Journal of Materials Science

, Volume 50, Issue 24, pp 8128–8141 | Cite as

Atomic scale modeling of shock response of fused silica and α-quartz

Original Paper

Abstract

Large-scale molecular dynamics (MD) simulations are carried out using the Tersoff potential to understand the shock wave propagation behavior and the microstructural response of amorphous silica (a-SiO2) and α-quartz. The effect of shock pressure on the densification and phase transformation behavior is investigated using impact velocities of 0.5, 1.0, 1.5, and 2.0 km/s for a-SiO2 and using impact velocities of 2.0 and 3.0 km/s for α-quartz. MD simulations for a-SiO2 suggest that impact velocities of 1.5 km/s and higher result in average pressures that are greater than 9 GPa for the compressed material leading to permanent densification of the material behind the shock front. In addition, the high peak pressures render a phase transformation of the amorphous phase to the high-pressure stishovite phase, and the microstructure corresponds to a heterogeneous mixture of stishovite and liquid SiO2. Spall failure due to the interaction of the reflected tensile waves, however, is not observed for any of the velocities considered for amorphous silica as the peak tensile pressure generated is insufficient to nucleate cracks. This is verified through MD simulations of uniaxial expansion of fused silica to compute the spall strength at the strain rates generated during shock simulations (109 to 1010 s−1). The uniaxial expansion simulations suggest a brittle mode of failure for a-SiO2, as observed experimentally. In comparison, shock-induced densification and phase transformation behavior to the high-pressure stishovite phase are also observed for α-quartz for an impact velocity of 3.0 km/s. The threshold pressures for the densification and phase transformation behavior for amorphous silica and α-quartz compare very well with those observed experimentally.

Keywords

Impact Velocity Shock Front Amorphous Silica Shock Pressure Spall Strength 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The research was sponsored by the University of Connecticut Research Foundation (UCRF) through the Faculty Large Grant funding program. The authors also acknowledge the Booth Engineering Center for Advanced Technology (BECAT) high performance computing resources at the University of Connecticut used to carry out this research.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Materials Science and Engineering, Institute of Materials ScienceUniversity of ConnecticutStorrsUSA
  2. 2.Department of Mechanical EngineeringUniversity of MississippiUniversityUSA

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