Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNon-Iterative Characteristics Analysis for High-Pressure Ramp Loading
170
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
In the canonical ramp compression experiment, a smoothly-increasing load is applied to the surface of the sample, and the particle velocity history is measured at two or more different distances into the sample, at interfaces where the surface of the sample can be probed. The velocity histories are used to deduce a stress-density relation, usually using iterative Lagrangian analysis to account for the perturbing effect of the impedance mismatch at the interface. In that technique, a stress- density relation is assumed in order to correct for the perturbation, and is adjusted until it becomes consistent with the deduced stress-density relation. This process is subject to the usual difficulties of nonlinear optimization, such as the existence of local minima (sensitivity to the initial guess), possible failure to converge, and relatively large computational effort. We show that, by considering the interaction of successive characteristics reaching the interfaces, the stress-density relation can be deduced directly by recursion rather than iteration. This calculation is orders of magnitude faster than iterative analysis, and does not require an initial guess. Direct recursion may be less suitable for very noisy data, but it was robust when applied to trial data. The stress-density relation deduced was identical to the result from iterative Lagrangian analysis.
rate research
Read More
Ramp compression experiment are used to deduce the relation between compression and normal stress in a material, by measuring how a compression wave evolves as it propagates through different thicknesses of the sample material. The compression wave is generally measured by Doppler velocimetry from a surface that can be observed with optical or near-optical photons. For high-pressure ramp loading, the reflectivity of a free surface often decreases as it is accelerated by the ramp wave, and window materials transparent to the probing photons are used to keep the surface flatter and preserve its reflectivity. We previously described a method of analyzing ramp-wave data measured at the free surface which did not require numerical iteration. However, this method breaks down when the pressure at the surface changes and hence cannot be used for data taken with a finite-impedance window. We have now generalized this non-iterative analysis method to apply to measurements taken through a window. Free surfaces can be treated seamlessly, and the need for sampling at uniform intervals of velocity has been removed. These calculations require interpolation of partially-released states using the partially-constructed stress-compression relation, making them slower than the previous free-surface scheme, but they are still much more robust and fast than iterative analysis.
Diamond is used extensively as a component in high energy density experiments, but existing equation of state (EOS) models do not capture its observed response to dynamic loading. In particular, in contrast with first principles theoretical EOS models, no solid-solid phase changes have been detected, and no general-purpose EOS models match the measured ambient isotherm. We have performed density functional theory (DFT) calculations of the diamond phase to ~10TPa, well beyond its predicted range of thermodynamic stability, and used these results as the basis of a Mie-Greuneisen EOS. We also performed DFT calculations of the elastic moduli, and calibrated an algebraic elasticity model for use in simulations. We then estimated the flow stress of diamond by comparison with the stress-density relation measured experimentally in ramp-loading experiments. The resulting constitutive model allows us to place a constraint on the Taylor-Quinney factor (the fraction of plastic work converted to heat) from the observation that diamond does not melt on ramp compression.
The phase diagram of oxygen is investigated for pressures from 50 to 130~GPa and temperatures up 1200 K using first principles theory. A metallic molecular structure with the $P6_3/mmc$ symmetry ($eta^{}$ phase) is determined to be thermodynamically stable in this pressure range at elevated temperatures above the $epsilon$(${O_8}$) phase. Long-standing disagreements between theory and experiment for the stability of $epsilon$(${O_8}$), its metallic character, and the transition pressure to the $zeta$ oxygen phase are resolved. Crucial for obtaining these results are the inclusion of anharmonic lattice dynamics effects and accurate calculations of exchange interactions in the presence of thermal disorder.
We introduce a Python framework designed to automate the most common tasks associated with the extraction and upscaling of the statistics of single-impact crater functions to inform coefficients of continuum equations describing surface morphology evolution. Designed with ease-of-use in mind, the framework allows users to extract meaningful statistical estimates with very short Python programs. Wrappers to interface with specific simulation packages, routines for statistical extraction of output, and fitting and differentiation libraries are all hidden behind simple, high-level user-facing functions. In addition, the framework is extensible, allowing advanced users to specify the collection of specialized statistics or the creation of customized plots. The framework is hosted on the BitBucket service under an open-source license, with the aim of helping non-specialists easily extract preliminary estimates of relevant crater function results associated with a particular experimental system.
Tomography has made a radical impact on diverse fields ranging from the study of 3D atomic arrangements in matter to the study of human health in medicine. Despite its very diverse applications, the core of tomography remains the same, that is, a mathematical method must be implemented to reconstruct the 3D structure of an object from a number of 2D projections. In many scientific applications, however, the number of projections that can be measured is limited due to geometric constraints, tolerable radiation dose and/or acquisition speed. Thus it becomes an important problem to obtain the best-possible reconstruction from a limited number of projections. Here, we present the mathematical implementation of a tomographic algorithm, termed GENeralized Fourier Iterative REconstruction (GENFIRE). By iterating between real and reciprocal space, GENFIRE searches for a global solution that is concurrently consistent with the measured data and general physical constraints. The algorithm requires minimal human intervention and also incorporates angular refinement to reduce the tilt angle error. We demonstrate that GENFIRE can produce superior results relative to several other popular tomographic reconstruction techniques by numerical simulations, and by experimentally by reconstructing the 3D structure of a porous material and a frozen-hydrated marine cyanobacterium. Equipped with a graphical user interface, GENFIRE is freely available from our website and is expected to find broad applications across different disciplines.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
