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.