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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Measurement of subresolution terrain displacements using SPOT panchromatic imagery

SPOT panchromatic imagery is used to measure subresolution horizontal terrain displacements that are associated with earthquakes, sand dune migration, coastal processes, and glacial motion. The approach is aimed at detecting and measuring surface processes by statistically matching the radiometric patterns in the data that differ spatially in a consistent direction over many pixels. Data obtained from the satellite-derived imagery can facilitate the understanding of natural hazards and the determination of the rates of many environmental processes worldwide.

Crippen, Robert E.↗
NASA NTRS1995

Calculation of design load for the MOD-5A 7.3 mW wind turbine system

Design loads are presented for the General Electric MOD-SA wind turbine. The MOD-SA system consists of a 400 ft. diameter, upwind, two-bladed, teetered rotor connected to a 7.3 mW variable-speed generator. Fatigue loads are specified in the form of histograms for the 30 year life of the machine, while limit (or maximum) loads have been derived from transient dynamic analysis at critical operating conditions. Loads prediction was accomplished using state of the art aeroelastic analyses developed at General Electric. Features of the primary predictive tool - the Transient Rotor Analysis Code (TRAC) are described in the paper. Key to the load predictions are the following wind models: (1) yearly mean wind distribution; (2) mean wind variations during operation; (3) number of start/shutdown cycles; (4) spatially large gusts; and (5) spatially small gusts (local turbulence). The methods used to develop statistical distributions from load calculations represent an extension of procedures used in past wind programs and are believed to be a significant contribution to Wind Turbine Generator analysis. Test/theory correlations are presented to demonstrate code load predictive capability and to support the wind models used in the analysis. In addition MOD-5A loads are compared with those of existing machines. The MOD-5A design was performed by the General Electric Company, Advanced Energy Program Department, under Contract DEN3-153 with NASA Lewis Research Center and sponsored by the Department of Energy.

Mirandy, L.↗
NASA NTRS2001

The X-Ray Spectrometer for Mercury MESSENGER

Mercury is the closest planet to the Sun and because it is so close, it is difficult to study from Earth-based observatories. Its proximity to the Sun has also limited the number of spacecraft to visit this tiny planet to just one, Mariner 10, which flew by Mercury twice in 1974 and once in 1975. Mariner 10 provided a wealth of new information about Mercury, yet much still remains unknown about Mercury's geologic history and the processes that led to its formation. The origin of Mercury's metal-rich composition is just one area of investigation awaiting more and improved data to sort between competing hypotheses. Mercury plays an important role in comparative planetology, and many of the processes that were important during its formation are relevant to the Earth's early history. MESSENGER (Mercury Surface, Space Environment, Geochemistry, and Ranging) is a Discovery mission that has been designed to fly by and orbit Mercury. It will launch in March 2004, flyby Mercury in 2007 and 2008 and enter an elliptical orbit in April 2009. During the one-year orbital phase, a suite of instruments on board the MESSENGER spacecraft will study the exosphere, magnetosphere, surface, and interior of Mercury. One of these instruments will be an X-Ray Spectrometer (XRS) that will measure surface elemental abundances. Remote X-ray spectroscopy has been accomplished before on the Apollo 15 and 16 missions, and more recently on NEAR Shoemaker. The MESSENGER XRS will measure characteristic X-ray emissions induced in the surface of Mercury by the incident solar flux. The Ka lines for the elements Mg, Al, Si, S, Ca, Ti, and Fe will be detected with spatial resolution on the order of 40 km when counting statistics are not a limiting factor. These measurements can be used to obtain quantitative information on elemental composition.

Starr, R. D.↗
NASA NTRS2022

Skillful Seasonal Forecasts of Land Carbon Uptake in Northern Mid- and High Latitudes

Here we present a first look at the Gross Primary Production (GPP) forecast skill levels achievable with a state-of-the-art subseasonal-to-seasonal (S2S) forecast system. Using NASA’s retrospective S2S ensemble forecast in conjunction with a terrestrial biosphere model, and using an independent, remote sensing-based dataset for validation, we demonstrate an ability to accurately forecast spring-summer carbon uptake at multi-month leads. Averaged across mid-and high latitudes of the Northern Hemisphere land, the GPP forecast initialized on January 1 produces statistically significant skill through summer. The skill achieved, however, is spatially variable, with some regions appearing to extract skill from accurate forecasts of snowpack removal and others extracting skill from the initialization of carbon and nitrogen states. Our results reveal some heretofore unexplored facets of climate predictability and provide a look at what might be possible with future S2S forecast systems that are fully integrated with biogeochemical cycles.

Eunjee Lee↗
NASA NTRS2023

Tensor-to-scalar ratio forecasts for extended LiteBIRD frequency configurations

LiteBIRD is a planned JAXA-led cosmic microwave background (CMB) B -mode satellite experiment aiming for launch in the late 2020s, with a primary goal of detecting the imprint of primordial inflationary gravitational waves. Its current baseline focal-plane configuration includes 15 frequency bands between 40 and 402 GHz, fulfilling the mission requirements to detect the amplitude of gravitational waves with the total uncertainty on the tensor-to-scalar ratio, δr , down to δr < 0.001. A key aspect of this performance is accurate astrophysical component separation, and the ability to remove polarized thermal dust emission is particularly important. In this paper we note that the CMB frequency spectrum falls off nearly exponentially above 300 GHz relative to the thermal dust spectral energy distribution, and a relatively minor high frequency extension can therefore result in even lower uncertainties and better model reconstructions. Specifically, we compared the baseline design with five extended configurations, while varying the underlying dust modeling, in each of which the High-Frequency Telescope (HFT) frequency range was shifted logarithmically toward higher frequencies, with an upper cutoff ranging between 400 and 600 GHz. In each case, we measured the tensor-to-scalar ratio r uncertainty and bias using both parametric and minimum-variance component-separation algorithms. When the thermal dust sky model includes a spatially varying spectral index and temperature, we find that the statistical uncertainty on r after foreground cleaning may be reduced by as much as 30–50% by extending the upper limit of the frequency range from 400 to 600 GHz, with most of the improvement already gained at 500 GHz. We also note that a broader frequency range leads to higher residuals when fitting an incorrect dust model, but also it is easier to discriminate between models through higher χ 2 sensitivity. Even in the case in which the fitting procedure does not correspond to the underlying dust model in the sky, and when the highest frequency data cannot be modeled with sufficient fidelity and must be excluded from the analysis, the uncertainty on r increases by only about 5% for a 500 GHz configuration compared to the baseline.

U. Fuskeland↗
NASA NTRS1973

Statistical parameters of reverberating fields, calculated by computer

A method, developed for making measurements in reverberation chambers in the steady state mode, is reported. Spatial distribution patterns of the square of the sound pressure vs type of exciting signal were produced theoretically and checked experimentally in a reverberation chamber. Results of measurements of spatial irregularities of the field with pure tones and with noise bands in the 200 to 2,000 Hz frequency range were used for calculating statistical parameters of the reverbation fields by computer. The calculation program included determination of density probability function, integral distribution pattern, mathematical expectation, normalized dispersion of the mean square of the pressure.

Lebedeva, I. V.↗
DOE OSTI2024

Statistical Downscaling of Climate Models for Solar Resource Assessment

This study presents the development of statistical models to efficiently downscale future projections of solar irradiance for solar energy applications. A climate data set simulated from a Regional Climate Model (RCM) obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) is selected as input to the statistical models to create high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). Our approach builds statistical downscaling models that (1) regrid RCM data (0.22 degree and daily spatiotemporal resolution), (2) correct bias of GHI projections, (3) downscale the future GHI project from daily-scale to hourly-scale, and (4) spatially downscale to generate GHI at 8-km resolution. To calibrate and validate the statistical models, we adapt and use the National Solar Radiation Database (NSRDB). Preliminary results show that the statistical downscaling approach downscales future projections of GHI under two climate scenarios (RCP4.5 and RCP8.5) with a nBIAS of 3%, nMAE of 34% and nRMSE of 46% estimated against NSRDB for the contiguous United State. This presentation will summarize the implemented methodology and validation results as well as future extension of this research.

climate data↗
DOE OSTI2024

Modeling Spatial Asymmetries in Teleconnected Extreme Temperatures

Abstract Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.

Krock, Mitchell L.↗
NASA NTRS1994

Quantitative 3-D imaging topogrammetry for telemedicine applications

The technology to reliably transmit high-resolution visual imagery over short to medium distances in real time has led to the serious considerations of the use of telemedicine, telepresence, and telerobotics in the delivery of health care. These concepts may involve, and evolve toward: consultation from remote expert teaching centers; diagnosis; triage; real-time remote advice to the surgeon; and real-time remote surgical instrument manipulation (telerobotics with virtual reality). Further extrapolation leads to teledesign and telereplication of spare surgical parts through quantitative teleimaging of 3-D surfaces tied to CAD/CAM devices and an artificially intelligent archival data base of 'normal' shapes. The ability to generate 'topogrames' or 3-D surface numerical tables of coordinate values capable of creating computer-generated virtual holographic-like displays, machine part replication, and statistical diagnostic shape assessment is critical to the progression of telemedicine. Any virtual reality simulation will remain in 'video-game' realm until realistic dimensional and spatial relational inputs from real measurements in vivo during surgeries are added to an ever-growing statistical data archive. The challenges of managing and interpreting this 3-D data base, which would include radiographic and surface quantitative data, are considerable. As technology drives toward dynamic and continuous 3-D surface measurements, presenting millions of X, Y, Z data points per second of flexing, stretching, moving human organs, the knowledge base and interpretive capabilities of 'brilliant robots' to work as a surgeon's tireless assistants becomes imaginable. The brilliant robot would 'see' what the surgeon sees--and more, for the robot could quantify its 3-D sensing and would 'see' in a wider spectral range than humans, and could zoom its 'eyes' from the macro world to long-distance microscopy. Unerring robot hands could rapidly perform machine-aided suturing with precision micro-sewing machines, splice neural connections with laser welds, micro-bore through constricted vessels, and computer combine ultrasound, microradiography, and 3-D mini-borescopes to quickly assess and trace vascular problems in situ. The spatial relationships between organs, robotic arms, and end-effector diagnostic, manipulative, and surgical instruments would be constantly monitored by the robot 'brain' using inputs from its multiple 3-D quantitative 'eyes' remote sensing, as well as by contact and proximity force measuring devices. Methods to create accurate and quantitative 3-D topograms at continuous video data rates are described.

Altschuler, Bruce R.↗
NASA NTRS2006

Proceedings of the 2004 High Spatial Resolution Commercial Imagery Workshop

Topics covered include: NASA Applied Sciences Program; USGS Land Remote Sensing: Overview; QuickBird System Status and Product Overview; ORBIMAGE Overview; IKONOS 2004 Calibration and Validation Status; OrbView-3 Spatial Characterization; On-Orbit Modulation Transfer Function (MTF) Measurement of QuickBird; Spatial Resolution Characterization for QuickBird Image Products 2003-2004 Season; Image Quality Evaluation of QuickBird Super Resolution and Revisit of IKONOS: Civil and Commercial Application Project (CCAP); On-Orbit System MTF Measurement; QuickBird Post Launch Geopositional Characterization Update; OrbView-3 Geometric Calibration and Geopositional Accuracy; Geopositional Statistical Methods; QuickBird and OrbView-3 Geopositional Accuracy Assessment; Initial On-Orbit Spatial Resolution Characterization of OrbView-3 Panchromatic Images; Laboratory Measurement of Bidirectional Reflectance of Radiometric Tarps; Stennis Space Center Verification and Validation Capabilities; Joint Agency Commercial Imagery Evaluation (JACIE) Team; Adjacency Effects in High Resolution Imagery; Effect of Pulse Width vs. GSD on MTF Estimation; Camera and Sensor Calibration at the USGS; QuickBird Geometric Verification; Comparison of MODTRAN to Heritage-based Results in Vicarious Calibration at University of Arizona; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Estimating Sub-Pixel Proportions of Sagebrush with a Regression Tree; How Do YOU Use the National Land Cover Dataset?; The National Map Hazards Data Distribution System; Recording a Troubled World; What Does This-Have to Do with This?; When Can a Picture Save a Thousand Homes?; InSAR Studies of Alaska Volcanoes; Earth Observing-1 (EO-1) Data Products; Improving Access to the USGS Aerial Film Collections: High Resolution Scanners; Improving Access to the USGS Aerial Film Collections: Phoenix Digitizing System Product Distribution; System and Product Characterization: Issues Approach; Innovative Approaches to Analysis of Lidar Data for the National Map; Changes in Imperviousness near Military Installations; Geopositional Accuracy Evaluations of QuickBird and OrbView-3: Civil and Commercial Applications Project (CCAP); Geometric Accuracy Assessment: OrbView ORTHO Products; QuickBird Radiometric Calibration Update; OrbView-3 Radiometric Calibration; QuickBird Radiometric Characterization; NASA Radiometric Characterization; Establishing and Verifying the Traceability of Remote-Sensing Measurements to International Standards; QuickBird Applications; Airport Mapping and Perpetual Monitoring Using IKONOS; OrbView-3 Relative Accuracy Results and Impacts on Exploitation and Accuracy Improvement; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Applying High-Resolution Satellite Imagery and Remotely Sensed Data to Local Government Applications: Sioux Falls, South Dakota; Automatic Co-Registration of QuickBird Data for Change Detection Applications; Developing Coastal Surface Roughness Maps Using ASTER and QuickBird Data Sources; Automated, Near-Real Time Cloud and Cloud Shadow Detection in High Resolution VNIR Imagery; Science Applications of High Resolution Imagery at the USGS EROS Data Center; Draft Plan for Characterizing Commercial Data Products in Support of Earth Science Research; Atmospheric Correction Prototype Algorithm for High Spatial Resolution Multispectral Earth Observing Imaging Systems; Determining Regional Arctic Tundra Carbon Exchange: A Bottom-Up Approach; Using IKONOS Imagery to Assess Impervious Surface Area, Riparian Buffers and Stream Health in the Mid-Atlantic Region; Commercial Remote Sensing Space Policy Civil Implementation Update; USGS Commercial Remote Sensing Data Contracts (CRSDC); and Commercial Remote Sensing Space Policy (CRSSP): Civil Near-Term Requirements Collection Update.

Source record↗
NASA NTRS1989

Cloud field classification based on textural features

An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes of features. Preliminary results based on the GLDV textural features alone look promising.

Sengupta, Sailes Kumar↗
NASA NTRS2013

A Stochastic Fractional Dynamics Model of Space-time Variability of Rain

Rainfall varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic feature of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order for the point rain rate, that allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model is thus capable of providing a unified description of the statistics of both radar and rain gauge data. The underlying dynamical equation can be expressed in terms of space-time derivatives of fractional orders that are adjusted together with other model parameters to fit the data. The form of the resulting spectrum gives the model adequate flexibility to capture the subtle interplay between the spatial and temporal scales of variability of rain but strongly constrains the predicted statistical behavior as a function of the averaging length and times scales. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and in Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to the second moment statistics of radar data. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well without any further adjustment.

TRMM↗
NASA NTRS1995

The mosaic structure of plasma bulk flows in the Earth's magnetotail

Moments of plasma distributions observed in the magnetotail vary with different time scales. In this paper we attempt to explain the observed variability on intermediate timescales of approximately 10-20 min that result from the simultaneous energization and spatial structuring of solar wind plasma in the distant magnetotail. These processes stimulate the formation of a system of spatially disjointed. highly accelerated filaments (beamlets) in the tail. We use the results from large-scale kinetic modeling of magnetotail formation from a plasma mantle source to calculate moments of ion distribution functions throughout the tail. Statistical restrictions related to the limited number of particles in our system naturally reduce the spatial resolution of our results, but we show that our model is valid on intermediate spatial scales Delta(x) x Delta(z) equal to approximately 1 R(sub E) x 1000 km. For these spatial scales the resulting pattern, which resembles a mosaic, appears to be quite variable. The complexity of the pattern is related to the spatial interference between beamlets accelerated at various locations within the distant tail which mirror in the strong near-Earth magnetic field. Global motion of the magnetotail results in the displacement of spacecraft with respect to this mosaic pattern and can produce variations in all of the moments (especially the x-component of the bulk velocity) on intermediate timescales. The results obtained enable us to view the magnetotail plasma as consisting of two different populations: a tailward-Earthward system of highly accelerated beamlets interfering with each other, and an energized quasithermal population which gradually builds as the Earth is approached. In the near-Earth tail, these populations merge into a hot quasi-isotropic ion population typical of the near-Earth plasma sheet. The transformation of plasma sheet boundary layer (PSBL) beam energy into central plasma sheet (CPS) quasi-thermal energy occurs in the absence of collisions or noise. This paper also clarifies the relationship between the global scale where an MHD description might be appropriate and the lower intermediate scales where MHD fails and large-scale kinetic theory should be used.

Ashour-Abdalla, M.↗
NASA NTRS1974

Influences of physical training on the heart of dogs

To investigate the effects of physical training on cardiac dimensions and function, eight dogs were exercised for 12 weeks by treadmill running 1 hour/day, 5 days/week. Five dogs were confined in cages as controls for an 8-week period. Heart rates were monitored by telemetry during rest and exercise. Maximum QRS spatial magnitudes were calculated from records of McFee lead electrocardiograms. Left ventricular end-diastolic dimensions were determined radiographically by the bead and clip technique. Training produced statistically significant decreases in heart rate at rest and at a standard work load of 6.1 mph on a level treadmill and statistically significant increases in work load at a standard heart rate of 194 beats/min. Improvements were rapid during the first 4 weeks of training but gradual during the remaining 8 weeks. Training caused small but statistically significant increases in left ventricular end-diastolic wall thickness, estimated left ventricular mass, and maximum QRS spatial magnitude.

Wyatt, H. L.↗
NASA NTRS1984

A statistical evaluation and comparison of VISSR Atmospheric Sounder (VAS) data

In order to account for the temporal and spatial discrepancies between the VAS and rawinsonde soundings, the rawinsonde data were adjusted to a common hour of release where the new observation time corresponded to the satellite scan time. Both the satellite and rawinsonde observations of the basic atmospheric parameters (T Td, and Z) were objectively analyzed to a uniform grid maintaining the same mesoscale structure in each data set. The performance of each retrieval algorithm in producing accurate and representative soundings was evaluated using statistical parameters such as the mean, standard deviation, and root mean square of the difference fields for each parameter and grid level. Horizontal structure was also qualitatively evaluated by examining atmospheric features on constant pressure surfaces. An analysis of the vertical structure of the atmosphere were also performed by looking at colocated and grid mean vertical profiles of both the satellite and rawinsonde data sets. Highlights of these results are presented.

Jedlovec, G. J.↗
NASA NTRS2020

Spatial validation reveals poor predictive performance of large-scale ecological mapping models

Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to overoptimistic assessment of model predictive power. To illustrate this issue, we reproduce the approach of large-scale mapping studies using a massive forest inventory dataset of 11.8 million trees in central Africa to train and validate a random forest model based on multispectral and environmental variables. A standard nonspatial validation method suggests that the model predicts more than half of the forest biomass variation, while spatial validation methods accounting for SAC reveal quasi-null predictive power. This study underscores how a common practice in big data mapping studies shows an apparent high predictive power, even when predictors have poor relationships with the ecological variable of interest, thus possibly leading to erroneous maps and interpretations.

biomass↗
DOE OSTI2026

Beyond Contrast Transfer: Spectral SNR as a Finite-Dose Metric for STEM Phase Retrieval

The contrast transfer function (CTF) is widely used to evaluate phase retrieval methods in scanning transmission electron microscopy (STEM), including center-of-mass imaging, parallax imaging, direct ptychography, and iterative ptychography. However, the CTF reflects only the maximum usable signal, neglecting the effects of finite electron fluence and the Poisson-limited nature of detection. As a result, it can significantly overestimate practical performance, especially in low-dose regimes. Here, we employ the spectral signal-to-noise ratio (SSNR), as a finite-dose statistical framework to evaluate the recoverable signal as a function of spatial frequency. Using numerical reconstructions of white-noise objects, we show that center-of-mass, parallax, and direct ptychography exhibit dose-independent SSNRs, with close-form analytic expressions. In contrast, iterative ptychography exhibits a surprising dose dependence: at low fluence, its SSNR converges to that of direct ptychography; at high fluence, it saturates at a value consistent with the maximum detective quantum efficiency predicted by recent quantum Fisher information bounds. The results highlight the limitations of CTF-based evaluation and motivate SSNR as a more accurate, finite-dose metric for assessing STEM phase retrieval methods.

STEM phase retrieval↗
DOE OSTI2024

Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network

The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving accuracy with negligible additional computational cost. Here, we show that a convolutional neural network can also be used to upsample tally results from Monte Carlo simulations of sodium-cooled fast reactor assemblies, thereby extending the applicability beyond thermal systems. The convolutional neural network model is trained using neutron flux tallies from 300 procedurally generated nuclear reactor assemblies simulated using OpenMC. Validation and test datasets included 16 simulations of procedurally generated assemblies, and a realistic simulation of a European sodium-cooled fast reactor assembly was included in the test dataset. We show the residuals between the high-resolution flux tallies predicted by the neural network and high-resolution Monte Carlo tallies on relative and absolute bases. The network can upsample tallies from simulations of fast reactor assemblies with diverse and heterogeneous materials and geometries by a factor of two in each spatial and energy dimension. The network’s predictions are within the statistical uncertainty of the Monte Carlo tallies in almost all cases. This includes test assemblies for which burnup values and geometric parameters were well outside the ranges of those in assemblies used to train the network.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗