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

Article dans une revue avec comité de lecture
Author
FRANZKOWIAK, Jean Eloi
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
119523 DAM Île-de-France [DAM/DIF]
PRUDHOMME, G.
706 Laboratoire sols, solides, structures - risques [Grenoble] [3SR]
119523 DAM Île-de-France [DAM/DIF]
300016 Commissariat à l'énergie atomique et aux énergies alternatives [CEA]
MERCIER, Patrick P.
119523 DAM Île-de-France [DAM/DIF]
LAURIOT, S.
119523 DAM Île-de-France [DAM/DIF]
DUBREUIL, E.
119523 DAM Île-de-France [DAM/DIF]
BERTHE, Laurent
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/15435
DOI
10.1063/1.4997365
Date
2018
Journal
Review of Scientific Instruments

Abstract

A metallic tin plate with a given surface finish of wavelength ' 6 0 m and amplitude h ' 8 m 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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