry (SPAV): a statistical data loss that improves the accuracy of PTV. SPAV is based on an explicit particle advection model that predicts particle positions over time as a function of the estimated velocity field. The model can account for non-ideal effects like drag on inertial particles. A statistical data loss that compares the tracked and advected particle positions, accounting for arbitrary localization and tracking uncertainties, is derived and approximated. We implement our approach using a physics-informed neural network, which simultaneously minimizes the SPAV data loss, a Navier–Stokes physics loss, and a wall boundary loss, where appropriate. Results are reported for simulated and experimental DIH-PTV measurements of laminar and turbulent flows. Our statistical approach significantly improves the accuracy of PTV reconstructions compared to a conventional data loss, resulting in an average reduction of error close to 50%. Furthermore, our framework can be readily adapted to work with other data assimilation techniques like state observer, Kalman filter, and adjoint–variational methods.

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“…[59][60][61]. However, we find that adaptive techniques yield marginal benefits in the presence of realistic noise [58,62].…”

Section: Particle Extraction and Trackingmentioning

confidence: 75%

Section: A4 Physics-informed Machine Learningmentioning

confidence: 99%

Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Zhou

Hong

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…The Adam optimizer hyperparameters are kept as default as previously reported by [22]. A convergence criteria is used to stop training when the L varies less than 2% across a 2000-iteration stretch with a 200-iteration running average, similar to the implementation by Molnar et al [19].…”

Section: Methodsmentioning

confidence: 99%

“…PINNs are useful in ensuring flow fields obey physical constraints by using Navier-Stokes, advection-diffusion, and other equations. While PINNs have been employed mainly in the context of computational fluid dynamics, a PINN approach that combines measurement loss and physics loss for BOS measurements has been demonstrated by Molnar et al [18,19]. The use of PINNs for data assimilation and the post-processing of experimental particle tracking data, such as the work of Di Carlo et al [20] and Clark Di Leoni et al [21], is also noted and related to the current effort.…”

Section: Introductionmentioning

confidence: 92%

Investigation of a neural implicit representation tomography method for flow diagnostics

Kelly,

Thurow

2024

Meas. Sci. Technol.

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In this work, a new gridless approach to tomographic reconstruction of 3D flow fields is introduced and investigated. The approach, termed here as FluidNeRF, is based on the concept of volume representation through Neural Radiance Fields (NeRF). NeRF represents a 3D volume as a continuous function using a deep neural network. In FluidNeRF, the neural network is a function of 3D spatial coordinates in the volume and produces an intensity of light per unit volume at that position. The network is trained using the loss between measured and rendered 2D projections similar to other multi-camera tomography techniques. Projections are rendered using an emission-based integrated line-of-sight method where light rays are traced through the volume; the network is used to determine intensity values along the ray. This paper investigates the influence of the NeRF hyperparameters, camera layout and spacing, and image noise on the reconstruction quality as well as the computational cost. A DNS-generated synthetic turbulent jet is used as a ground-truth representative flow field. Results obtained with FluidNeRF are compared to an adaptive simultaneous algebraic reconstruction technique (ASART), which is representative of a conventional reconstruction technique. Results show that FluidNeRF matches or outperforms ASART in reconstruction quality, is more robust to noise, and offers several advantages that make it more flexible and thus suitable for extension to other flow measurement techniques and scaling to larger-scale problems.

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“…The most important benefit of using PINNs is their flexibility. PINNs can deal with any boundary condition or no boundary condition, they do not need to deal with the complex grid designs required to incorporate the kinematics of the immersed body, they are less sensitive to the spatio-temporal resolution and noise, and they can patch the results in regions where velocity field data are not available ( Cai et al, 2021 ; Jin et al, 2021 ; Di Leoni et al, 2022 preprint; Molnar and Grauer, 2022 ; Du et al, 2023 ; Molnar et al, 2023 ; Zhou et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Reconstructing the pressure field around swimming fish using a physics-informed neural network

Calicchia

Mittal

Seo

et al. 2023

Journal of Experimental Biology

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Fish detect predators, flow conditions, environments, and each other through pressure signals. Lateral line ablation is often performed to understand the role of pressure sensing. In this study, the authors propose a non-invasive method for reconstructing the instantaneous pressure field sensed by a fish's lateral line system from 2D particle image velocimetry (PIV) measurements. The method uses a physics-informed neural network (PINN) to predict an optimized solution for the pressure field near and on the fish's body that satisfy both the Navier-Stokes equations and the constraints put forward by the PIV measurements. The method was validated using a direct numerical simulation of a swimming mackerel, Scomber scombrus, and was applied to experimental data of a turning zebrafish, Danio rerio. The results demonstrate that this method is relatively insensitive to the spatio-temporal resolution of the PIV measurements and accurately reconstructs the pressure on the fish's body.

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

Cited by 28 publications

References 74 publications

Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Investigation of a neural implicit representation tomography method for flow diagnostics

Reconstructing the pressure field around swimming fish using a physics-informed neural network

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