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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Reassessing the Origins and Contemporary Relevance of ck Acceptability Parameters: Evolving Perspectives on Similarity

“Sensitivity and Uncertainty Analyses Applied to Criticality Safety Validation,” introduces sensitivity and uncertainty methods to address challenges in defining and extending areas of applicability for criticality safety validation. These areas are traditionally defined by the bounds or limits on key parameters, but establishing valid ranges and managing complex parameter variations remain challenging. NUREG/CR-6655 introduces ck and other integral indices, as well as concepts such as the completeness of benchmark coverage, to better quantify system similarities. The work proposed herein seeks to evaluate these foundational concepts to ensure that the bounds remain effective in guiding the assessment of similarity and applicability in modern applications. The concept of completeness, along with other parameters envisioned within the framework, serves as an example of the foundational ideas that have been established, though their effectiveness in practice may not be fully understood. Advancements in scripting tools, coupled with the speed and efficiency of modern computing and statistical models, now allow for faster and more thorough assessments than previously possible. These advancements also enable the identification of trends within the data, which could provide additional insight into system behavior and further broaden the scope of previously performed benchmarks. By leveraging these capabilities, we will revisit and expand the scope of these foundational methods to determine whether the necessary elements for robust similarity evaluation are already embedded, partially realized, or remain untapped.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Hyper Spectral Anomaly Detection

Anomaly detection is a common machine learning (ML) task with growing importance in the fields of imaging, quality assurance, and multiple security related disciplines. Anomaly detection is more difficult than traditional machine learning methods due to the inherent unlabeled nature of the datasets. Existing anomaly detection architectures commonly face challenges with explainability, retaining information related to the relational structure of the data, and false positive rates. Hyperspectral Imaging Anomaly Detection (HSI) is a statistical model that employs vertex and edge weighted graphs to preserve the data’s relationships on different topographical scales. The model is able to generalize from anomaly detection in 2D images to novel datasets related to cyber-security. Furthermore, the use of multi-spectral and other filtering methods results in fewer false positives and increases the explainability of model predictions. When applying HSI to cyber-security datasets, we are able to successfully detect malicious activity with a relatively high degree of accuracy.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS2020

Development of TID Hardness Assurance Methodologies to Capitalize on Statistical Radiation Environment Models

We develop methods for bounding part-to-part variation in TID data. When used in conjunction with statistical radiation environment models, these methods allow development of RHA that deliver estimated piece part reliability for any desired confidence level, moving beyond risk avoidance methodologies based on radiation design margin. Two methods are developed, and their results compared for realistic data.

Total Ionizing Dose↗
NASA NTRS2020

Development of TID Hardness Assurance Methodologies to Capitalize on Statistical Radiation Environment Models

We develop methods for bounding part-to-part variation in TID data. When used in conjunction with statistical radiation environment models, these methods allow development of RHA that deliver estimated piece part reliability for any desired confidence level, moving beyond risk avoidance methodologies based on radiation design margin. Two methods are developed, and their results compared for realistic data.

Total ionizing dose↗
NASA NTRS1985

Composite statistical method for modeling wind gusts for aircraft simulation

This paper discusses the application of three statistical methods in combination to model wind gusts for use in aircraft flight simulation. The approach combines principal components analysis, time series analysis and probability distribution model to analyze and simulate wind gust components. Comparisons are given between wind gust components generated by the model and components measured onboard an aircraft.

Schiess, J. R.↗
DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R 2 ) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R 2 values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1999

Multi-Parent Clustering Algorithms from Stochastic Grammar Data Models

We introduce a statistical data model and an associated optimization-based clustering algorithm which allows data vectors to belong to zero, one or several "parent" clusters. For each data vector the algorithm makes a discrete decision among these alternatives. Thus, a recursive version of this algorithm would place data clusters in a Directed Acyclic Graph rather than a tree. We test the algorithm with synthetic data generated according to the statistical data model. We also illustrate the algorithm using real data from large-scale gene expression assays.

Mjoisness, Eric↗
NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term. The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗
NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission TRMM Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗
NASA NTRS1990

A method for modelling peak signal statistics on a mobile satellite transponder

A simulation method is proposed. The simulation was developed to model the peak duration and energy content of signal peaks in a mobile communication satellite operating in a Frequency Division Multiple Access (FDMA) mode and presents an estimate of those power peaks for a system where the channels are modeled as band limited Gaussian noise, which is taken as a reasonable representation for Amplitude Commanded Single Sideband (ACSSB), Minimum Shift Keying (MSK), or Phase Shift Keying (PSK) modulated signals. The simulation results show that, under this hypothesis, the level of the signal power peaks for 10 percent, 1 percent, and 0.1 percent of the time are well described by a Rayleigh law and that their duration is extremely short and inversely proportional to the total FDM system bandwidth.

Bilodeau, Andre↗
NASA NTRS2010

Statistical Analysis of Model Data for Operational Space Launch Weather Support at Kennedy Space Center and Cape Canaveral Air Force Station

The 12-km resolution North American Mesoscale (NAM) model (MesoNAM) is used by the 45th Weather Squadron (45 WS) Launch Weather Officers at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to support space launch weather operations. The 45 WS tasked the Applied Meteorology Unit to conduct an objective statistics-based analysis of MesoNAM output compared to wind tower mesonet observations and then develop a an operational tool to display the results. The National Centers for Environmental Prediction began running the current version of the MesoNAM in mid-August 2006. The period of record for the dataset was 1 September 2006 - 31 January 2010. The AMU evaluated MesoNAM hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The MesoNAM forecast winds, temperature and dew point were compared to the observed values of these parameters from the sensors in the KSC/CCAFS wind tower network. The data sets were stratified by model initialization time, month and onshore/offshore flow for each wind tower. Statistics computed included bias (mean difference), standard deviation of the bias, root mean square error (RMSE) and a hypothesis test for bias = O. Twelve wind towers located in close proximity to key launch complexes were used for the statistical analysis with the sensors on the towers positioned at varying heights to include 6 ft, 30 ft, 54 ft, 60 ft, 90 ft, 162 ft, 204 ft and 230 ft depending on the launch vehicle and associated weather launch commit criteria being evaluated. These twelve wind towers support activities for the Space Shuttle (launch and landing), Delta IV, Atlas V and Falcon 9 launch vehicles. For all twelve towers, the results indicate a diurnal signal in the bias of temperature (T) and weaker but discernable diurnal signal in the bias of dewpoint temperature (T(sub d)) in the MesoNAM forecasts. Also, the standard deviation of the bias and RMSE of T, T(sub d), wind speed and wind direction indicated the model error increased with the forecast period all four parameters. The hypothesis testing uses statistics to determine the probability that a given hypothesis is true. The goal of using the hypothesis test was to determine if the model bias of any of the parameters assessed throughout the model forecast period was statistically zero. For th is dataset, if this test produced a value >= -1 .96 or <= 1.96 for a data point, then the bias at that point was effectively zero and the model forecast for that point was considered to have no error. A graphical user interface (GUI) was developed so the 45 WS would have an operational tool at their disposal that would be easy to navigate among the multiple stratifications of information to include tower locations, month, model initialization times, sensor heights and onshore/offshore flow. The AMU developed the GUI using HyperText Markup Language (HTML) so the tool could be used in most popular web browsers with computers running different operating systems such as Microsoft Windows and Linux.

Bauman, William H., III↗
NASA NTRS1994

Solar granulation and statistical crystallography: A modeling approach using size-shape relations

The irregular polygonal pattern of solar granulation is analyzed for size-shape relations using statistical crystallography. In contrast to previous work which has assumed perfectly hexagonal patterns for granulation, more realistic accounting of cell (granule) shapes reveals a broader basis for quantitative analysis. Several features emerge as noteworthy: (1) a linear correlation between number of cell-sides and neighboring shapes (called Aboav-Weaire's law); (2) a linear correlation between both average cell area and perimeter and the number of cell-sides (called Lewis's law and a perimeter law, respectively) and (3) a linear correlation between cell area and squared perimeter (called convolution index). This statistical picture of granulation is consistent with a finding of no correlation in cell shapes beyond nearest neighbors. A comparative calculation between existing model predictions taken from luminosity data and the present analysis shows substantial agreements for cell-size distributions. A model for understanding grain lifetimes is proposed which links convective times to cell shape using crystallographic results.

Noever, D. A.↗
NASA NTRS1990

A Unified Statistical Rain-Attenuation Model for Communication Link Fade Predictions and Optimal Stochastic Fade Control Design Using a Location-Dependent Rain-Statistic Database

A static and dynamic rain-attenuation model is presented which describes the statistics of attenuation on an arbitrarily specified satellite link for any location for which there are long-term rainfall statistics. The model may be used in the design of the optimal stochastic control algorithms to mitigate the effects of attenuation and maintain link reliability. A rain-statistics data base is compiled, which makes it possible to apply the model to any location in the continental U.S. with a resolution of 0-5 degrees in latitude and longitude. The model predictions are compared with experimental observations, showing good agreement.

Manning, Robert M.↗
DOE OSTI2024

Strong Coupling of Hydrodynamics and Reactions in Nuclear Statistical Equilibrium for Modeling Convection in Massive Stars

We build on the simplified spectral deferred corrections (SDC) coupling of hydrodynamics and reactions to handle the case of nuclear statistical equilibrium (NSE) and electron/positron captures/decays in the cores of massive stars. Our approach blends a traditional reaction network on the grid with a tabulated NSE state from a very large, ${\mathcal O }(100)$ nuclei network. We demonstrate how to achieve second-order accuracy in the simplified-SDC framework when coupling NSE to hydrodynamics, with the ability to evolve the star on the hydrodynamics time step. We discuss the application of this method to convection in massive stars leading up to core collapse. We also show how to initialize the initial convective state from a 1D model in a self-consistent fashion. All of these developments are done in the publicly available Castro simulation code and the entire simulation methodology is fully GPU-accelerated.

Explosive nucleosynthesis↗
NASA NTRS2010

Low and High Frequency Models of Response Statistics of a Cylindrical Orthogrid Vehicle Panel to Acoustic Excitation

This presentation further develops the orthogrid vehicle panel work. Employed Hybrid Module capabilities to assess both low/mid frequency and high frequency models in the VA One simulation environment. The response estimates from three modeling approaches are compared to ground test measurements. Detailed Finite Element Model of the Test Article -Expect to capture both the global panel modes and the local pocket mode response, but at a considerable analysis expense (time & resources). A Composite Layered Construction equivalent global stiffness approximation using SEA -Expect to capture response of the global panel modes only. An SEA approximation using the Periodic Subsystem Formulation. A finite element model of a single periodic cell is used to derive the vibroacoustic properties of the entire periodic structure (modal density, radiation efficiency, etc. Expect to capture response at various locations on the panel (on the skin and on the ribs) with less analysis expense

Smith, Andrew↗
NASA NTRS1976

Inadequacy of the statistical many-body model for molecular infrared absorption in the far wings

It is known that the infrared absorption coefficient of CO2 in the far wings is far less than what is derived from the assumption of a Lorentzian absorption profile. Sorgen and Ben-Aryeh (1972) tried to show that by incorporating the statistical many-body model into impact theory, the discrepancy between the Lorentzian absorption profile and the experimental infrared absorption coefficient can be removed. This paper attempts to show that the high frequency or short-time property of the dipole-dipole interaction cannot be adequately explained by the many-body model; in other words, the statistical model as conventionally understood is not applicable to infrared spectral lines in the far wings.

Fowler, B. W.↗
NASA NTRS2002

Comparison of a 2D Photochemical Model to Data Using Statistical Trend Analysis

We have analyzed our 23-year merged ozone data set for variability and trends with a statistical time-series model. To assist in that analysis, we have analyzed the Goddard ED photochemical model for the same time period with the same time-series model. Multiple runs of the photochemical model allow us to separate the effects of various terms on ozone, such as solar cycle and volcanic eruptions. We use this to help us separate those signals from each other in the data. We also used a smoothed version of the photochemical model's prediction of global ozone change in place of a simple linear trend. We find a number of interesting results. This particular photochemical model is more sensitive to chlorine perturbations than the atmosphere appears to be. It is less sensitive to solar cycle. It predicts an effect from the Pinatubo eruption that is nearly symmetric in the two hemispheres, but the data appears to have not responded to Pinatubo in the southern mid-latitudes. These results and their uncertainties will be discussed.

Stolarski, Richard S.↗