Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

For over a decade, velocimetry based techniques have been used to infer the electrical current delivered to dynamic materials properties experiments on pulsed power drivers such as the Z Machine. Though originally developed for planar load geometries, in recent years, inferring the current delivered to cylindrical coaxial loads has become a valuable diagnostic tool for numerous platforms. Presented is a summary of uncertainties that can propagate through the current inference technique when applied to expanding cylindrical anodes. An equation representing quantitative uncertainty is developed which shows the unfold method to be accurate to a few percent above 10 MA of load current.

For over a decade, velocimetry based techniques have been used to infer the electrical current delivered to dynamic materials properties experiments on pulsed power drivers such as the Z Machine. Though originally developed for planar load geometries, in recent years inferring the current delivered to cylindrical coaxial loads has become a valuable diagnostic tool for numerous platforms. Previous work summarized uncertainties that can propagate through the current inference technique when applied to cylindrical anodes. The present work compensates for a known source of error generated when openings (slots) are cut into the cylindrical anode to allow optical diagnostic access to the load anode/cathode gap.

Abstract not provided.

Material strength and moduli can be determined from dynamic high-pressure ramp-release experiments using an indirect method of Lagrangian wave profile analysis of surface velocities. This method, termed self-consistent Lagrangian analysis (SCLA), has been difficult to calibrate and corroborate with other experimental methods. Using nonequilibrium molecular dynamics, we validate the SCLA technique by demonstrating that it accurately predicts the same bulk modulus, shear modulus, and strength as those calculated from the full stress tensor data, especially where strain rate induced relaxation effects and wave attenuation are small. We show here that introducing a hold in the loading profile at peak pressure gives improved accuracy in the shear moduli and relaxation-adjusted strength by reducing the effect of wave attenuation. When rate-dependent effects coupled with wave attenuation are large, we find that Lagrangian analysis overpredicts the maximum unload wavespeed, leading to increased error in the measured dynamic shear modulus. These simulations provide insight into the definition of dynamic strength, as well as a plausible explanation for experimental disagreement in reported dynamic strength values.

Abstract not provided.

Abstract not provided.

Dynamic material properties experiments provide access to the most extreme temperatures and pressures attainable in a laboratory setting; the data from these experiments are often used to improve our understanding of material models at these extreme conditions. We apply Bayesian model calibration to dynamic material property applications where the experimental output is a function: velocity over time. This framework can accommodate more uncertainties and facilitate analysis of new types of experiments relative to techniques traditionally used to analyse dynamic material experiments. However, implementation of Bayesian model calibration requires more sophisticated statistical techniques, because of the functional nature of the output as well as parameter and model discrepancy identifiability. We propose a novel Bayesian model calibration process to simplify and improve the estimation of the material property calibration parameters. Specifically, we propose scaling the likelihood function by an effective sample size rather than modelling the auto–correlation function to accommodate the functional output. Additionally, we propose sensitivity analyses by using the notion of 'modularization' to assess the effect of experiment–specific nuisance input parameters on estimates of the physical parameters. Furthermore, the Bayesian model calibration framework proposed is applied to dynamic compression of tantalum to extreme pressures, and we conclude that the procedure results in simple, fast and valid inferences on the material properties for tantalum.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.