Experimental Study Of Shock Waves Driven Matter Ejection On The Free Surface Of A Metal.

Bizeuil, C.; Chapron, P.; Elias, P.; Laurent, B.

doi:10.1117/12.968054

Cited by 1 publication

(1 citation statement)

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“…The fragmentation of a roughened metallic plate under shock-loading has been extensively investigated in the last decade, theoretically [1][2][3][4][5][6] and experimentally. [7][8][9][10][11][12][13] Many physical mechanisms have to be taken into account: spalling, microspalling, melting, and micro-jetting being the main ones. The increasing interest to characterize these surface destruction products has been leading to many developments, such as X-ray shadowgraphy, piezoelectric pins, Asay foils, 14 Miesizing diagnostic, Photonic Doppler Velocimetry (PDV), 15 and holography.…”

Section: Introductionmentioning

confidence: 99%

PDV-based estimation of ejecta particles’ mass-velocity function from shock-loaded tin experiment

Franzkowiak

Prudhomme

Mercier

et al. 2018

Review of Scientific Instruments

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A metallic tin plate with a given surface finish of wavelength λ ≃ 60 μm and amplitude h ≃ 8 μm is explosively driven by an electro-detonator with a shock-induced breakout pressure PSB = 28 GPa (unsupported). The resulting dynamic fragmentation process, the so-called “micro-jetting,” is the creation of high-speed jets of matter moving faster than the bulk metallic surface. Hydrodynamic instabilities result in the fragmentation of these jets into micron-sized metallic particles constituting a self-expanding cloud of droplets, whose areal mass, velocity, and particle size distributions are unknown. Lithium-niobate-piezoelectric sensor measured areal mass and Photonic Doppler Velocimetry (PDV) was used to get a time-velocity spectrogram of the cloud. In this article, we present both experimental mass and velocity results and we relate the integrated areal mass of the cloud to the PDV power spectral density with the assumption of a power law particle size distribution. Two models of PDV spectrograms are described. The first one accounts for the speckle statistics of the spectrum and the second one describes an average spectrum for which speckle fluctuations are removed. Finally, the second model is used for a maximum likelihood estimation of the cloud’s parameters from PDV data. The estimated integrated areal mass from PDV data is found to agree well with piezoelectric results. We highlight the relevance of analyzing PDV data and correlating different diagnostics to retrieve the physical properties of ejecta particles.

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