Vertical digital holographic bench for underexpanded jet gas density reconstruction

Olchewsky, François; Desse, Jean-Michel; Donjat, David; Champagnat, Frédéric

doi:10.1364/dh.2019.th2b.3

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“…• local time-resolved measurements, such as hot-wire sensor (Fernandez et al (2018)) or Laser Doppler Anemometry (Bisgaard (1983)) which allow to capture velocity signals at high frequency (20 kHz) • space-resolved flow field, such as 2D PIV (or Stereo-PIV on a laser sheet Foucaut et al (2009),Béra et al (2001)) which make possible to obtain velocity fields space-resolved but at a lower frequency (conventionally 2 kHz for high-speed PIV) and only in a two dimensions plan, or refractive index gradient-based methods (shadowgraphy Emerick et al (2012), Schlieren or holography Olchewsky et al (2019)) which enable the capture of pressure fields (or pressure gradient fields) but not velocity fields.…”

Section: Introductionmentioning

confidence: 99%

Pulsed jet phase-averaged flow field estimation based on neural network approach

et al. 2021

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Single hot-wire velocity measurements have been conducted along a three-dimensional measurement grid to capture the flow-field induced by a 45°inclined slotted pulsed jet. Based on the periodic behavior of the flow, two different estimation methods have been implemented. The first one, considered as the reference base-line, is the conditional approach which consists in the redistribution of the experimental data into spaceand time-resolved three-dimensional velocity fields. The second one uses a neural network to estimate 3D velocity fields given spatial coordinates and time. This paper compares the two methods for a complete flow-field estimation based on hot-wire measurements. Results suggest that the neural network is tailored to capture the phase-averaged dynamic response of the jet induced by the actuator, and identify the coherent structures in the flow field. Interesting performances are also observed when degrading the learning database, meaning that neural networks can be used to drastically improve the temporal or spatial resolution of a flow field estimation compared to the experimental data resolution.

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Section: Introductionmentioning

confidence: 99%

Pulsed jet phase-averaged flow field estimation based on neural network approach

et al. 2021

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Vertical digital holographic bench for underexpanded jet gas density reconstruction

Abstract: The built of a multi-viewpoint digital holographic bench permits to study snapshots of a three-dimensional underexpanded jet. The coincidence in time between the measurements allows to study unsteadiness of the jet and explain Fluid Dynamics phenomenon.

Cited by 1 publication

References 2 publications

Pulsed jet phase-averaged flow field estimation based on neural network approach

Pulsed jet phase-averaged flow field estimation based on neural network approach

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