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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Burst wait time simulation of CALIBAN reactor at delayed super-critical state

In the past, the super prompt critical wait time probability distribution was measured on CALIBAN fast burst reactor [4]. Afterwards, these experiments were simulated with a very good agreement by solving the non-extinction probability equation [5]. Recently, the burst wait time probability distribution has been measured at CEA-Valduc on CALIBAN at different delayed super-critical states [6]. However, in the delayed super-critical case the non-extinction probability does not give access to the wait time distribution. In this case it is necessary to compute the time dependent evolution of the full neutron count number probability distribution. In this paper we present the point model deterministic method used to calculate the probability distribution of the wait time before a prescribed count level taking into account prompt neutrons and delayed neutron precursors. This method is based on the solution of the time dependent adjoint Kolmogorov master equations for the number of detections using the generating function methodology [8,9,10] and inverse discrete Fourier transforms. The obtained results are then compared to the measurements and Monte-Carlo calculations based on the algorithm presented in [7]. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Reply to “Structural and magnetic behavior of the cubic oxyfluoride SrFeO{sub 2}F studied by neutron diffraction”

In this article we comment on the results published by Thompson et al. (, J. Solid State Chem. 219 (2014) 173–178) on the crystal structure of SrFeO{sub 2}F, who claim the compound to crystallize in the cubic space group Pm-3m. We give a more detailed explanation of the determination of our previously reported structural model with Imma symmetry (Clemens et al., J. Solid State Chem. 206 (2013) 158–169), with addition of variable temperature XRD measurements with high counting time to provide unambiguous evidence for the Imma model being correct for our sample. - Graphical abstract: The crystal structure of SrFeO{sub 2}F is discussed with regards to previous reports. - Highlights: • SrFeO{sub 2}F was synthesized by polymer based fluorination of SrFeO{sub 3}. • Evaluation of the diffraction data shows a pseudocubic cell metric. • Superstructure reflections at low d-spacings indicate deviation from cubic symmetry. • The phase transition temperature from orthorhombic to cubic was determined using variable temperature X-ray diffraction. • Results published by Thompson et al. are critically discussed with respect to those observations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Investigating the impact of environmental factors on West Nile virus human case prediction in Ontario, Canada

West Nile virus is the most common mosquito borne disease in North America and the leading cause of viral encephalitis. West Nile virus is primarily transmitted between birds and mosquitoes while humans are incidental, dead-end hosts. Climate change may increase the risk of human infections as climatic variables have been shown to affect the mosquito life cycle, biting rate, incubation period of the disease in mosquitoes, and bird migration patterns. We develop a zero-inflated Poisson model to investigate how human West Nile virus case counts vary with respect to mosquito abundance and infection rates, bird abundance, and other environmental covariates. We use a Bayesian paradigm to fit our model to data from 2010–2019 in Ontario, Canada. Our results show mosquito infection rate, temperature, precipitation, and crow abundance are positively correlated with human cases while NDVI and robin abundance are negatively correlated with human cases. We find the inclusion of spatial random effects allows for more accurate predictions, particularly in years where cases are higher. Our model is able to accurately predict the magnitude and timing of yearly West Nile virus outbreaks and could be a valuable tool for public health officials to implement prevention strategies to mitigate these outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A Machine-Learning Decision-Support Tool for Travel-Demand Modeling

Utility maximization(UM) models are the lifeblood of virtually all travel demand models (TDM) in practice. Be it the traditional travel demand models or more advanced activity-based models, utility maximization models are used extensively to model and predict myriad travel choices such as location choice, mode choice, route choice etc. More recently machine learning (ML) models are being applied in a variety of contexts to predict choice patterns (product suggestions on Amazon, restaurant suggestions on Yelp etc.,). In the TDM arena, there has been interest in incorporating ML models where they can enhance prediction accuracy. Though there have been sporadic efforts at comparing specific utility maximization models to machine learning models, there is a need for a standard comparison tool which can evaluate ML models against UM models for a given choice context. Addressing this need, we present a tool for applying an array of models including logit, nested logit, neural network, Naive Bayes and decision tree classifiers. The tool is specifically tailored to aid in the deciding the best model for a given choice context and can be used to choose an appropriate model family or to construct a model ensemble to improve upon current modeling standards in travel demand modeling. We test our proposed system on household vehicle count and work schedule targets from the 2017 National Household Travel Survey. Results demonstrate that for some variables, logit are not the most effective models, and the proposed system can aid in selecting a better model.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2026

Remote Sensing-Driven Hydrodynamic Modeling in Data-Scarce Regions: Integrating ICESat-2, Sentinel-2, SWOT and Re-analysis Models for Coastal Monitoring

Hydrodynamic models in coastal and estuarine systems are typically constrained by sparse bathymetry, boundary, and validation data, especially in regions where field campaigns are costly or impractical. Here we develop and test a fully satellite-driven framework for hydrodynamic modeling in South Africa’s Langebaan Lagoon without using any local in situ measurements. Bathymetry is derived by training multispectral Sentinel-2 reflectance against ICESat-2 ATL24 photon-derived depths using an XGBoost model optimized with Bayesian search. The final satellite-derived bathymetry reproduces independent ATL24 points with RMSE = 0.45 m and R 2 = 0.97. This bathymetry was used in a depth-averaged Delft3D Flexible Mesh model driven at the open boundary by TPXO tidal harmonics and by ERA5 winds. We validate modeled water surface elevation against 16 SWOT low-rate (250 m, unsmoothed) passes in 2023. SWOT–model comparisons yield an overall RMSE of 0.11 m and R 2 = 0.61, with typical point differences <0.10 m (∼7% of the 1.5 m tidal range), and showed consistent spatial gradients in water level from the offshore boundary, through Saldanha Bay, and into the lagoon. At the offshore boundary, TPXO and SWOT sea surface heights agree closely (R 2 = 0.86). A simple phase adjustment of ∼26,min between TPXO and SWOT lowers the RMSE from 0.18,m to 0.11,m, showing that phase offset accounts for some of the discrepancy, with additional errors likely linked to non-tidal signals. Our results demonstrate that combining passive optical, photon-counting LiDAR, radar interferometry, and global tidal/atmospheric models enables robust, transferrable hydrodynamic modeling in data-scarce coastal systems, offering a cost-effective pathway for monitoring.

ICESat-2↗
NASA NTRS2009

Group Capability Model

The Group Capability Model (GCM) is a software tool that allows an organization, from first line management to senior executive, to monitor and track the health (capability) of various groups in performing their contractual obligations. GCM calculates a Group Capability Index (GCI) by comparing actual head counts, certifications, and/or skills within a group. The model can also be used to simulate the effects of employee usage, training, and attrition on the GCI. A universal tool and common method was required due to the high risk of losing skills necessary to complete the Space Shuttle Program and meet the needs of the Constellation Program. During this transition from one space vehicle to another, the uncertainty among the critical skilled workforce is high and attrition has the potential to be unmanageable. GCM allows managers to establish requirements for their group in the form of head counts, certification requirements, or skills requirements. GCM then calculates a Group Capability Index (GCI), where a score of 1 indicates that the group is at the appropriate level; anything less than 1 indicates a potential for improvement. This shows the health of a group, both currently and over time. GCM accepts as input head count, certification needs, critical needs, competency needs, and competency critical needs. In addition, team members are categorized by years of experience, percentage of contribution, ex-members and their skills, availability, function, and in-work requirements. Outputs are several reports, including actual vs. required head count, actual vs. required certificates, CGI change over time (by month), and more. The program stores historical data for summary and historical reporting, which is done via an Excel spreadsheet that is color-coded to show health statistics at a glance. GCM has provided the Shuttle Ground Processing team with a quantifiable, repeatable approach to assessing and managing the skills in their organization. They now have a common frame of reference across NASA/contractor lines to communicate and mitigate any critical skills concerns.

Olejarski, Michael↗
DOE OSTI2022

The PDF perspective on the tracer-matter connection: Lagrangian bias and non-Poissonian shot noise

ABSTRACT We study the connection of matter density and its tracers from the probability density function (PDF) perspective. One aspect of this connection is the conditional expectation value 〈δtracer|δm〉 when averaging both tracer and matter density over some scale. We present a new way to incorporate a Lagrangian bias expansion of this expectation value into standard frameworks for modelling the PDF of density fluctuations and counts-in-cells statistics. Using N-body simulations and mock galaxy catalogues we confirm the accuracy of this expansion and compare it to the more commonly used Eulerian parametrization. For haloes hosting typical luminous red galaxies, the Lagrangian model provides a significantly better description of 〈δtracer|δm〉 at second order in perturbations. A second aspect of the matter-tracer connection is shot-noise, i.e. the scatter of tracer density around 〈δtracer|δm〉. It is well known that this noise can be significantly non-Poissonian and we validate the performance of a more general, two-parameter shot-noise model for different tracers and simulations. Both parts of our analysis are meant to pave the way for forthcoming applications to survey data.

Friedrich, Oliver↗
DOE OSTI2026

EAGLE-I County Customer Dataset Fall 2025

This dataset provides a combination of modeled and collected county-level electric customer counts derived from 2023 EIA-861 utility customer data, 2021 HIFLD electric retail service territory boundaries, 2021 LandScan population estimates, and 2025 EAGLE-I customer outages. The dataset details county FIPS code, number of customers, and customer type (modeled, collected, mixed). Outage data in included for all 50 U.S. states, Puerto Rico, and the District of Columbia (excluding other U.S. territories).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams

Situational awareness data is a prototypical example of structured heterogeneous streaming data with asynchronous timing that can quickly scale beyond the cognitive power of a human analyst. We present deep learning methods that can train in real time from unlabeled streams and provide probabilistic anomaly scores. We compare our novel deep and recurrent neural network models to three baseline systems using threat detection recall on the CERT Insider Threat Dataset v6.2. In our experiments, our deep neural network and our recurrent neural network systems outperformed all baselines, and we found that using aggregated activity count features yielded the best performance. Our best model produces on average an anomaly score of 95.53 percentile for the day, suggesting it can facilitate a great reduction in analyst workload. Our models are also interpretable, in that they break down the anomaly score into individual features of user behavior, which could further aid analysts in reviewing potential cases of insider threat.

Tuor, Aaron R.↗
DOE OSTI2023

Diffuse sources, clustering, and the excess anisotropy of the radio synchrotron background

ABSTRACT We present the largest low frequency (120 MHz) arcminute resolution image of the radio synchrotron background (RSB) to date, and its corresponding angular power spectrum of anisotropies (APS) with angular scales ranging from 3° to 0.3 arcmin. We show that the RSB around the north celestial pole has a significant excess anisotropy power at all scales over a model of unclustered point sources based on source counts of known source classes. This anisotropy excess, which does not seem attributable to the diffuse Galactic emission, could be linked to the surface brightness excess of the RSB. To better understand the information contained within the measured APS, we model the RSB varying the brightness distribution, size, and angular clustering of potential sources. We show that the observed APS could be produced by a population of faint clustered point sources only if the clustering is extreme and the size of the Gaussian clusters is ≲1 arcmin. We also show that the observed APS could be produced by a population of faint diffuse sources with sizes ≲1 arcmin, and this is supported by features present in our image. Both of these cases would also cause an associated surface brightness excess. These classes of sources are in a parameter space not well probed by even the deepest radio surveys to date.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1998

Fragmented Land Cover Types and Estimation of Area with Course Spatial Resolution Imagery

Imagery of coarse resolution, such weather satellite imagery with 1 sq km pixels, is increasingly used to monitor dynamic and fragmented types of land surface types, such as scars from recent fires and ponds in wetlands. Accurate estimates of these land cover types at regional to global scales are required to assess the roles of fires and wetlands in global warming, yet difficult to compute when much of the area is accounted for by fragments about the same size as the pixels. In previous research, we found that size distribution of the fragments in several example scenes fit simple two-parameter models and related effects of coarse resolution to errors in area estimates based on pixel counts. We report on progress to develop accurate area estimates based on modelling the size distribution of the fragments, including analysis of size distributions on an expanded set of maps developed from digital imagery and a test of a procedure to correct for effects of coarse spatial resolution.

Hlavka, Chris↗
DOE OSTI2016

Development of a Low-Level Ar-37 Calibration Standard

Argon-37 is an important environmental signature of an underground nuclear explosion. Producing and quantifying low-level 37Ar standards is an important step in the development of sensitive field measurement instruments for use during an On-Site Inspection, a key provision of the Comprehensive Nuclear-Test-Ban Treaty. This paper describes progress at Pacific Northwest National Laboratory (PNNL) in the development of a process to generate and quantify low-level 37Ar standard material, which can then be used to calibrate sensitive field systems at activities consistent with soil background levels. The 37Ar used for our work was generated using a laboratory-scale, high-energy neutron source to irradiate powdered samples of calcium carbonate. Small aliquots of 37Ar were then extracted from the head space of the irradiated samples. The specific activity of the head space samples, mixed with P10 (90% stable argon:10% methane by mole fraction) count gas, is then derived using the accepted Length-Compensated Internal-Source Proportional Counting method. Due to the low activity of the samples, a set of three Ultra-Low Background Proportional-Counters designed and fabricated at PNNL from radio-pure electroformed copper was used to make the measurements in PNNL’s shallow underground counting laboratory. Very low background levels (<10 counts/day) have been observed in the spectral region near the 37Ar emission feature at 2.8 keV. Two separate samples from the same irradiation were measured. The first sample was counted for 12 days beginning 28 days after irradiation, the second sample was counted for 24 days beginning 70 days after irradiation (the half-life of 37Ar is 35.0 days). Both sets of measurements were analyzed and yielded very similar results for the starting activity (~0.1 Bq) and activity concentration (0.15 mBq/ccSTP argon) after P10 count gas was added. A detailed uncertainty model was developed based on the ISO Guide to the Expression of Uncertainty in Measurement. This paper presents a discussion of the measurement analysis, along with assumptions and uncertainty estimates.

Absolute radiation measurement↗
DOE OSTI2014

Sci—Thur PM: Imaging — 05: Calibration of a SPECT/CT camera for quantitative SPECT with {sup 99m}Tc

While quantitation is the norm in PET, it is not widely available yet in SPECT. This work's aim was to calibrate a commercially available SPECT/CT system to perform quantitative SPECT. Counting sensitivity, dead-time (DT) constant and partial volume effect (PVE) of the system were assessed. A dual-head Siemens SymbiaT6 SPECT/CT camera equipped with low energy high-resolution collimators was studied. {sup 99m}Tc was the radioisotope of interest because of its wide usage in nuclear medicine. First, point source acquisitions were performed (activity: 30–990MBq). Further acquisitions were then performed with a uniform Jaszczak phantom filled with water at high activity (25–5000MBq). PVE was studied using 6 hot spheres (diameters: 9.9–31.2 mm) filled with {sup 99m}Tc (2.8MBq/cc) in the Jaszczak phantom, which was: (1) empty, (2) water-filled and (3) water-filled with low activity (0.1MBq/cc). The data was reconstructed with the Siemens's Flash3D iterative algorithm with 4 subsets and 8 iterations, attenuation-correction (AC) and scatter-correction (SC). DT modelling was based on the total spectrum counting rate. Sensitivity was assessed using AC-SC reconstructed SPECT data. Sensitivity and DT for the sources were 99.51±1.46cps/MBq and 0.60±0.04µs. For the phantom, sensitivity and DT were 109.9±2.3cps/MBq and 0.62±0.13µs. The recovery-coefficient varied from 5% for the 9.9mm, to 80% for the 31.2mm spheres. With our calibration methods, both sensitivity and DT constant of the SPECT camera had little dependence on the object geometry and attenuation. For small objects of known size, recovery-coefficient can be applied to correct PVE. Clinical quantitative SPECT appears to be possible and has many potential applications.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS2013

Integrating Wind Profiling Radars and Radiosonde Observations with Model Point Data to Develop a Decision Support Tool to Assess Upper-Level Winds for Space Launch

On the day-of-launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds for their launch customers to include NASA's Launch Services Program and NASA's Ground Systems Development and Operations Program. They currently do not have the capability to display and overlay profiles of upper-level observations and numerical weather prediction model forecasts. The LWOs requested the Applied Meteorology Unit (AMU) develop a tool in the form of a graphical user interface (GUI) that will allow them to plot upper-level wind speed and direction observations from the Kennedy Space Center (KSC) 50 MHz tropospheric wind profiling radar, KSC Shuttle Landing Facility 915 MHz boundary layer wind profiling radar and Cape Canaveral Air Force Station (CCAFS) Automated Meteorological Processing System (AMPS) radiosondes, and then overlay forecast wind profiles from the model point data including the North American Mesoscale (NAM) model, Rapid Refresh (RAP) model and Global Forecast System (GFS) model to assess the performance of these models. The AMU developed an Excel-based tool that provides an objective method for the LWOs to compare the model-forecast upper-level winds to the KSC wind profiling radars and CCAFS AMPS observations to assess the model potential to accurately forecast changes in the upperlevel profile through the launch count. The AMU wrote Excel Visual Basic for Applications (VBA) scripts to automatically retrieve model point data for CCAFS (XMR) from the Iowa State University Archive Data Server (http://mtarchive.qeol.iastate.edu) and the 50 MHz, 915 MHz and AMPS observations from the NASA/KSC Spaceport Weather Data Archive web site (http://trmm.ksc.nasa.gov). The AMU then developed code in Excel VBA to automatically ingest and format the observations and model point data in Excel to ready the data for generating Excel charts for the LWO's. The resulting charts allow the LWOs to independently initialize the three models 0-hour forecasts against the observations to determine which is the best performing model and then overlay the model forecasts on time-matched observations during the launch countdown to further assess the model performance and forecasts. This paper will demonstrate integration of observed and predicted atmospheric conditions into a decision support tool and demonstrate how the GUI is implemented in operations.

Bauman, William H., III↗
DOE OSTI2013

X-ray cluster constraints on non-Gaussianity

We report constraints on primordial non-Gaussianity from the abundance of X-ray detected clusters. Our analytic prescription for adding non-Gaussianity to the cluster mass function takes into account moments beyond the skewness, and we demonstrate that those moments should not be ignored in most analyses of cluster data. We constrain the amplitude of the skewness for two scenarios that have different overall levels of non-Gaussianity, characterized by how amplitudes of higher cumulants scale with the skewness. We find that current data can constrain these one-parameter non-Gaussian models at a useful level, but are not sensitive to adding further details of the corresponding inflation scenarios. Combining cluster data with Cosmic Microwave Background constraints on the cosmology and power spectrum amplitude, we find the dimensionless skewness to be 10{sup 3}M{sub 3} = −1{sub −28}{sup +24} for one of our scaling scenarios, and 10{sup 3}M{sub 3} = −4±7 for the other. These are the first constraints on non-Gaussianity from Large Scale Structure that can be usefully applied to any model of primordial non-Gaussianity. The former constraint, when applied to the standard local ansatz (where the n-th cumulant scales as M{sub n}∝M{sub 3}{sup n−2}), corresponds to f{sub NL}{sup local} = −3{sub −91}{sup +78}. When applied to a model with a local-shape bispectrum but higher cumulants that scale as M{sub n}∝M{sub 3}{sup n/3} (the second scaling scenario), the amplitude of the local-shape bispectrum is constrained to be f{sub NL}{sup local*} = −14{sub −21}{sup +22}. For this second scaling (which occurs in various well-motivated models of inflation), we also obtain strong constraints on the equilateral and orthogonal shapes of the bispectrum, f{sub NL}{sup equil} = −52{sub −79}{sup +85} and f{sub NL}{sup orth} = 63{sub −104}{sup +97}. This sensitivity implies that cluster counts could be used to distinguish qualitatively different models for the primordial fluctuations that have identical bispectra.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1974

Confrontation of Lemaitre models and the cosmological constant with observations

The history of the cosmological constant and the Lemaitre models is reviewed briefly. Using recent cosmological observations, it is found that the cosmological constant if nonzero must be in absolute value less than 2 times 10 to the negative 56th power per sq cm. The predictions of the Lemaitre models are compared with modern observations. It is shown that Lemaitre models without evolution fail to reproduce the observed radio source counts. The existence of quasars with large redshift (z greater than 2.5) is shown to be strong evidence against the Lemaitre models.

Petrosian, V.↗
DOE OSTI2019

Investigating the practicality of a minimally defined co-axial HPGe detector model using MCNP

An MCNP model of a co-axial HPGe detector, defined by the specification sheet and without parameter optimization, was used to assess practicality for efficiency characterization and complex spectra simulation. Simulated efficiencies, with and without Gaussian energy broadening, were compared against an experimental calibration for a standard counting geometry. Results show that this minimal detector model predicted efficiencies within 6% of experimental values at energies above 100 keV, while including energy broadening offered little improvement. Furthermore, this work shows that extensive detector characterization and optimization of parameters is not absolutely necessary, thereby reducing the experimental burden associated with detector modeling for many applications.

42 ENGINEERING↗
DOE OSTI2015

CLASSIFYING X-RAY BINARIES: A PROBABILISTIC APPROACH

In X-ray binary star systems consisting of a compact object that accretes material from an orbiting secondary star, there is no straightforward means to decide whether the compact object is a black hole or a neutron star. To assist in this process, we develop a Bayesian statistical model that makes use of the fact that X-ray binary systems appear to cluster based on their compact object type when viewed from a three-dimensional coordinate system derived from X-ray spectral data where the first coordinate is the ratio of counts in the mid- to low-energy band (color 1), the second coordinate is the ratio of counts in the high- to low-energy band (color 2), and the third coordinate is the sum of counts in all three bands. We use this model to estimate the probabilities of an X-ray binary system containing a black hole, non-pulsing neutron star, or pulsing neutron star. In particular, we utilize a latent variable model in which the latent variables follow a Gaussian process prior distribution, and hence we are able to induce the spatial correlation which we believe exists between systems of the same type. The utility of this approach is demonstrated by the accurate prediction of system types using Rossi X-ray Timing Explorer All Sky Monitor data, but it is not flawless. In particular, non-pulsing neutron systems containing “bursters” that are close to the boundary demarcating systems containing black holes tend to be classified as black hole systems. As a byproduct of our analyses, we provide the astronomer with the public R code which can be used to predict the compact object type of XRBs given training data.

79 ASTRONOMY AND ASTROPHYSICS↗