RESUMO
A roughened metallic plate, subjected to intense shock wave compression, gives rise to an expanding ejecta particle cloud. Photonic Doppler velocimetry (PDV), a fiber-based heterodyne velocimeter, is often used to track ejecta velocities in dynamic compression experiments and on nanosecond time scales. Shortly after shock breakout at the metal-vacuum interface, a particular feature observed in many experiments in the velocity spectrograms is what appear to be slow-moving ejecta, below the free-surface velocity. Using Doppler Monte Carlo simulations incorporating the transport of polarization in the ejecta, we show that this feature is likely to be explained by the multiple scattering of light, rather than by possible collisions among particles, slowing down the ejecta. As the cloud expands in a vacuum, the contribution of multiple scattering decreases due to the limited field of view of the pigtailed collimator used to probe the ejecta, showing that the whole geometry of the system must be taken into account in the calculations to interpret and predict PDV measurements.
RESUMO
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.
