Tomographic long-distance µPIV to investigate the small scales of turbulence in a jet at high Reynolds number

Fiscaletti, Daniele; Ragni, Daniele; Overmars, Edwin; Westerweel, J.; Elsinga, Gerrit E.

doi:10.1007/s00348-021-03359-5

Cited by 4 publications

(3 citation statements)

References 62 publications

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“…2016; Fiscaletti et al. 2022) is the major issue that limits its application in certain circumstances. The present study evaluates the field correction for the low-sampling-rate tomo-PIV results through DA reconstruction, with much effort being given to the regeneration of the turbulence structures by small scales (SS) injection from the observations.…”

Section: Resultsmentioning

confidence: 99%

“…Although tomo-PIV has become one of the most widely used volumetric measurement techniques in fluid mechanics, the compromise between the measurement domain size and the spatial resolution resulting from the cross-correlation (Lawson & Dawson 2014;Naka et al 2016;Fiscaletti et al 2022) is the major issue that limits its application in certain circumstances. The present study evaluates the field correction for the low-sampling-rate tomo-PIV results through DA reconstruction, with much effort being given to the regeneration of the turbulence structures by small scales (SS) injection from the observations.…”

Section: Super-temporal-resolution Reconstruction For Tomo-piv With S...mentioning

confidence: 99%

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Four-dimensional variational data assimilation of a turbulent jet for super-temporal-resolution reconstruction

He,

Zeng,

Wang

et al. 2024

J. Fluid Mech.

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The super-temporal-resolution (STR) reconstruction of turbulent flows is an important data augmentation application for increasing the data reach in measurement techniques and understanding turbulence dynamics. This paper proposes a data assimilation (DA) strategy based on weak-constraint four-dimensional variation to conduct an STR reconstruction in a turbulent jet beyond the Nyquist limit from given low-sampling-rate observations. Highly resolved large-eddy simulation (LES) data are used to produce synthetic measurements, which are used as observations and for validation. A segregated assimilation procedure is realised to assimilate the initial condition, inflow boundary condition and model error separately. Different types of observational data are tested. The first type is down-sampled LES data containing many small-scale turbulence structures with or without synthetic noise. The DA results show that the temporal variation of the small-scale structures is well recovered even with noise in the observations. The spectra are resolved to a frequency approximately one order of magnitude higher than what can be captured within the Nyquist limit. The second type of observation is low-sampling-rate tomographic particle image velocimetry (tomo-PIV) data with or without the injection of small-scale structures. The modulation between the large-scale structures contained in the tomo-PIV fields and the small scales injected from the observations is improved. The resultant small scales in the STR reconstruction have the characteristics of authentic turbulence to a considerable extent. Additionally, DA yields much smaller errors in the prediction of particle positions when compared with the Wiener filter, demonstrating the great potential for Lagrangian particle tracking in measurement techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Super-temporal-resolution Reconstruction For Tomo-piv With S...mentioning

confidence: 99%

Four-dimensional variational data assimilation of a turbulent jet for super-temporal-resolution reconstruction

He,

Zeng,

Wang

et al. 2024

J. Fluid Mech.

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“…The original aeroacoustic analogy as derived from Lighthill or Lilley can still be used in its original form just before making assumptions for its simplification, with the aim to estimate the acoustic far-field pressure fluctuations in function of the velocity distribution obtained from the vortex-in-cell methodology. A detailed analysis of the capabilities of the methodology to correctly represent the spatial organization and evolution of coherent flow structures can be found in Schneiders et al (2016Schneiders et al ( , 2018, and very recently in Fiscaletti et al (2022). The advantages of the combination of the time-marching methodology with the integral form of the far-field pressure lie behind the possibility to use uncorrelated tomographic velocity fields, thus saving on the costs associated with a high-speed tomographic system.…”

Section: Temporal Jet Evolution By Finite Vortex-in-cell Marchingmentioning

confidence: 99%

Jet noise predictions by time marching of single-snapshot tomographic PIV fields

2022

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This work combines the latest advancements in time marching of 3D vector fields from tomographic particle image velocimetry, with an adapted version of Lighthill’s formulation, for the prediction of far-field jet noise. Three-dimensional velocity vector fields of the jet flow are first reconstructed from a tomographic volume of 4$$\times$$ × 3$$\times$$ × 9.5 $$D_{j}^3$$ D j 3 , with $$D_{j}$$ D j = 5 cm being the jet-exit diameter. (The jet-exit Mach number $$M_{j}$$ M j ranges from 0.10 to 0.20.) The obtained vector fields are then used as input to a recently developed procedure for the time marching of the vorticity field, which relies upon the vortex-in-cell methodology. This yields time series of each three-dimensional velocity field, from which the far-field pressure is computed via Lilley’s acoustic analogy (through evaluation of the Lighthill’s stress tensor). It is shown that the estimate of the far-field noise spectrum compares well with the spectrum measured directly from a far-field microphone in the anechoic A-tunnel facility of TU Delft, in the Strouhal number range from approximately 1 to 12. Graphical Abstract

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Data-informed characterization of spatio-temporal scales in experiments of microconfined high-pressure transcritical turbulence

Hurtán,

Monteiro,

Jofre

et al. 2024

Experimental Thermal and Fluid Science

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Tomographic long-distance µPIV to investigate the small scales of turbulence in a jet at high Reynolds number

Cited by 4 publications

References 62 publications

Four-dimensional variational data assimilation of a turbulent jet for super-temporal-resolution reconstruction

Four-dimensional variational data assimilation of a turbulent jet for super-temporal-resolution reconstruction

Jet noise predictions by time marching of single-snapshot tomographic PIV fields

Data-informed characterization of spatio-temporal scales in experiments of microconfined high-pressure transcritical turbulence

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