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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Liquid cooled data center design selection

Input data, specifying aspects of a thermal design of a liquid cooled data center, is obtained. The input data includes data indicative of ambient outdoor temperature for a location of the data center; and/or data representing workload power dissipation for the data center. The input data is evaluated to obtain performance of the data center thermal design. The performance includes cooling energy usage; and/or one pertinent temperature associated with the data center. The performance of the data center thermal design is output.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

A Bayesian approach to multivariate measurement system assessment

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

random effects↗
DOE OSTI2017

Neutron and Gamma Correlations using CGM in MCNP 6.2.0

The general-purpose Monte Carlo radiation-transport code MCNP6{sup TM} came from the integration of MCNP5{sup TM} and MCNPX{sup TM}. The most recent release of MCNP, version 6.2.0, is scheduled for release in early 2017, which includes a variety of bug fixes and new features to the code. MCNP traditionally models nuclear reactions using Monte Carlo sampling techniques on measured and modeled cross-sectional data contained within ACE (A Compact Evaluated Nuclear Data File) libraries. In addition to reaction cross-sections, ACE libraries contain information relating to the production of secondary particles, such as secondary neutrons and photons. For example, an incident neutron on a nucleus can produce a nuclear reaction in the form (n, M{sub n}n'), where n is the incident neutron and M{sub n} is the number of secondary neutrons n' produced. Using this notation, different reactions can be described based on their neutron multiplicity, such as neutron capture (M{sub n} = 0), elastic and inelastic scattering (M{sub n} = 1), and reactions with multiple secondary neutrons (M{sub n} > 1). Accounting for secondary γ-rays emitted from the residual nuclei, this reaction can further be generalized as n, Mnn'M to describe a reaction producing M{sub n} neutrons and M -rays. These values M{sub n} and M are referred to as the neutron and multiplicities, respectively. ACE libraries contain the cross-sectional data for each reaction, and their corresponding statistically averaged multiplicities for neutrons and photons, M-bar{sub n} and M-bar{sub γ}. However, there are several limitations to reaction sampling using ACE libraries. First, the data contained within ACE libraries is limited by the accuracy of the model and/or experiment, which out of necessity can greatly simplify the true physics behind a reaction. For example, the value M{sub γ} in a (n, M{sub n}n'M{sub γ}γ) reaction is determined by the statistically averaged multiplicity M-bar{sub γ} instead of a distribution. Consequently, MCNP, unable to produce a continuous distribution, will utilizes a binary sampling around M-bar{sub γ}. While this sampling method provides a statistically average M{sub γ} over a large simulation, it is an inaccurate representation of an individual reaction and its corresponding secondary particles. Secondary neutrons are affected in a similar manner, such that different kinds of secondary neutrons can be sampled from the same interaction (eg., an inelastic neutron with an [n, 2n] neutron). Lastly, the random sampling technique for secondary particles remains completely independent of the sampled reaction, eliminating the ability to correlate secondary particles for a given reaction. To supplement these data libraries, additional codes and models describing certain nuclear reactions are employed. These codes can fill in the information missing from ACE, provide a better model to the true physics distribution, and can provide true correlated secondary particles, which has many useful applications in research and industry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Fission Product Yields from 232Th, 238U, and 235U Using 14 MeV Neutrons

Neutron-induced fission yield studies using deuterium-tritium fusion-produced 14 MeV neutrons have not yet directly measured fission yields from fission products with half-lives on the order of seconds (far from the line of nuclear stability). Fundamental data of this nature are important for improving and validating the current models of the nuclear fission process. Cyclic neutron activation analysis (CNAA) was performed on three actinide targets–thorium-oxide, depleted uranium metal, and highly enriched uranium metal–at the University of Michigan's Neutron Science Laboratory (UM-NSL) using a pneumatic system and Thermo-Scientific D711 accelerator-based fusion neutron generator. This was done to measure the fission yields of short-lived fission products and to examine the differences between the delayed fission product signatures of the three actinides. The measured data were compared against previously published results for 89Kr, -90, and -92 and 138Xe, -139, and -140. The average percent deviation of the measured values from the Evaluated Nuclear Data Files VII.1 (ENDF/B-VII.1) for thorium, depleted-uranium, and highly-enriched uranium were -10.2%, 4.5%, and -12.9%, respectively. In addition to the measurements of the six known fission products, 23 new fission yield measurements from 84As to 146La are presented.

Pierson, Bruce D.↗
DOE OSTI2016

An ensemble constrained variational analysis of atmospheric forcing data and its application to evaluate clouds in CAM5: Ensemble 3DCVA and Its Application

Large-scale atmospheric forcing data can greatly impact the simulations of atmospheric process models including Large Eddy Simulations (LES), Cloud Resolving Models (CRMs) and Single-Column Models (SCMs), and impact the development of physical parameterizations in global climate models. This study describes the development of an ensemble variationally constrained objective analysis of atmospheric large-scale forcing data and its application to evaluate the cloud biases in the Community Atmospheric Model (CAM5). Sensitivities of the variational objective analysis to background data, error covariance matrix and constraint variables are described and used to quantify the uncertainties in the large-scale forcing data. Application of the ensemble forcing in the CAM5 SCM during March 2000 intensive operational period (IOP) at the Southern Great Plains (SGP) of the Atmospheric Radiation Measurement (ARM) program shows systematic biases in the model simulations that cannot be explained by the uncertainty of large-scale forcing data, which points to the deficiencies of physical parameterizations. The SCM is shown to overestimate high clouds and underestimate low clouds. These biases are found to also exist in the global simulation of CAM5 when it is compared with satellite data.

58 GEOSCIENCES↗
DOE OSTI2020

Advanced Laboratory and Field Arrays: Evaluating Sampling Techniques for MHK Biological Monitoring (Task 6)

The overall goal of the task was to identify cost effective biological sampling techniques for MHK environmental monitoring. Protected, demersal, and pelagic fish and selected nektonic invertebrates were surveyed using capture and remote sensing techniques at the PacWave sites. The performance of capture and remote sensing monitoring techniques were evaluated and generic nekton monitoring indices were developed for MHK technologies and sites. In parallel to data collections at the PacWave site, the ability of regression models to characterize, detect, and predict change in acoustic data was evaluated using acoustic data collected in Admiralty Inlet, WA, during a biological monitoring study of the proposed SnoPud tidal turbine project site. Expected outcomes of these efforts included an evaluation of instrumentation and techniques used to monitor biological variability; identification of data streams that can be used to detect and quantify change; and sampling requirements to ensure detection of change in monitored variables.

13 HYDRO ENERGY↗
DOE OSTI2023

pvOps: a Python package for empirical analysis of photovoltaic field data

The purpose of pvOps is to support empirical evaluations of data collected in the field related to the operations and maintenance (O&M) of photovoltaic (PV) power plants. pvOps presently contains modules that address the diversity of field data, including text-based maintenance logs, current-voltage (IV) curves, and timeseries of production information. The package functions leverage machine learning, visualization, and other techniques to enable cleaning, processing, and fusion of these datasets. These capabilities are intended to facilitate easier evaluation of field patterns and extraction of relevant insights to support reliability-related decision-making for PV sites. The open-source code, examples, and instructions for installing the package through PyPI can be accessed through the GitHub repository.

14 SOLAR ENERGY↗
DOE OSTI2022

Evaluation of a scientific data search infrastructure

The ability to search over large scientific datasets has become crucial to next-generation scientific discoveries as data generated from scientific facilities grow dramatically. In previous work, we developed and deployed ScienceSearch, a search infrastructure for scientific data which uses machine learning to automate metadata creation. Our current deployment is deployed atop a container based platform at a HPC center. In this article, we present an evaluation and discuss our experiences with the ScienceSearch infrastructure. Specifically, we present a performance evaluation of ScienceSearch's infrastructure focusing on scalability trends. The obtained results show that ScienceSearch is able to serve up to 130 queries/min with latency under 3 s. We discuss our infrastructure setup and evaluation results to provide our experiences and a perspective on opportunities and challenges of our search infrastructure.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Nuclear Data Sheets for A = 209

The experimental data are evaluated for known nuclides of mass number A = 209 (Au,Hg,Tl,Pb,Bi,Po,At,Rn, Fr,Ra,Ac,Th). Detailed evaluated level properties and related nuclear structure information are presented, with the best values recommended for level energies, half-lives, γ–ray energies and intensities, decay data (energies, intensities and placement of radiations), and other spectroscopic data. This work supersedes the earlier full evaluation of A = 209 by M.J. Martin (1991Ma16)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

AI-Ready Data Pilot Project Report

The proliferation of artificial intelligence in scientific research has created an urgent need to define "AI-ready data" for researchers and, more importantly, provide resources to help them produce AI-ready data. At Pacific Northwest National Laboratory, we conducted a pilot study with three data scientists evaluating three CSV datasets from different scientific domains, followed by semi-structured interviews capturing assessment practices. Our findings reveal that AI-readiness evaluation is intuition-based, with practitioners asking "How fast can I go from raw data to my machine learning pipeline?" Data scientists consistently prioritized workflow efficiency, human interpretability, and quality stewardship signals. From these insights, we developed a practical evaluation framework comprising data requirements, metadata standards, and validation tests that provides actionable criteria for producing and curating AI-ready datasets, addressing the gap between theoretical understanding and practical implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data↗
NASA NTRS1998

The Design and Evaluation of the Lighting Imaging Sensor Data Applications Display (LISDAD)

The design and evaluation of the Lightning Imaging Sensor Data Applications Display (LISDAD). The ultimate goal of the LISDAD system is to quantify the utility of total lightning information in short-term, severe-weather forecasting operations. To this end, scientists from NASA, NWS, and MIT organized an effort to study the relationship of lightning and severe-weather on a storm-by-storm, and even cell-by-cell basis for as many storms as possible near Melbourne, Florida. Melbourne was chosen as it offers a unique combination of high probability of severe weather and proximity to major relevant sensors - specifically: NASA's total lightning mapping system at Kennedy Space Center (the LDAR system at KSC); a NWS/NEXRAD radar (at Melbourne); and a prototype Integrated Terminal Weather System (ITWS, at Orlando), which obtains cloud-to-ground lightning Information from the National Lightning Detection Network (NLDN), and also uses NSSL's Severe Storm Algorithm (NSSL/SSAP) to obtain information about various storm-cell parameters. To assist in realizing this project's goal, an interactive, real-time data processing system (the LISDAD system) has been developed that supports both operational short-term weather forecasting and post facto severe-storm research. Suggestions have been drawn from the operational users (NWS/Melbourne) in the design of the data display and its salient behavior. The initial concept for the users Graphical Situation Display (GSD) was simply to overlay radar data with lightning data, but as the association between rapid upward trends in the total lightning rate and severe weather became evident, the display was significantly redesigned. The focus changed to support the display of time series of storm-parameter data and the automatic recognition of cells that display rapid changes in the total-lightning flash rate. The latter is calculated by grouping discrete LDAR radiation sources into lightning flashes using a time-space association algorithm. Specifically, the GSD presents the user with the Composite Maximum Reflectivity obtained from the NWS/NEXRAD. Superimposed upon this background image are placed small black circles indicating the locations of storm cells identified by the NSSL/SSA. The circles become cyan if lightning is detected within the storm-cell; if the cell has lightning rates indicative of a severe-storm, the circle turns red. This paper will: (1) review the design of LISDAD system; (2) present some examples of its data display; and shown results of the lightning based severe-weather prediction algorithm.

Boldi, B.↗
NASA NTRSDate not supplied

Flight Trajectory Prediction Based on Hybrid-Recurrent Networks

The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostlyrely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 100-day and 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-LSTM design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicatethe effectiveness of LSTM and GRUcells for state-of-the-art data processing (interpolation). Additionally, GRUs may be quickly trained with limited data, allowing for exacting improvements with optimizer selection. Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value, requiring support from tailored measurements for Air Traffic Management.

Nathan Schimpf↗
DOE OSTI2020

Python Library For Reading Endf Files

Evaluated Nuclear Data Files (ENDFs) are the most common way to store nuclear data. However, they are a hard-to-read format that few people or programs can read. OpenMC, an open source project, developed a way to read Evaluated Nuclear Data Files (ENDF) into a useful form for python. This library will remove all unnecessary code from OpenMC to focus solely on reading nuclear data. In addition, it will handle parsing Grouped cross-section data from an ENDF file, which OpenMC does not currently handle.

Gale, MicahD.↗
NASA NTRS1993

Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors

Statistical modeling and evaluation of the performance of obstacle detection systems for Unmanned Ground Vehicles (UGVs) is essential for the design, evaluation, and comparison of sensor systems. In this report, we address this issue for imaging range sensors by dividing the evaluation problem into two levels: quality of the range data itself and quality of the obstacle detection algorithms applied to the range data. We review existing models of the quality of range data from stereo vision and AM-CW LADAR, then use these to derive a new model for the quality of a simple obstacle detection algorithm. This model predicts the probability of detecting obstacles and the probability of false alarms, as a function of the size and distance of the obstacle, the resolution of the sensor, and the level of noise in the range data. We evaluate these models experimentally using range data from stereo image pairs of a gravel road with known obstacles at several distances. The results show that the approach is a promising tool for predicting and evaluating the performance of obstacle detection with imaging range sensors.

Matthies, Larry↗
DOE OSTI2015

Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency

Purpose: Medical diagnosis and molecular or biochemical confirmation typically rely on the knowledge of the clinician. Although this is very difficult in extremely rare diseases, we hypothesized that the recording of patient phenotypes in Human Phenotype Ontology (HPO) terms and computationally ranking putative disease-associated sequence variants improves diagnosis, particularly for patients with atypical clinical profiles. Methods: Using simulated exomes and the National Institutes of Health Undiagnosed Diseases Program (UDP) patient cohort and associated exome sequence, we tested our hypothesis using Exomiser. Exomiser ranks candidate variants based on patient phenotype similarity to (i) known disease–gene phenotypes, (ii) model organism phenotypes of candidate orthologs, and (iii) phenotypes of protein– protein association neighbors. Results: Benchmarking showed Exomiser ranked the causal variant as the top hit in 97% of known disease–gene associations and ranked the correct seeded variant in up to 87% when detectable disease–gene associations were unavailable. Using UDP data, Exomiser ranked the causative variant(s) within the top 10 variants for 11 previously diagnosed variants and achieved a diagnosis for 4 of 23 cases undiagnosed by clinical evaluation. Conclusion: Structured phenotyping of patients and computational analysis are effective adjuncts for diagnosing patients with genetic disorders.

semantic comparison↗
DOE OSTI2013

Comparison of Burnup Credit Uncertainty Quantification Methods

The recent revision of Interim Staff Guidance (ISG-8 revision 3), issued by the US Nuclear Regulatory Commission, on burnup credit for criticality safety analysis of pressurized water reactor spent fuel in transportation and storage casks expands the nuclides that can be credited in the fuel composition to include minor actinides and fission products. The technical basis for development of the uncertainties associated with calculated nuclide concentrations in spent nuclear fuel that are recommended in ISG-8 was established through the use of measured isotopic data acquired from destructive radiochemical assay data for spent fuel samples. These data provide a direct experimental basis for determining uncertainties in the calculated nuclide concentrations. To provide additional insight into code uncertainties resulting from nuclear data and also the level of conservatism associated with using experimental assay benchmark data for uncertainty evaluation, a new uncertainty analysis tool in the SCALE code system that performs stochastic sampling of nuclear data has been applied to representative burnup credit calculations for spent fuel pool and dry cask storage applications methodology. The nuclear data uncertainty analysis capabilities in Sampler can account for uncertainty contributions from cross sections, fission yields, and decay data by using correlated sampling of covariance files developed for all nuclear data used by SCALE. These uncertainties associated with nuclear data evaluations are compared to corresponding values developed in ISG-8 that are based on destructive assay data, as well as to values obtained using an alternate methodology that was developed by the Electric Power Research Institute based on reactor core simulation data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗