Isotope identification algorithms that are contained in the Gamma Detector Response and Analysis Software (GADRAS) can be used for real-time stationary measurement and search applications on platforms operating under Linux or Android operating sys-tems. Since the background radiation can vary considerably due to variations in natu-rally-occurring radioactive materials (NORM), spectral algorithms can be substantial-ly more sensitive to threat materials than search algorithms based strictly on count rate. Specific isotopes or interest can be designated for the search algorithm, which permits suppression of alarms for non-threatening sources, such as such as medical radionuclides. The same isotope identification algorithms that are used for search ap-plications can also be used to process static measurements. The isotope identification algorithms follow the same protocols as those used by the Windows version of GADRAS, so files that are created under the Windows interface can be copied direct-ly to processors on fielded sensors. The analysis algorithms contain provisions for gain adjustment and energy lineariza-tion, which enables direct processing of spectra as they are recorded by multichannel analyzers. Gain compensation is performed by utilizing photopeaks in background spectra. Incorporation of this energy calibration tasks into the analysis algorithm also eliminates one of the more difficult challenges associated with development of radia-tion detection equipment.
This report demonstrates versatile and practical model validation and uncertainty quantification techniques applied to the accuracy assessment of a computational model of heated steel pipes pressurized to failure. The Real Space validation methodology segregates aleatory and epistemic uncertainties to form straightforward model validation metrics especially suited for assessing models to be used in the analysis of performance and safety margins. The methodology handles difficulties associated with representing and propagating interval and/or probabilistic uncertainties from multiple correlated and uncorrelated sources in the experiments and simulations including: material variability characterized by non-parametric random functions (discrete temperature dependent stress-strain curves); very limited (sparse) experimental data at the coupon testing level for material characterization and at the pipe-test validation level; boundary condition reconstruction uncertainties from spatially sparse sensor data; normalization of pipe experimental responses for measured input-condition differences among tests and for random and systematic uncertainties in measurement/processing/inference of experimental inputs and outputs; numerical solution uncertainty from model discretization and solver effects.
We found that the wetting and spreading of molten filler materials (pure Al, pure Ag, and AgAl alloys) on a Kovar ™ (001) substrate was studied with molecular dynamics simulations. A suite of different simulations was used to understand the effects on spreading rates due to alloying as well as reactions with the substrate. Moreover, the important conclusion is that the presence of Al in the alloy enhances the spreading of Ag, while the Ag inhibits the spreading of Al.
The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quanti cation, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.
The wedge geometry is a simple geometry for establishing a relatively constant gradient of strain in a forged part. The geometry is used to establish gradients in microstructure and strength as a function of strain, forging temperature, and quenching time after forging. This geometry has previously been used to benchmark predictions of strength and recrystallization using Sandias materials model for type 304L austenitic stainless steel. In this report, the processing conditions, in particular the times to forge and quench the forged parts, are summarized based on information recorded during forging on June 18, 2013 of the so-called wedge geometry from type 316L and 21Cr-6Ni-9Mn austenitic stainless steels.