A continuously scanning spatiotemporal averaging method for obtaining volumetric mean flow measurements with stereoscopic PIV

Zigunov, Fernando; Seckin, Serdar; Huss, Rhylan; Eggart, Cameron; Alvi, Farrukh S.

doi:10.1007/s00348-023-03596-w

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…2020; Zigunov et al. 2023). This is done simply by feeding the successive snapshots , taken during each alternating forward and backward scan (see figure 1), at times equally spaced by , where for H1 and for H4.…”

Section: Methodsmentioning

confidence: 99%

“…A key strength of this PINN is its natural ability to reconstruct truly instantaneous three-dimensional flow fields by overcoming the spanwise distortion of the nearinstantaneous data acquired by scanning (as also attempted by Knutsen et al 2020;Zigunov et al 2023). This is done simply by feeding the n y successive snapshots (u, ρ)(x, y i , z, t i ), taken during each alternating forward and backward scan (see figure 1), at times t i equally spaced by t/n y , where ( t, n y ) = (2.29, 39) for H1 and (1.08, 30) for H4.…”

Section: The Pinnmentioning

confidence: 99%

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New insights into experimental stratified flows obtained through physics-informed neural networks

Zhu,

Jiang,

Lefauve

et al. 2024

J. Fluid Mech.

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We develop a physics-informed neural network (PINN) to significantly augment state-of-the-art experimental data of stratified flows. A fully connected deep neural network is trained using time-resolved experimental data in a salt-stratified inclined duct experiment, consisting of three-component velocity fields and density fields measured simultaneously in three dimensions at Reynolds number $= O(10^3)$ and at Prandtl or Schmidt number $=700$ . The PINN enforces incompressibility, the governing equations for momentum and buoyancy, and the boundary conditions at the duct walls. These physics-constrained, augmented data are output at an increased spatio-temporal resolution and demonstrate five key results: (i) the elimination of measurement noise; (ii) the correction of distortion caused by the scanning measurement technique; (iii) the identification of weak but dynamically important three-dimensional vortices of Holmboe waves; (iv) the revision of turbulent energy budgets and mixing efficiency; and (v) the prediction of the latent pressure field and its role in the observed asymmetric Holmboe wave dynamics. These results mark a significant step forward in furthering the reach of experiments, especially in the context of stratified turbulence, where accurately computing three-dimensional gradients and resolving small scales remain enduring challenges.

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Section: Methodsmentioning

confidence: 99%

Section: The Pinnmentioning

confidence: 99%

New insights into experimental stratified flows obtained through physics-informed neural networks

Zhu,

Jiang,

Lefauve

et al. 2024

J. Fluid Mech.

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Four-dimensional particle tracking velocimetry measurements of unsteady three-dimensional vortex onset and progression for 5415 straight ahead, static drift, and pure sway

Sanada,

Starman,

Bhushan

et al. 2023

Physics of Fluids

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Large-scale towed system four-dimensional particle tracking velocimetry (4DPTV) implementation and measurements are presented for towing tank tests for a naval combatant 5415 ship model for straight ahead, static drift β = 10°, and pure sway βmax = 10° conditions. The results, for the first time, provide instantaneous volumetric flow field data around the ship model including near the hull surface, and a complete description of the sonar dome 3D unsteady vortex separation onset and progression due to the 4DPTV larger measurement volume and higher data rates in comparison with previous results using tomographic particle image velocimetry (TPIV); thereby, providing the essential data required for the assessment of current hybrid Reynolds-averaged Navier–Stokes/large eddy simulation turbulence modeling capabilities and guidance for the necessary modeling improvements. The 4DPTV system is summarized and compared with the previous TPIV, including the camera calibration procedures, trigger systems, and synchronization with the sway motion. Analysis procedures, including data reduction procedures and the 3D vortex core detection and voxel labeling techniques, are described. The identification of the complex vortex separations and breakdown is aided using complementary detached eddy simulations. The vortex–vortex interaction process is visualized from instantaneous volumetric datasets. The pros and cons of the 4DPTV vs TPIV, the comparison between the second- and third-generation vortex visualization technique, and the statistical convergence error analysis of sonar dome port vortex for β = 10° are discussed. Plans for increased spatial resolution of the 4DPTV system and additional data reduction techniques for detailed turbulence analysis are also discussed.

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A fast, matrix-based method to perform omnidirectional pressure integration

Zigunov,

Charonko

2024

Meas. Sci. Technol.

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Experimentally-measured pressure fields play an important role in understanding many fluid dynamics problems.Unfortunately, pressure fields are difficult to measure directly with non-invasive, spatially resolved diagnostics, and calculations of pressure from velocity have proven sensitive to error in the data. Omnidirectional line integration methods are usually more accurate and robust to these effects as compared to implicit Poisson equations, but have seen slower uptake due to the higher computational and memory costs, particularly in 3D domains. This paper demonstrates how omnidirectional line integration approaches can be converted to a matrix inversion problem.This novel formulation uses an iterative approach so that the boundary conditions are updated each step, preserving the convergence behavior of omnidirectional schemes while also keeping the computational efficiency of Poisson solvers. This method is implemented in Matlab and also as a GPU-accelerated code in CUDA-C++. The behavior of the new method is demonstrated on 2D and 3D synthetic and experimental data. Three-dimensional grid sizes of up to 125 million grid points are tractable with this method, opening exciting opportunities to perform volumetric pressure field estimation from 3D PIV measurements.

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A continuously scanning spatiotemporal averaging method for obtaining volumetric mean flow measurements with stereoscopic PIV

Cited by 7 publications

References 26 publications

New insights into experimental stratified flows obtained through physics-informed neural networks

New insights into experimental stratified flows obtained through physics-informed neural networks

Four-dimensional particle tracking velocimetry measurements of unsteady three-dimensional vortex onset and progression for 5415 straight ahead, static drift, and pure sway

A fast, matrix-based method to perform omnidirectional pressure integration

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