skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: DARHT Radiographic Grid Scale Correction

Abstract

Recently it became apparent that the radiographic grid which has been used to calibrate the dimensional scale of DARHT radiographs was not centered at the location where the objects have been centered. This offset produced an error of 0.188% in the dimensional scaling of the radiographic images processed using the assumption that the grid and objects had the same center. This paper will show the derivation of the scaling correction, explain how new radiographs are being processed to account for the difference in location, and provide the details of how to correct radiographic image processed with the erroneous scale factor.

Authors:
 [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1170264
Report Number(s):
LA-UR-15-21064
DOE Contract Number:
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Warthen, Barry J. DARHT Radiographic Grid Scale Correction. United States: N. p., 2015. Web. doi:10.2172/1170264.
Warthen, Barry J. DARHT Radiographic Grid Scale Correction. United States. doi:10.2172/1170264.
Warthen, Barry J. Fri . "DARHT Radiographic Grid Scale Correction". United States. doi:10.2172/1170264. https://www.osti.gov/servlets/purl/1170264.
@article{osti_1170264,
title = {DARHT Radiographic Grid Scale Correction},
author = {Warthen, Barry J.},
abstractNote = {Recently it became apparent that the radiographic grid which has been used to calibrate the dimensional scale of DARHT radiographs was not centered at the location where the objects have been centered. This offset produced an error of 0.188% in the dimensional scaling of the radiographic images processed using the assumption that the grid and objects had the same center. This paper will show the derivation of the scaling correction, explain how new radiographs are being processed to account for the difference in location, and provide the details of how to correct radiographic image processed with the erroneous scale factor.},
doi = {10.2172/1170264},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Feb 13 00:00:00 EST 2015},
month = {Fri Feb 13 00:00:00 EST 2015}
}

Technical Report:

Save / Share:
  • The Dual Axis Radiographic Hydrodynamic Test (DARHT) injector system was designed, constructed and tested in the dummy load configuration at Pulse Sciences, Inc. (PSI), San Leandro, CA for Los Alamos National Laboratories (LANL) during the period from September 1989 through December 1990. The injector was installed and its operation was demonstrated in the dummy load configuration at LANL from January 1991 through April 1991. Testing of the system configuration into a diode load began in June 1991. Cross-sectional views of the injector in both the dummy load and system configurations are shown. The injector is designed to produce a 4more » MV, flat-top ([plus minus] 1%), 65 nsec (99--99%) acceleration pulse into a 150 ohm load with a command fire jitter of less than 3 nsec (3[sigma]). The load consists of an adjustable sodium thiosulfate solution resistor located at the vacuum tube interface in parallel with an [approximately]1 k[Omega] electron beam diode. This manual describes the injector and its ancillary systems and gives operating, maintenance and assembly instructions for the system in the dummy load configuration.« less
  • Rodents are effective indicators of environmental contamination and the Dual-Axis Radiographic Hydrodynamic Test (DARHT) Facility Mitigation Action Plan specifies the (radionuclide) comparison of small mammals to baseline levels to determine if there are any impacts as a result of operations. Consequently, samples of (whole body) field mice (Peromyscus spp.) were collected from within the grounds of the DARHT facility at Los Alamos National Laboratory, Technical Area 15, from 2001 through 2003. Samples were analyzed for {sup 3}H, {sup 137}Cs, {sup 90}Sr, {sup 241}Am, {sup 238}Pu, {sup 239,240}Pu, {sup 234}U, {sup 235}U, and {sup 238}U. Results, which represent three years sincemore » the start of operations in 2000, were compared with baseline statistical reference level (BSRL) data established over a four-year-long preoperational period. Most radionuclides in mice were either at nondetectable levels or within BSRLs. The few radionuclides that were above BSRLs included U isotopes; and the ratios of some samples indicated depleted U sources. Although the amounts of U in some samples were just above BSRLs, and since depleted U is less soluble and less toxic (chemical and radioactive) than naturally occurring U, the very small levels in the mice collected around the DARHT facility grounds are unlikely to pose a threat to predators that feed upon them.« less
  • This white paper introduces the application of advanced data analytics to the modernized grid. In particular, we consider the field of machine learning and where it is both useful, and not useful, for the particular field of the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper wemore » consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid and the building-to-grid interface. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. Machine learning is a subfield of computer science that studies and constructs algorithms that can learn from data and make predictions and improve forecasts. Incorporation of machine learning in grid monitoring and analysis tools may have the potential to solve data and operational challenges that result from increasing penetration of distributed and behind-the-meter energy resources. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors – such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals – such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop.« less
  • A sub-grid mix model based on a volume-of-fluids (VOF) representation is described for computational simulations of the transient mixing between reactive fluids, in which the atomically mixed components enter into the reactivity. The multi-fluid model allows each fluid species to have independent values for density, energy, pressure and temperature, as well as independent velocities and volume fractions. Fluid volume fractions are further divided into mix components to represent their 'mixedness' for more accurate prediction of reactivity. Time dependent conversion from unmixed volume fractions (denoted cf) to atomically mixed (af) fluids by diffusive processes is represented in resolved scale simulations withmore » the volume fractions (cf, af mix). In unresolved scale simulations, the transition to atomically mixed materials begins with a conversion from unmixed material to a sub-grid volume fraction (pf). This fraction represents the unresolved small scales in the fluids, heterogeneously mixed by turbulent or multi-phase mixing processes, and this fraction then proceeds in a second step to the atomically mixed fraction by diffusion (cf, pf, af mix). Species velocities are evaluated with a species drift flux, {rho}{sub i}u{sub di} = {rho}{sub i}(u{sub i}-u), used to describe the fluid mixing sources in several closure options. A simple example of mixing fluids during 'interfacial deceleration mixing with a small amount of diffusion illustrates the generation of atomically mixed fluids in two cases, for resolved scale simulations and for unresolved scale simulations. Application to reactive mixing, including Inertial Confinement Fusion (ICF), is planned for future work.« less
  • This report describes the US Stockpile Stewardship Program which is meant to sustain and evaluate nuclear weapon stockpile with no underground nuclear tests. This research will focus on DARHT, the Dual Axis Radiographic Hydrodynamic Test facility.