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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Qualitative and Quantitative Evaluation for Representative Human Reliability Analysis Methods

The Korea Institute of Nuclear Safety (KINS) is the regulatory expert organization established by the Korean government to strengthen the nation’s technical capabilities relating to nuclear safety regulation. KINS oversees the technical aspects of nuclear safety regulation, including safety reviews, inspections, education, and safety research—all conducted based on technical knowledge and accumulated regulatory experience. In 2023, KINS requested that Idaho National Laboratory (INL) validates representative human reliability analysis (HRA) methods used throughout the world, thus affording KINS with a basis for determining an HRA method adequate for its domestic regulatory purposes. The present paper mainly examines INL’s efforts in this regard. The resulting INL study covered four representative HRA methods widely used by nuclear utilities and regulatory institutes. These methods were qualitatively evaluated by applying specific evaluation criteria and determining how well each method reflected critical HRA issues. For this assessment, INL benchmarked the Halden International HRA Empirical Study. Using the Halden empirical data, along with information on human failure events (HFEs), the present study employed the selected HRA methods to estimate human error probabilities (HEPs) for the HFEs. It also performed statistical analyses to compare the HEPs predicted via the HRA methods against those from the Halden empirical data.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

The Davis–Chandrasekhar–Fermi method revisited

ABSTRACT Despite the rich observational results on interstellar magnetic fields in star-forming regions, it is still unclear how dynamically significant the magnetic fields are at varying physical scales, because direct measurement of the field strength is observationally difficult. The Davis–Chandrasekhar–Fermi (DCF) method has been the most commonly used method to estimate the magnetic field strength from polarization data. It is based on the assumption that gas turbulent motion is the driving source of field distortion via linear Alfvén waves. In this work, using MHD simulations of star-forming clouds, we test the validity of the assumption underlying the DCF method by examining its accuracy in the real 3D space. Our results suggest that the DCF relation between turbulent kinetic energy and magnetic energy fluctuation should be treated as a statistical result instead of a local property. We then develop and investigate several modifications to the original DCF method using synthetic observations, and propose new recipes to improve the accuracy of DCF-derived magnetic field strength. We further note that the biggest uncertainty in the DCF analysis may come from the linewidth measurement instead of the polarization observation, especially since the line-of-sight gas velocity can be used to estimate the gas volume density, another critical parameter in the DCF method.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Measuring photometric redshifts for high-redshift radio source surveys

With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to z = 3 before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify ≈ 76% of the highest redshift sources—sources at redshift z > 2.51 —as being in either the highest bin (z > 2.51) or second highest (z = 2.25).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Mapping tree height in complex terrain of northern China using ultra-high-resolution images

Tree height is a key parameter for estimating forest biomass and carbon sequestration. In recent years, notable progress has been made in mapping tree height using satellite imagery. However, existing tree height products show low accuracy in mountainous and complex terrains, and few studies typically addressed tree height estimations in mountain areas. This study examines the Mentougou district of Beijing, China, characterized by complex terrain and mountainous landscapes. We analyzed two methods for estimating tree height: one using only spectral features and another combining spectral features with topographic factors (elevation, slope, aspect). We used 3-m resolution PlanetScope 8-band multispectral imagery, with 710 field-measured individual tree heights averaged to obtain 471 pixel-level tree height values as ground-truth, to develop tree height prediction models using eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Gradient Boosting Machine (GBM) models. The results show that the XGBoost model consistently presented the highest accuracy for both methods evaluated. Specifically, the XGBoost model that combined spectral data with elevation and slope variables with an R² of 0.75 and an RMSE of 2.69 m. Using the XGBoost model, we generated the tree height map for the Mentougou area at 3 m resolution, showing tree heights ranging from 0.5 to 30.4 m, and the model’s prediction error standard deviations ranged from 2.50 to 4.71 m, indicating reliable performance across varied terrain. Additionally, we compared and evaluated the global tree height products, identifying limitations in the accuracy within complex terrains. This study demonstrates the potential for accurately predicting tree heights by combining high-resolution multispectral satellites with a terrain factor modeling approach.

Complex terrain↗
DOE OSTI2020

Physically based correction of systematic errors of Rotating Shadowband Irradiometers

Accurate measurements of direct normal, diffuse horizontal and global horizontal irradiance (DNI, DHI and GHI) are needed for meteorological studies and are essential for the solar resource assessment at potential solar power plant sites. Often, these potential sites are remote and hence require robust sensors that require minimal maintenance that are not affected strongly by soiling. Therefore, Rotating Shadowband Irradiometers (RSI) are widely used for resource assessment. To achieve the required accuracy, corrections for the raw values of RSIs depending on systematic temperature, incidence angle and spectral errors must be used, and a thorough calibration of the sensor head must be applied. The existing correction functions are derived from comparisons of RSIs to thermopile radiometers at selected sites and therefore empirical. Their accuracy is considered to be site dependent. In this work a new correction and calibration method is presented that removes the systematic errors using a physical approach. It is based on information of the sensor properties as well as measurements of its directional response, and incorporates the atmospheric conditions at the measurement site. In this case, no empiric relations obtained from a specific site are required. The method requires estimates of the current DHI and GHI spectra during each measurement of the RSI. Based on these spectra, a spectral correction, which includes a spectrum dependent temperature correction, can be made without employing empirical relationships. The new physical calibration and correction method is tested at three sites and reaches similar results compared to the empirical functions. This is already achieved with rudimentary estimations of the GHI and DHI spectra and we expect that these estimations can be improved in the future. The results indicate that the physical approach reduces the problematic location dependence of the current calibration and correction methods. The physical correction and calibration method show promise for a further improvement of the RSI accuracy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Detecting outbreaks using a spatial latent field

In this paper, we present a method for estimating the infection-rate of a disease as a spatial-temporal field. Our data comprises time-series case-counts of symptomatic patients in various areal units of a region. We extend an epidemiological model, originally designed for a single areal unit, to accommodate multiple units. The field estimation is framed within a Bayesian context, utilizing a parameterized Gaussian random field as a spatial prior. We apply an adaptive Markov chain Monte Carlo method to sample the posterior distribution of the model parameters condition on COVID-19 case-count data from three adjacent counties in New Mexico, USA. Our results suggest that the correlation between epidemiological dynamics in neighboring regions helps regularize estimations in areas with high variance (i.e., poor quality) data. Using the calibrated epidemic model, we forecast the infection-rate over each areal unit and develop a simple anomaly detector to signal new epidemic waves. Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.

Safta, Cosmin [Sandia National Laboratories (SNL-C↗
DOE OSTI2023

A verification study of fatigue-based methods in API RP 1183 for estimating fatigue life of pipeline dents

Pipeline dents are mechanical damage caused by a third party in buried pipelines during construction or maintenance repair. Dents are recognized as a leading cause of pipeline failures for both liquid and gas transmission lines, and their assessment and management are critical to pipeline integrity. To meet the industry need, American Institute of Petroleum (API) in 2020 published a recommended practice (RP) 1183 – Assessment and Management of Pipeline Dents. This API code provides the pipeline industry “standard methods” for evaluating severity and fatigue life of pipeline dents with a single peak. This paper performed an initial verification study of fatigue-based methods prescribed in API RP 1183 for estimating fatigue life of pipeline dents. A brief review was first given to the dent fatigue-based methods, including three dent screening methods for estimating dent fatigue life in Sections 7.4.1, 7.4.2, and 7.4.3 of API 1183 and one dent assessment method for predicting fatigue life in Section 8.3.4. In order to verify these fatigue life prediction methods, a set of “standard examples” of dents provided in Annexes A.1 to A.5 of API RP 1183 were utilized in this study to calculate fatigue life, to determine restraint condition, and to evaluate both screening and assessment methods for single-peak dents. Through detailed calculations and comparisons, this study confirmed some specifications of API RP 1183, but also found some self-inconsistences and errors existing in this new API code. Furthermore, this includes the self-inconsistences between three screening methods and the self-inconsistence between the screening and assessment methods. On this basis, recommendations were made on how the operators should use API RP 1183, and what actions API should take to improve this code.

42 ENGINEERING↗
DOE OSTI2022

Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy

In this work, we develop several inference methods to estimate the position of dislocations from images generated using dark-field X-ray microscopy (DFXM)—achieving superresolution accuracy and principled uncertainty quantification. Using the framework of Bayesian inference, we incorporate models of the DFXM contrast mechanism and detector measurement noise, along with initial position estimates, into a statistical model coupling DFXM images with the dislocation position of interest. We motivate several position estimation and uncertainty quantification algorithms based on this model. We then demonstrate the accuracy of our primary estimation algorithm on synthetic realistic DFXM images of edge dislocations in single-crystal aluminum. We conclude with a discussion of our methods’ impact on future dislocation studies and possible future research avenues.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Methods for Evaluating DC Arc Incident Energy in PV Systems: Preprint

Renewable energy systems continue to be one of the fastest growing segments of the energy industry. This paper focuses on the understanding of how photovoltaic (PV) technology behaves under dc arc conditions. Emphasis is placed on the electrical safety aspect of DC arc flash incident energy evaluation. Because of the fast proliferation of PV systems and the lack of formal equivalent calculation guidelines such as IEEE 1584 for AC systems, it has been necessary to rely on different equations and models presented by various researchers over the last few years. This paper discusses the behavior of PV systems under arc conditions and presents the results of available methods to estimate the dc arc flash incident energy. This paper provides a comparative analysis of a proposed arc-flash incident energy calculation method against different laboratory tests including those performed by NREL. Detailed explanations are provided regarding the effect of PV module I-V and P-V curves under arcing conditions. Examples of the application of the proposed calculation method to the test measurements are included.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Data from: Comparing traditional and Bayesian approaches to ecological meta-analysis

Despite the wide application of meta-analysis in ecology, some of the traditional methods used for meta-analysis may not perform well given the type of data characteristic of ecological meta-analyses. We reviewed published meta-analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies (ni) and the number of studies or records (k) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta-analysis methods for estimating a mean effect size and its uncertainty interval. Our literature review showed that ni and k were highly variable, distributions were right-skewed, and were generally small (median ni =5, median k=44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). When k was low (<40), 95% coverage was achieved by a confidence interval based on the t-distribution that uses an adjusted standard error (the Hartung-Knapp-Sidik-Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap or z-distribution confidence intervals had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta-analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults. In general, for a simple random-effects meta-analysis, the performance of the best frequentist and Bayesian methods were similar for the same combinations of factors (k and mean replication), though the Bayesian approaches had higher than nominal (>95%) coverage for the mean effect when k was very low (k<15). Our literature review suggests that many meta-analyses that used z-distribution or bootstrapping confidence intervals may have over-estimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta-analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach

Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Preprocessing approaches, such as missing trace or shot interpolation, are typically employed to enhance seismic data in such cases. Recently, deep learning has been used to address the trace interpolation problem at the expense of large amounts of training data to adequately represent typical seismic events. Nonetheless, most research in this area has focused on trace reconstruction, with little attention having been devoted to shot interpolation. Furthermore, existing methods assume regularly spaced receivers/sources failing in approximating seismic data from real (irregular) surveys. This work presents a novel shot gather interpolation approach which uses a continuous coordinate-based representation of the acquired seismic wavefield parameterized by a neural network. The proposed unsupervised approach, which we call coordinate-based seismic interpolation (CoBSI), enables the prediction of specific seismic characteristics in irregular land surveys without using external data during neural network training. Importantly, experimental results on real and synthetic 3-D data validate the ability of the proposed method to estimate continuous smooth seismic events in the time-space and frequency-wavenumber domains, improving sparsity or low-rank-based interpolation methods.

58 GEOSCIENCES↗
DOE OSTI2022

Savannah River National Laboratory Participation in the International Atomic Energy Agency CPC Interlaboratory Comparison Exercise 2

International Atomic Energy Agency Safeguards Analytical Services (IAEA-SGAS) oversees coordinating the second Controlled Potential Coulometry Quality Control Interlaboratory Comparison (CPC QC ILC) on behalf of the Nuclear Material Laboratory (NML). The purpose of the exercise is to: (1) verify the ability of participating laboratories to measure the Pu concentration in the sample solutions, and (2) estimate the method performance (repeatability and intermediate reproducibility). Sample shipping coordination was performed by IAEA-SGAS. The test sample is a plutonium nitrate solution (estimated molarity of 3-5 M), delivered in two PTFE screw-capped quartz ampoules. Each participating laboratory is to receive two ampoules. Each ampoule holds approximately 8 mL of solution containing approximately 10 mg Pu/g. The gross weights of the ampoules were provided. The test sample is assumed to have an iron content below the level of significance for the CPC. The isotopic composition of the plutonium test sample was provided; however, no analysis date was provided. In addition, IAEA-SGAS provided analytical instructions to participants. On September 20, 2022, SRNL received the samples sent by IAEA-SGAS.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths: Supporting Data

The extent and duration of snow cover is predicted to be altered as the climate changes. Developing high-resolution estimates of snow cover change is crucial for estimating changes in snow cover and the effects of these changes on watershed and ecosystems processes. Remote sensing tools have been a common method for rapidly mapping snow covered area (SCA) across a landscape. The most common remote sensing method for estimating SCA uses satellite-based calculations of the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI is effective at catchment- to regional-scale estimates of SCA, but due to spatial resolution limitations of SWIR measurements, NDSI cannot be used to assess fine-scale SCA. In this work, we develop a new algorithm, called the Blue Snow Threshold (BST) algorithm, that maps high-resolution SCA by calculating a threshold on the blue wavelengths from high-resolution satellite imagery. This data package includes Orthorectified IKONOS-2 imagery (IkonosTestImage.tif) from August 14, 2004 at 1.00 meters Ground Sample Distance for Cook Inlet, Alaska (59.966414 , -152.982975). The Blue Snow Threshold algorithm (BST.py) was then used to produce a snow cover estimate (IkonosTestImage_BST.tif) for this study area. Additional imagery metadata is included in the ImageInfo.txt file. See Thaler et al., 2023 (https://doi.org/10.1016/j.rse.2022.113403) for more information about the BST algorithm.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Laser-Induced Plasma as a Method for the Metallic Materials Hardness Estimation: An Alternative Approach

The application of an alternative laser-induced breakdown spectroscopy (LIBS) method based on transversely excited atmospheric (TEA) CO2 laser was investigated for the first time for estimating the hardness of metallic materials. The human eye-safe (TEA) CO2 laser, operating at 10.6 µm, was used for plasma generation. The LIBS spectra were recorded by employing a cost-effective CCD camera for the time-integrated and spatial resolved measurements. The cast iron and aluminum alloys samples with different hardness have been tested. The ratio between the magnesium ionic and neutral lines in LIBS spectra was applied for estimating the material hardness. In addition, the hardness of all samples included in this study was determined using the conventional method for material hardness determination, i.e. Vickers hardness test. The linear dependence of magnesium lines intensity ratio on the sample’s hardness was obtained for both kinds of materials. Profilometric measurements were used to verify that the newly-introduced method is almost nondestructive for the investigated metals. The results obtained in the present work confirmed the potential of using this original LIBS system not only for the fast elemental analysis but also for the direct estimation of the hardness of metals and alloys. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Raccoon density estimation from camera traps for raccoon rabies management

Abstract Density estimation for unmarked animals is particularly challenging, yet density estimates are often necessary for effective wildlife management. Raccoons ( Procyon lotor ) are the primary terrestrial wildlife reservoir for Lyssavirus rabies within the United States. The raccoon rabies variant (RRVV) is actively managed at landscape scales using oral rabies vaccination (ORV) within the eastern United States. To effectively manage RRVV, it is important to know the density of raccoons to appropriately scale the density of ORV baits distributed on the landscape. We compared methods to estimate raccoon densities from camera‐trap data versus more intensive capture‐mark‐recapture (CMR) estimates across 2 land cover types (upland pine and bottomland hardwood) in the southeastern United States during 2019 and 2020. We evaluated the effect of alternative camera configurations and durations of camera trapping on density estimates and used an N‐mixture model to estimate raccoon densities, including covariates on abundance and detection. We further compared different methods of scaling camera‐based counts, with the maximum number of raccoons seen on any given image within a day best explaining density. Camera‐trap density estimates were moderately correlated with CMR estimates ( r = 0.56). However, densities from camera‐trap data were more reliable when classifying category of density as an index used to inform management (83% correct when compared to CMR estimates), although the densities in our study fell into the 2 lowest density classes only. Using more cameras reduced bias and uncertainty around density estimates; however, if ≤6 camera traps were used at a site, a line transect approach proved less biased than a grid design. Camera trapping should be conducted for at least 3 weeks for more accurate estimates of raccoon population density in our study area (<5% bias). We show that camera‐trap data can be used to assign raccoon densities to management‐relevant density index bins, but more studies are needed to ensure reliability across a greater range of environmental conditions and raccoon densities.

Davis, Amy J.↗
DOE OSTI2023

Modified Eigen-Decomposition-based Interval Analysis (MEDIA) for Power System Dynamic State Estimation

The Bayesian approach has been used for the dynamic state estimation (DSE) of a power system. However, due to the complexity of noise resources, it is difficult to quantify measurement and process noise using probability density functions (PDFs). To overcome the difficulty, the authors of this paper propose a modified eigen-decomposition-based interval analysis (MEDIA) method, which employs bounds instead of PDFs to quantify the noise, and uses the eigen decomposition method to reduce the negative impact of the overestimation problem. Using the simulation data generated from IEEE 16-machine and IEEE 10-machine systems, it is shown that the proposed MEDIA method can estimate the hard boundaries of dynamic states in real time. Furthermore, comparison with the forward-backward propagation method and the extended set-membership filter also shows that the proposed MEDIA method performs better by providing narrower boundaries in the DSE.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A 3D Full Stress Tensor Model for Oklahoma

Abstract The stress tensor is an important property for upper crustal studies such as those that involve pore fluids and earthquake hazards. At tectonic plate scale, plate boundary forces and mantle convection are the primary drivers of the stress field. In many local settings (10–100 s of km and <10 km depth) in tectonic plate interiors, we can simplify by assuming a constant background stress field that is perturbed by local heterogeneity in density and elasticity. Local stress orientation and sometimes magnitude can be estimated from earthquake and borehole‐based observations when available. Modeling of the local stress field often involves interpolating sparse observations. We present a new method to estimate the 3D stress field in the upper crust and demonstrate it for Oklahoma. We created a 3D material model by inverting multiple types of geophysical observations simultaneously. Integrating surface‐wave dispersion, local travel times and gravity observations produces a model of P‐wave velocity, S‐wave velocity, and density. The stress field can then be modeled using finite element simulations. The simulations are performed using our simplified view of the local stress field as the sum of a constant background stress field that is perturbed by local density and elasticity heterogeneity and gravitational body forces. An orientation of N82°E, for the maximum compressive tectonic force, best agrees with previously observed stress orientations and faulting types in Oklahoma. The gravitational contribution of the horizontal stress field has a magnitude comparable to the tectonic contribution for the upper 5 km of the subsurface.

58 GEOSCIENCES↗
DOE OSTI2021

A Scalable Meter Placement Method for Distribution System State Estimation

This paper studies the optimal meter placement problem for distribution system state estimation given limited measurement resources. We formulate the problem as a mixed integer semi-definite programming that minimizes the worst case estimation errors over a set of operating points. To solve the problem, we first relax the problem as a convex optimization problem. Motivated by the lack of scalability of existing solvers, we next leverage the special structure of the cost function and propose an algorithm based on barrier method that solves the problem with significantly better numerical performance. The proposed method has been validated on the IEEE 13-bus, IEEE 123-bus, and IEEE 8,500-bus feeders.

barrier method↗