Shock Properties Characterization of Dielectric Materials Using Millimeter-Wave Interferometry and Convolutional Neural Networks.

In this paper, a neural network approach is applied for solving an electromagnetic inverse problem involving solid dielectric materials subjected to shock impacts and interrogated by a millimeter-wave interferometer. Under mechanical impact, a shock wave is generated in the material and modifies the refractive index. It was recently demonstrated that the shock wavefront velocity and the particle velocity as well as the modified index in a shocked material can be remotely derived from measuring two characteristic Doppler frequencies in the waveform delivered by a millimeter-wave interferometer. We show here that a more accurate estimation of the shock wavefront and particle velocities can be obtained from training an appropriate convolutional neural network, especially in the important case of short-duration waveforms of few microseconds.

Toward Improvements in Pressure Measurements for Near Free-Field Blast Experiments.

This paper proposes two ways to improve pressure measurement in air-blast experimentations, mostly for close-in detonations defined by a small-scaled distance below 0.4 m.kg-1/3. Firstly, a new kind of custom-made pressure probe sensor is presented. The transducer is a piezoelectric commercial, but the tip material has been modified. The dynamic response of this prototype is established in terms of time and frequency responses, both in a laboratory environment, on a shock tube, and in free-field experiments. The experimental results show that the modified probe can meet the measurement requirements of high-frequency pressure signals. Secondly, this paper presents the initial results of a deconvolution method, using the pencil probe transfer function determination with a shock tube. We demonstrate the method on experimental results and draw conclusions and prospects.