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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Casting and Characterization of U-50Zr

Uranium alloyed with 50 wt% zirconium (U-50Zr) is a proposed light water reactor (LWR) nuclear fuel by Lightbridge Corporation (LTBR). The proposed method for making the U-50Zr alloy is to arc-melt master alloys and then remelt in a vacuum induction melter (VIM) to consolidate the material and cast into an intermediate shape. After casting, the material will eventually need to be formed into a desired fuel shape. The work discussed in this report resulted from a joint effort between Ltbr and Pacific Northwest National Laboratory (PNN) to investigate a 500g – 1kg scale casting process to produce the U-50Zr alloy in the desired δ-UZr 2 phase and characterize the impurities and microstructure that result from the casting process. Master alloys were fabricated in an arc melter, then five castings were carried out in a VIM with multiple inert coating and crucible materials to find an appropriate combination. Both ZrO 2 and graphite crucibles were used and different combinations of Y 2 O 3 , CaZrO 3 , and TiC to identify which would contain the molten metal with the least reaction. On each casting, the C, O, N, H impurities were analyzed as well as the phase by x-ray diffraction and microstructure. Of the five castings, two resulted in majority of δ-UZr 2 phase-pure material and had impurity levels within acceptable ranges. The two most successful castings utilized a graphite crucible with a TiC undercoating and a Y 2 O 3 overcoat. The O and N levels were below 1000 ppm and the C content was variable but did not result in measurable carbide formation. The highest success casting resulted in an average of 282 ppm C, 567 ppm O, 217 ppm N and 79 ppm H. This casting's crucible and inert coating material was repeated with slightly different casting parameters and resulted in higher C numbers but similar phase identification. The differences between each casting are discussed and recommendations are made for future experiments to better decide on a casting process to go forward with.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States

To increase situational awareness and support evidence-based policymaking, we formulated a mathematical model for coronavirus disease transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a nonexponentially distributed incubation period, asymptomatic persons, and mild and severe forms of symptomatic disease. We used Bayesian inference to calibrate region-specific models for consistency with daily reports of confirmed cases in the 15 most populous metropolitan statistical areas in the United States. We also quantified uncertainty in parameter estimates and forecasts. This online learning approach enables early identification of new trends despite considerable variability in case reporting.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Analyzing Potential Failures and Effects in a Pilot-Scale Biomass Preprocessing Facility for Improved Reliability

This study demonstrates a failure identification methodology applied to a preprocessing facility generating conversion-ready feedstocks from biomass meeting conversion process critical quality attribute (CQA) specifications. Failure Modes and Effects Analysis (FMEA) was used as an industrially relevant risk analysis approach to evaluate a logging residue preprocessing system to prepare feedstock for pyrolysis conversion. Risk evaluations considered both system-level and operation unit-level assessments considering process efficiency, product quality, cost, sustainability, and safety. Key outputs included estimations of semi-quantitative risk scores for each failure, identification of the failure impacts, identification of failure causes associated with material attributes and process parameters, ranking success rates of failure detection methods, and speculation of potential mitigation strategies for decreasing failure risk scores. Results showed that deviations from moisture specifications had cascading consequences for other CQAs along with process safety implications. Failures linked to fixed carbon specifications carried the highest risk scores for product quality and process efficiency impacts. As increased throughput can be inversely related to meeting product quality specifications; achieving throughput and other material-based CQAs simultaneously will likely require system optimization or prioritization based on system economics. Ultimately, this work successfully demonstrates FMEA as a risk analysis approach for other bioenergy process systems.

09 BIOMASS FUELS↗
NASA NTRS1972

User oriented multispectral data processing at the University of Michigan

The feasibility of solving practical multispectral data reduction problems by available processing techniques and the identification of areas for more technical development requirements are considered. Parameter mapping and ratio mapping techniques prove their usefulness in a corn blight watch experiment. A hybrid computer processing system is advocated for complete realization of aircraft and spacecraft sensor potentials.

Thomson, F. J.↗
NASA NTRS1981

Total-dose radiation effects data for semiconductor devices, volume 1

Steady-state, total-dose radiation test data are provided in graphic format, for use by electronic designers and other personnel using semiconductor devices in a radiation environment. Data are presented by JPL for various NASA space programs on diodes, bipolar transistors, field effect transistors, silicon-controlled rectifiers, and optical devices. A vendor identification code list is included along with semiconductor device electrical parameter symbols and abbreviations.

Price, W. E.↗
NASA NTRS1983

Current status of system identification methodology

Large space structures, system identification, model formulation, experimental design, model order and structure determination, parameter estimation, reduced order modeling, and closed loops are considered.

Larimore, W. E.↗
NASA NTRS2003

Comparing Ultraviolet Spectra Against Calculations: First Results

The five-year goal of this effort is to calculate high fidelity mid-UV spectra for individual stars and stellar systems for a wide range of ages, abundances, and abundance ratios. In this first year, the emphasis was placed on revising the list of atomic line parameters used to calculate mid-UV spectra. First, new identifications of atomic lines and measurements of their transition probabilities were obtained for lines of the first and second ionization stages of iron-peak elements. Second, observed mid-UV and optical spectra for standard stars were re-analyzed and compared to new calculations, to refine the determination of transition probabilities and to estimate the identity of lines still missing from the laboratory lists. As evidenced by the figures, a dramatic improvement has resulted in the reproduction of the spectra of standard stars by the calculations.

Peterson, Ruth C.↗
NASA NTRS2018

Time-Varying Manual Control Identification in a Stall Recovery Task Under Different Simulator Motion Conditions

This paper adds data to help the development of simulator motion cueing guidelines for stall recovery training by identifying time-varying manual control behavior in a stall recovery task under different simulator motion conditions. A study was conducted with seventeen general aviation pilots in the NASA Ames Vertical Motion Simulator. Pilots had to follow a flight director through four stages of a high-altitude stall task. A time-varying identification method was used to quantify how pilot manual control parameters change throughout different stages of the task in both roll and pitch. Four motion configurations were used: no motion, generic hexapod motion, enhanced hexapod motion and full motion. Pilot performance was highest for the enhanced hexapod and full motion configurations in both roll and pitch, and the lowest without motion. In the roll axis, the pilot position gain did not significantly change throughout the stall task, but was the lowest for the condition with no motion. The pilot roll velocity gain was significantly different between motion conditions, the largest difference being found close to the stall point. The enhanced hexapod motion condition had the highest pilot roll velocity gain. In the pitch axis, the pilot position gain was significantly different between time segments but not between motion conditions. The pilot pitch velocity gain was highest for the full motion condition and increased close to the stall point, but did not change significantly for the other motion conditions. Overall, pilot control behavior under enhanced hexapod motion was most similar to that under full aircraft motion. This indicates that motion cueing for stall recovery training on hexapod simulators might be improved by using the principles behind the enhanced hexapod motion configuration.

manual control↗
NASA NTRS2018

NASA GIBS and Worldview: Leveraging Visualizations to Improve Data Discovery

NASA's Global Imagery Browse Services (GIBS) leverages scientific and community best practices and standards to provide a scalable, compliant, and authoritative source for NASA Earth Observing System (EOS) Earth science data visualizations. Since 2013, its goal has been to "transform how end users interact and discover [EOS] data through visualizations." Imagery layers within GIBS allow end users to easily and quickly interact with full resolution, pre-generated visualizations of scientific parameters. This interactive discovery approach relies on visual observation and identification of phenomena that are not as simply identified otherwise.

Global Imagery Browse Services↗
NASA NTRS2001

Unambiguous Spectral Evidence for High- (and Low-) Calcium Pyroxene In Asteroids and Meteorites

Spectroscopy remains a powerful tool for inferring the modal mineralogy and mafic mineral composition of asteroid surfaces. Since similar measurements can be made on meteorite samples, spectroscopy can help link the two populations and add spatial and geologic context to detailed geo knowledge derived from meteorite samples. For example, analysis of the recent NEAR-Shoemaker mission to Eros include detailed study of NIS spectra to assess the affinity of Eros to ordinary chondrites. As discussed in these studies, pyrox (PYX) and olivine (OLV) absorption are readily detectable in the spectra. Furthermore, subtleties in band parameters (position vs. area) suggest the presence of both low- and high-calcium pyroxene (LCP and HCP), as expected from the petrology of ordinary chondrites. However unambiguous identification and detailed compositional inferences for both LCP and HCP (and OLV) are difficult from band parameters analysis. In this study, we examine spectra of S-asteroids and meteorites with the Modified Gaussian Model (MGM), an absorption band model, to explore the role of HCP in these silicate-rich spectra.

Sunshine, J. M.↗
NASA NTRS2001

Unambiguous Spectral Evidence for High- (and Low-) Calcium Pyroxene in Asteroids and Meteorites

Spectroscopy remains a powerful tool for inferring the modal mineralogy and mafic mineral composition of asteroid surfaces. Since similar measurements can be made on meteorite samples, spectroscopy can help link the two populations and add spatial and geologic context to detailed geochemical knowledge derived from meteorite samples. For example, analysis of the recent NEAR-Shoemaker mission to Eros include detailed study of NIS spectra to assess the affinity of Eros to ordinary chondrites. As discussed in these studies, pyroxene (PYX) and olivine (OLV) absorption are readily detectable in the spectra. Furthermore, subtleties in band parameters (position vs. area) suggest the presence of both low- and high-calcium pyroxene (LCP and HCP), as expected from the petrology of ordinary chondrites. However unambiguous identification and detailed compositional inferences for both LCP and HCP (and OLV) are difficult from band parameters analysis. In this study, we examine spectra of S-asteroids and meteorites with the Modified Gaussian Model (MGM), an absorption band model, to explore the role of HCP in these silicate-rich spectra.

Sunshine, J. M.↗
NASA NTRSDate not supplied

Application of ML/AI for Identifying Earth Science Datasets in Research Publications

NASA Data Active Archive Centers, or DAACs, ingest, store and distribute data acquired from satellites, ground systems as well as modelling data. These data are organized by the datasets, each presenting collection of files usually associated with the certain mission, instrument, processing level, parameter(s), algorithm and/or model. The number of datasets offered by a single DAAC to the public varies. GES DISC, for example, currently offers for public use approximately ~1,300 datasets. While each publicly offered dataset comes with supporting documentation, it is challenging for novice and even experienced scientists to navigate among the datasets that offer similar parameters to find the datasets for their particular research application. Supplying dataset documentation with the scientific paper citations that refer to that dataset provides means for the dataset users to educate themselves with the application research that dataset is being used in. Collecting citations of the papers that use the datasets for their research yield valuable insights into application areas of those datasets, information about usage of the dataset groups for specific applications and those application topics. It also gives insights into the “deep metrics” of the dataset usage, as opposed to the common metrics of the dataset usage such as number of users who downloaded the dataset files and volumes of downloaded data. Association of a certain scientific paper with the dataset(s) presents a challenge because most of the paper authors do not properly cite the datasets, datasets usually have cryptic names and Digital Object Identifiers (DOIs) that are used for dataset identification were assigned to the datasets only few years ago. Simple Google or online library search do not provide even meaningful fraction of the results when performed by the dataset name or DOI, however they provide too many results when the search is done by more broader terms such as mission and instrument names. Attempts to create an AI system capable to identify dataset in the scientific papers have already been made using neural networks classifiers on the basis of the dataset mission, instrument and variable name. This method was applied to NASA SEDAC, which has 41 datasets in total. In GES DISC there can be as many as ~100 datasets per mission/instrument with some of the datasets consisting of multiple variables so there is a need for more differentiating parameters for dataset identification in the paper. The approach we are currently investigating is creating AI classifiers that are based on multiple dataset features, or keywords, extracted from the NASA Earthdata Common Dataset Repository (CMR). The features are weighted based on how precisely they can identify a dataset. The classifier uses preprocessed paper text as input and searches for the CMR datasets whose feature sets are the closest to the feature sets contained in the paper. The challenges of dataset identification include variety of ways the paper authors describe the datasets in their papers and incomplete tagging of the CMR dataset description (DIFs).

Irina Gerasimov↗
DOE OSTI2024

Machine learning for the identification of phase transitions in interacting agent-based systems: A Desai-Zwanzig example

Deriving closed-form analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In this paper, we propose a data-driven framework that pinpoints phase transitions for an ABM—the Desai-Zwanzig model—in its mean-field limit, using a smaller number of variables than traditional closed-form models. To this end, we use the manifold learning algorithm Diffusion Maps to identify a parsimonious set of data-driven latent variables, and we show that they are in one-to-one correspondence with the expected theoretical order parameter of the ABM. We then utilize a deep learning framework to obtain a conformal reparametrization of the data-driven coordinates that facilitates, in our example, the identification of a single parameter-dependent ordinary differential equation (ODE) in these coordinates. Additionally, we identify this ODE through a residual neural network inspired by a numerical integration scheme (forward Euler). We then use the identified ODE—enabled through an odd symmetry transformation—to construct the bifurcation diagram exhibiting the phase transition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Identification and localization of rotational spectra using recurrent neural networks

A method of identifying molecular parameters in a complex mixture may include receiving a set of combined transition frequencies and analyzing the set of combined transition frequencies using a first trained artificial neural network to generate a plurality of separated transition frequency sets. Each of the plurality of separated frequency sets may be analyzed using a second trained artificial neural network to generate a respective set of estimated spectral parameters. The method may include identifying a set of molecular parameters corresponding to the set of separated transition frequencies.

Prozuments, Kirills↗
NASA NTRS1988

Parameter estimation technique for boundary value problems by spline collocation method

A parameter-estimation technique for boundary-integral equations of the second kind is developed. The output least-squares identification technique using the spline collocation method is considered. The convergence analysis for the numerical method is discussed. The results are applied to boundary parameter estimations for two-dimensional Laplace and Helmholtz equations.

Kojima, Fumio↗
NASA NTRS1995

Ultra-Low Noise HEMT Device Models: Application of On-Wafer Cryogenic Noise Analysis and Improved Parameter Extraction Techniques

Significant advances in the development of HEMT technology have resulted in high performance cryogenic low noise amplifiers whose noise temperatures are within an order of magnitude of the quantum noise limit. Key to the identification of optimum HEMT structures at cryogenic temperatures is the development of on-wafer noise and device parameter extraction techniques. Techniques and results are described.

electronic materials↗
NASA NTRS1985

An Algorithm for Efficient Maximum Likelihood Estimation and Confidence Interval Determination in Nonlinear Estimation Problems

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The algorithm was developed for airplane parameter estimation problems but is well suited for most nonlinear, multivariable, dynamic systems. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort. MNRES determines the sensitivities with less computational effort than using either a finite-difference method or integrating the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, thus eliminating algorithm reformulation with each new model and providing flexibility to use model equations in any format that is convenient. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. It is observed that the degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. The CR bounds were found to be close to the bounds determined by the search when the degree of nonlinearity was small. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels for the parameter confidence limits. The primary utility of the measure, however, was found to be in predicting the degree of agreement between Cramer-Rao bounds and search estimates.

Murphy, Patrick Charles↗
DOE OSTI2023

Transcriptome architecture of the three main lineages of agrobacteria

ABSTRACT Agrobacteria are a diverse, polyphyletic group of prokaryotes with multipartite genomes capable of transferring DNA into the genomes of host plants, making them an essential tool in plant biotechnology. Despite their utility in plant transformation, genome-wide transcriptional regulation is not well understood across the three main lineages of agrobacteria. Transcription start sites (TSSs) are a necessary component of gene expression and regulation. In this study, we used differential RNA-seq and a TSS identification algorithm optimized on manually annotated TSS, then validated with existing TSS to identify thousands of TSS with nucleotide resolution for representatives of each lineage. We extend upon the 356 TSSs previously reported in Agrobacterium fabrum C58 by identifying 1,916 TSSs. In addition, we completed genomes and phenotyping of Rhizobium rhizogenes C16/80 and Allorhizobium vitis T60/94, identifying 2,650 and 2,432 TSSs, respectively. Parameter optimization was crucial for an accurate, high-resolution view of genome and transcriptional dynamics, highlighting the importance of algorithm optimization in genome-wide TSS identification and genomics at large. The optimized algorithm reduced the number of TSSs identified internal and antisense to the coding sequence on average by 90.5% and 91.9%, respectively. Comparison of TSS conservation between orthologs of the three lineages revealed differences in cell cycle regulation of ctrA as well as divergence of transcriptional regulation of chemotaxis-related genes when grown in conditions that simulate the plant environment. These results provide a framework to elucidate the mechanistic basis and evolution of pathology across the three main lineages of agrobacteria. IMPORTANCE Transcription start sites (TSSs) are fundamental for understanding gene expression and regulation. Agrobacteria, a group of prokaryotes with the ability to transfer DNA into the genomes of host plants, are widely used in plant biotechnology. However, the genome-wide transcriptional regulation of agrobacteria is not well understood, especially in less-studied lineages. Differential RNA-seq and an optimized algorithm enabled identification of thousands of TSSs with nucleotide resolution for representatives of each lineage. The results of this study provide a framework for elucidating the mechanistic basis and evolution of pathology across the three main lineages of agrobacteria. The optimized algorithm also highlights the importance of parameter optimization in genome-wide TSS identification and genomics at large.

59 BASIC BIOLOGICAL SCIENCES↗