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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Uncertainty quantification and sensitivity analysis applications to fuel performance modeling

Best-estimate fuel performance codes such as BISON currently under development at the Idaho National Laboratory, utilize empirical and mechanistic lower-length-scale informed correlations to predict fuel behavior under normal operating and accident reactor conditions. Traditionally, best-estimate results are presented using the correlations with no quantification of the uncertainty in the output metrics of interest. However, there are associated uncertainties in the input parameters and correlations used to determine the behavior of the fuel and cladding under irradiation. Therefore, it is important to perform uncertainty quantification and include confidence bounds on the output metrics that take into account the uncertainties in the inputs. In addition, sensitivity analyses can be performed to determine which input parameters have the greatest influence on the outputs. In this paper we couple the BISON fuel performance code to the DAKOTA uncertainty analysis software to analyze a representative fuel performance problem. The case studied in this paper is based upon rod 1 from the IFA-432 integral experiment performed at the Halden Reactor in Norway. The rodlet is representative of a BWR fuel rod. The input parameters uncertainties are broken into three separate categories including boundary condition uncertainties (e.g., power, coolant flow rate), manufacturing uncertainties (e.g., pellet diameter, cladding thickness), and model uncertainties (e.g., fuel thermal conductivity, fuel swelling). Utilizing DAKOTA, a variety of statistical analysis techniques are applied to quantify the uncertainty and sensitivity of the output metrics of interest. Specifically, we demonstrate the use of sampling methods, polynomial chaos expansions, surrogate models, and variance-based decomposition. The output metrics investigated in this study are the fuel centerline temperature, cladding surface temperature, fission gas released, and fuel rod diameter. The results highlighted the importance of the thermal conductivity of the fuel, the thermal conductivity of the cladding, the gap thickness, the fuel thermal expansion, and the system power factor as influential parameters.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

fuel cycle cost analysis of Th/U MOX fuel in a block-type HTR - 18619

With a rapid development of nuclear energy in these years, the supply of nuclear fuel is becoming more and more important. Thorium regains attention because of its abundant reserves and excellent physical properties. As a next generation reactor, the high temperature reactor (HTR) is a promising choice for thorium utilization because of its deeper burnup, harder spectrum and better inherent safety. Therefore, a 600 MWth 108-column modular high temperature reactor (MHR) core model with 20%UO{sub 2}-80%ThO{sub 2}; MOX fuel is adapted. A traditional two-step calculation scheme, lattice calculation then core calculation, is used to get the neutronic performance of equilibrium cycle including uranium enrichment and mass of fuel. Using these data, the fuel cycle cost of HTR can be calculated by the levelized lifetime cost method. In order to take into account the variation of unit price, a well-established, standard statistical analysis technique is used by Monte Carlo simulation, (thus, the probability distribution of fuel cycle cost can be obtained. Furthermore, sensitivity calculations are made to analyze the impact on the fuel cycle cost of variations in the unit prices for each fuel cycle component. The result shows that the most influential parameters are unit price of natural uranium, enrichment and discount rate. Finally a comparison of fuel cycle cost between 100%UO{sub 2} and 20%UO{sub 2}-80%ThO{sub 2} MOX fuel is made. The latter is 11% cheaper than the former. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES

We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1975

Statistical analysis of trim maneuvers in low thrust interplanetary navigation

An analytical technique for the statistical analysis of a low thrust trim maneuver in interplanetary navigation has been developed. The maneuver is treated rigorously as a nonlinear function of the trajectory errors to be removed, and the corresponding nongaussian statistics are developed. The method determines the statistical properties of the maneuver direction and duration, as well as the final state dispersion covariance. The analytical technique is applied to the terminal maneuver in a 1980 Encke slow flyby mission and is found to yield accurate statistical results with much less effort than Monte Carlo simulation. The method permits consideration of a shorter terminal maneuver than does linear analysis, with a consequent improvement in delivery accuracy.

Rinker, G. C.↗
DOE OSTI2025

Hierarchical Bayesian Inverse Problems: A High-Dimensional Statistics Viewpoint

This paper analyzes hierarchical Bayesian inverse problems using techniques from highdimensional statistics. Furthermore, our analysis leverages a property of hierarchical Bayesian regularizers that we call approximate decomposability to obtain non-asymptotic bounds on the reconstruction error attained by maximum a posteriori estimators. The new theory explains how hierarchical Bayesian models that exploit sparsity, group sparsity, and sparse representations of the unknown parameter can achieve accurate reconstructions in high-dimensional settings.

MAP estimation↗
NASA NTRS1989

Calibration of quadpolarization SAR data using backscatter statistics

A new technique is described for calibration of complex multipolarization SAR imagery. Scatterer reciprocity and lack of correlation between like- and cross-polarized radar echoes for natural targets are used to remove cross-polarized contamination in the radar data channels without the use of known ground targets. If known targets are available, all data channels can be calibrated relative to one another and absolutely as well. The method is verified with airborne SAR data.

Klein, Jeffrey D.↗
DOE OSTI2016

MO-FG-CAMPUS-JeP3-01: A Statistical Model for Analyzing the Rotational Error of Single Iso-Center Technique

Purpose: To develop a generalized statistical model that incorporates the treatment uncertainty from the rotational error of single iso-center technique, and calculate the additional PTV (planning target volume) margin required to compensate for this error. Methods: The random vectors for setup and additional rotation errors in the three-dimensional (3D) patient coordinate system were assumed to follow the 3D independent normal distribution with zero mean, and standard deviations σx, σy, σz, for setup error and a uniform σR for rotational error. Both random vectors were summed, normalized and transformed to the spherical coordinates to derive the chi distribution with 3 degrees of freedom for the radical distance ρ. PTV margin was determined using the critical value of this distribution for 0.05 significant level so that 95% of the time the treatment target would be covered by ρ. The additional PTV margin required to compensate for the rotational error was calculated as a function of σx, σy, σz and σR. Results: The effect of the rotational error is more pronounced for treatments that requires high accuracy/precision like stereotactic radiosurgery (SRS) or stereotactic body radiotherapy (SBRT). With a uniform 2mm PTV margin (or σx =σy=σz=0.7mm), a σR=0.32mm will decrease the PTV coverage from 95% to 90% of the time, or an additional 0.2mm PTV margin is needed to prevent this loss of coverage. If we choose 0.2 mm as the threshold, any σR>0.3mm will lead to an additional PTV margin that cannot be ignored, and the maximal σR that can be ignored is 0.0064 rad (or 0.37°) for iso-to-target distance=5cm, or 0.0032 rad (or 0.18°) for iso-to-target distance=10cm. Conclusions: The rotational error cannot be ignored for high-accuracy/-precision treatments like SRS/SBRT, particularly when the distance between the iso-center and target is large.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2004

Methods and Piezoelectric Imbedded Sensors for Damage Detection in Composite Plates Under Ambient and Cryogenic Conditions

New methods for structural health monitoring are being assessed, especially in high-performance, extreme environment, safety-critical applications. One such application is for composite cryogenic fuel tanks. The work presented here attempts to characterize and investigate the feasibility of using imbedded piezoelectric sensors to detect cracks and delaminations under cryogenic and ambient conditions. A variety of damage detection methods and different Sensors are employed in the different composite plate samples to aid in determining an optimal algorithm, sensor placement strategy, and type of imbedded sensor to use. Variations of frequency, impedance measurements, and pulse echoing techniques of the sensors are employed and compared. Statistical and analytic techniques are then used to determine which method is most desirable for a specific type of damage. These results are furthermore compared with previous work using externally mounted sensors. Results and optimized methods from this work can then be incorporated into a larger composite structure to validate and assess its structural health. This could prove to be important in the development and qualification of any 2" generation reusable launch vehicle using composites as a structural element.

Engberg, Robert↗
DOE OSTI2019

Monitoring Aqueous Reprocessing Systems for Detection of Facility Misuse

Traditional approaches to material control and accounting at used nuclear fuel reprocessing facilities relies in large part on laboratory-based destructive assay of periodic samples taken from key stages. These techniques are highly accurate, but limited inspection frequency may limit the size and operation of a reprocessing facility. Online monitoring techniques using inline detectors can be applied to fuel reprocessing systems to improve the situational awareness as the facility is operating to provide additional indicators of the evolving state of the facility between normal inspection periods, potentially alerting regulators to the need for additional inspection. The Multi-Isotope Process (MIP) Monitor combines medium-resolution gamma detectors with statistical data analysis techniques to monitor reprocessing systems for anomalous conditions resulting from process control issues or malfeasance. Gamma spectra collected from the H-Canyon reprocessing facility were modeled using principal component analysis to demonstrate the efficacy of the MIP Monitor for detecting notional diversion scenarios.

safeguards, nuclear fuel reprocessing, gamma spect↗
NASA NTRS1961

Application of Monte Carlo Technique for Determining Maneuvering Loads from Statistical Information on Airplane Motions

Results of a statistical analysis of horizontal-tail loads on a fighter airplane are presented. The data were obtained from a number of operational training missions with flight at altitudes up to about 50,000 feet and at Mach numbers up to 1.22. The analysis was performed to determine the feasibility of calculating horizontal-tail load from data on the flight conditions and airplane motions. In the analysis the calculated loads are compared with the measured loads for the different types of missions performed. The loads were calculated by two methods: a direct approach and a Monte Carlo technique. The procedures used and some of the problems associated with the data analysis are discussed. frequencies of occurrence of tail loads of given magnitudes are derived from statistical information on the flight quantities. In the direct method, a time history of tail load is calculated from time-history measurements of the flight quantities. The Monte Carlo method could be useful for extending loads information for design of prospective airplanes . For the Monte Carlo method, the The results indicate that the accuracy of loads, regardless of the method used for calculation, is largely dependent on the knowledge of the pertinent airplane aerodynamic characteristics and center-of-gravity location. In addition, reliable Monte Carlo results require an adequate sample of statistical data and a knowledge of the more important statistical dependencies between the various flight conditions and airplane motions.

Hamer, Harold A.↗
NASA NTRS1990

Data analysis techniques

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order.

Park, Steve↗
DOE OSTI2019

Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning

Internal atmospheric variability fundamentally limits predictability of climate and obscures evidence of anthropogenic climate change regionally and on time scales of up to a few decades. Dynamical adjustment techniques estimate and subsequently remove the influence of atmospheric circulation variability on temperature or precipitation. The residual component is expected to contain the thermodynamical signal of the externally forced response but with less circulation-induced noise. Existing techniques have led to important insights into recent trends in regional (hydro-) climate and their drivers, but the variance explained by circulation is often low. Here, we develop a novel dynamical adjustment technique by implementing principles from statistical learning. We demonstrate in an ensemble of Community Earth System Model (CESM) simulations that statistical learning methods, such as regularized linear models, establish a clearer relationship between circulation variability and atmospheric target variables, and need relatively short periods of record for training (around 30 years). The method accounts for, on average, 83% and 78% of European monthly winter temperature and precipitation variability at gridcell level, and around 80% of global mean temperature and hemispheric precipitation variability. We show that the residuals retain forced thermodynamical contributions to temperature and precipitation variability. Accurate estimates of the total forced response can thus be recovered assuming that forced circulation changes are gradual over time. Overall, forced climate response estimates can be extracted at regional or global scales from approximately 3–5 times fewer ensemble members, or even a single realization, using statistical learning techniques. We anticipate the technique will contribute to reducing uncertainties around internal variability and facilitating climate change detection and attribution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis

Purpose: Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means to overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors’ goal was to develop techniques for applying statistical IR to tomosynthesis imaging data. Methods: These techniques include the following: a physics model with a local voxel-pair based prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction. Results: IR improved the detectability of low-contrast and small microcalcifications, reduced cross-plane artifacts, improved spatial resolution, and lowered noise in reconstructed images. Conclusions: Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

9Cr steel visualization and predictive modeling

The goals of this work were to develop tools to visualize and characterize materials information, to explore new alloy compositions and/or processing, to better predict tensile strength and other mechanical properties. The 9–12 wt% Cr (9Cr) steel test data were compiled from several sources. About 3000 records with 30 predictor variables represent 82 unique steel alloy compositions, variations of thermo-mechanical processing steps and temperatures (homogenization, normalization and tempering cycles), test conditions and outcomes. Detailed data processing steps such as visualization and exploratory analysis, including univariate and bivariate analysis, different clustering techniques used to segment the data, statistical post-hoc analysis to verify significance of the findings, feature engineering to identify predictors of importance, and predictive modeling with cross-validation were performed. The outcome of analysis at each step was reviewed in the context of the domain knowledge of this class of steel, to see if there were underlying physical mechanisms that explain statistical relationships. Data analytics techniques and their parameters were fine-tuned to facilitate interpretation of the results as aligned with the insights from domain experts. Here, the ensemble predictive modeling using Random Forest regressor and post-hoc means comparison corroborated domain knowledge on the role of Co which is known to increase strength of steel alloys through solid-solution strengthening and to affect diffusion of alloying elements and precipitates. Alloys with Co content of 0.7–8 wt% had significantly higher mean strength than the ones without (while having Cr locked within 10–11 wt% range in either group and keeping other compositional element ratios fixed). End products of the computational techniques exploration are presented as the tools that can be used in iterative workflow of materials development and testing.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Statistical Evaluation of Time Series Analysis Techniques

The performance of a modified version of NASA's multivariate spectrum analysis program is discussed. A multiple regression model was used to make the revisions. Performance improvements were documented and compared to the standard fast Fourier transform by Monte Carlo techniques.

Benignus, V. A.↗
NASA NTRS1976

Vector wind and vector wind shear models 0 to 27 km altitude for Cape Kennedy, Florida, and Vandenberg AFB, California

The techniques are presented to derive several statistical wind models. The techniques are from the properties of the multivariate normal probability function. Assuming that the winds can be considered as bivariate normally distributed, then (1) the wind components and conditional wind components are univariate normally distributed, (2) the wind speed is Rayleigh distributed, (3) the conditional distribution of wind speed given a wind direction is Rayleigh distributed, and (4) the frequency of wind direction can be derived. All of these distributions are derived from the 5-sample parameter of wind for the bivariate normal distribution. By further assuming that the winds at two altitudes are quadravariate normally distributed, then the vector wind shear is bivariate normally distributed and the modulus of the vector wind shear is Rayleigh distributed. The conditional probability of wind component shears given a wind component is normally distributed. Examples of these and other properties of the multivariate normal probability distribution function as applied to Cape Kennedy, Florida, and Vandenberg AFB, California, wind data samples are given. A technique to develop a synthetic vector wind profile model of interest to aerospace vehicle applications is presented.

Smith, O. E.↗
NASA NTRS2004

Statistical and Prediction modeling of the Ka Band Using Experimental Results from ACTS Propagation Terminals at 20.185 and 27.505 GHZ

With the increase in demand for wireless communication services, most of the operating frequency bands have become very congested. The increase of wireless costumers is only fractional contribution to this phenomenon. The demand for more services such as video streams and internet explorer which require a lot of band width has been a more significant contributor to the congestion in a communication system. One way to increase the amount of information or data per unit of time transmitted with in a wireless communication system is to use a higher radio frequency. However in spite the advantage available in the using higher frequency bands such as, the Ka-band, higher frequencies also implies short wavelengths. And shorter wavelengths are more susceptible to rain attenuation. Until the Advanced Communication Technology Satellite (ACTS) was launched, the Ka- band frequency was virtually unused - the majority of communication satellites operated in lower frequency bands called the C- and Ku- bands. Ka-band is desirable because its higher frequency allows wide bandwidth applications, smaller spacecraft and ground terminal components, and stronger signal strength. Since the Ka-band is a high frequency band, the millimeter wavelengths of the signals are easily degraded by rain. This problem known as rain fade or rain attenuation The Advanced Communication Technology Satellite (ACTS) propagation experiment has collected 5 years of Radio Frequency (RF) attenuation data from December 1993 to November 1997. The objective of my summer work is to help develop the statistics and prediction techniques that will help to better characterize the Ka Frequency band. The statistical analysis consists of seasonal and cumulative five-year attenuation statistics for the 20.2 and 27.5 GHz. The cumulative five-year results give the link outage that occurs for a given link margin. The experiment has seven ground station terminals that can be attributed to a unique rain zone climate. The locations are White Sands, NM, Tampa, Fly Clarksburg, MD, Norman, OK, Ft. Collins, COY Vancouver, BC, and Fairbanks, AK. The analysis will help us to develop and define specific parameters that will help system engineers develop the appropriated instrumentation and structure for a Ka-band wireless communication systems and networks.

Ogunwuyi, Oluwatosin O.↗