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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Chemical speciation correlated with microstructural heterogeneity of interdicted uranium materials

Two uranium powders seized by law enforcement in Victoria, Australia, have been characterized by established nuclear forensic methods in a previously published study. Here, in this work, the results of further characterization by a scanning transmission x-ray microscope (STXM) operating in the soft x-ray regime are reported. STXM images are used to estimate the elemental distribution in micrometer-scale particles of each powder, and oxygen K-edge absorption spectra are used to determine the chemical state of uranium. The results of the current study are consistent with the previous analysis; the first powder is found to be a potassium-uranium hydrate, while the second powder is determined to be a mixture of uranium oxides primarily consisting of UO 3 .

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Nonparametric Inference for the Reproductive Rate in Generalized Compartmental Models

We develop a tractable nonparametric model for the time-varying reproductive rate of infectious diseases that combines the structure of a deterministic compartmental model and a stochastic model for incidence data. We use Bayesian inference to estimate, with uncertainty, the reproductive rate of the Coronavirus 2019 outbreak in the U.S. states of California, Florida, Michigan, New Mexico, New York, and Texas from January 2020 to March 2022. Employing the inferred reproductive rates, we estimate the posterior distribution of the time-varying reproduction numbers for each state. Compering the time-varying reproduction numbers across the states, we identify some epidemic waves, potentially driven from changes in human behavior and virus mutations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Light element ($$Z=1,2$$) production from spontaneous ternary fission of $$^{252}$$Cf

Abstract The yields of light elements ( $$Z=1,2$$ Z = 1 , 2 ) obtained from spontaneous ternary fission of $$^{252}$$ 252 Cf are treated within a nonequilibrium approach, and the contribution of unstable nuclei and excited bound states is taken into account. These light cluster yields may be used to probe dense matter, and to infer in-medium corrections such as Pauli blocking which is determined by the nucleon density. Continuum correlations are calculated from scattering phase shifts using the Beth-Uhlenbeck formula, and the effect of medium modification is estimated. The relevant distribution is reconstructed from the measured yields of isotopes. This describes the state of the nucleon system at scission and cluster formation, using only three Lagrange parameters which are the nonequilibrium counterparts of the temperature and chemical potentials, as defined in thermodynamic equilibrium. We concluded that a simple nuclear statistical equilibrium model neglecting continuum correlations and medium effects is not able to describe the measured distribution of H and He isotopes. Moreover, the freeze-out concept may serve as an important ingredient to the nonequilibrium approach using the relevant statistical operator concept.

Röpke, G.↗
DOE OSTI2022

Time-Based CAN IDS Paper Results Code

Modern vehicles are complex cyber-physical systems made of hundreds of electronic control units (ECUs) that communicate over controller area networks (CANs). This inherited complexity has expanded the CAN attack surface which is vulnerable to message injection attacks. These injections change the overall timing characteristics of messages on the bus, and thus, to detect these malicious messages, time-based intrusion detection systems (IDSs) have been proposed. However, time-based IDSs are usually trained and tested on low-fidelity datasets with unrealistic, labeled attacks. This makes difficult the task of evaluating, comparing, and validating IDSs. Here we detail and benchmark four time-based IDSs against the newly published ROAD dataset, the first open CAN IDS dataset with real (non-simulated) stealthy attacks with physically verified effects. We found that methods that perform hypothesis testing by explicitly estimating message timing distributions have lower performance than methods that seek anomalies in a distribution related statistic. In particular, these “distribution-agnostic” based methods outperform “distribution-based” methods by at least 55% in area under the precision-recall curve (AUC-PR). Our results expand the body of knowledge of CAN time-based IDSs by providing details of these methods and reporting their results when tested on datasets with real advanced attacks. Finally, we develop an after-market plug-in detector using lightweight hardware, which can be used to deploy the best performing IDS method on nearly any vehicle.

Moriano, Pablo [Oak Ridge National Lab. (ORNL), Oa↗
DOE OSTI2024

Plastics Environmental Risk Calculator

SF-24-074 The Plastics Environmental Risk Calculator (PERC) was developed in Microsoft Excel and estimates the environmental distribution and lifetime of new biobased and conventional plastics from commonly measured properties of the plastics. A Random Forest regression model embedded in the calculator calculates plastic degradation rates and lifetimes as a proxy for environmental risk. Model default assumptions may be overwritten by the user.

Beckman, Kevin [Argonne National Laboratory (ANL),↗
DOE OSTI2021

Physics from Cosmic Surveys (Final Report)

The main focus of the PI during this time was leading the effort to produce cosmological results on the data set from the first three years from the Dark Energy Survey (DES). The analysis combines weak gravitational lensing and maps of the galaxy distribution, supplemented by estimates of the redshifts of galaxies. This will ultimately lead to 33 papers, culminating in a single paper that brings all this together and provides a constraint on the clustering parameter σ 8 .This will be directly compared to the constraint obtained by high-redshift experiments, such as the Planck satellite, extrapolated forward assuming the CDM model. If the two values disagree, as they currently seem to at the 2-sigma level, this will be an indication that the model is incorrect or incomplete. These papers are expected to be submitted in January, 2021. Leading this effort, with help from many others, has been the primary research focus of the PI during this grant.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Representing Complex Systems as Graphs for Debugging and Predictive Maintenance-Preliminary Thoughts

Representing complex systems as graphs enables use of mathematical tools to identify faults or predict failures. Graph nodes correspond to individual modules or subsystems, and edges link coupled system parts. ‘Probes’ measure the node outputs, monitoring the system health for unexpected behavior. Assuming one cannot probe every point, within a system, the fault correlates to a region—not necessarily the specific location. Bayesian networks trained to understand fault patterns can accurately identify the source. The diagnostic tool described aides debugging by pinpointing system failure causes. For predictive maintenance, probe data develop probability distribution functions describing subsystem mean time to failure. Unit lifetime can be estimated through these probability distributions. Two approaches include using Bayesian classifiers to infer the system failure source and developing maintenance schedules by treating systems as collections of random variables. When failure behavior does not follow a closed form function, use of similarity models is proposed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Entanglement swapping systems toward a quantum internet

We demonstrate conditional entanglement swapping, i.e. teleportation of entanglement, between time-bin qubits at the telecommunication wavelength of 1536.4 nm with high fidelity of 87%. Our system is deployable, utilizing modular, off-the-shelf, fiber-coupled, and electrically controlled components such as electro-optic modulators. It leverages the precise timing resolution of superconducting nanowire detectors, which are controlled and read out via a custom developed graphical user interface. The swapping process is described, interpreted, and guided using characteristic function-based analytical modeling that accounts for realistic imperfections. Our system supports quantum networking protocols, including source-independent quantum key distribution, with an estimated secret key rate of approximately 0.5 bits per sifted bit.

Davis, Samantha I. [Caltech] (ORCID:00000001999481↗
DOE OSTI2021

Life Cycle Emissions Factors for Electricity Generation Technologies

This dataset consists of a table containing the distribution of literature estimates of greenhouse gas emissions for the following electricity generation and storage technologies: biopower, coal, concentrating solar power, geothermal, hydrogen storage, hydropower, lithium-ion battery storage, natural gas, nuclear, ocean, oil, photovoltaic, pumped-storage hydropower, and wind. Quartile estimates of life cycle emissions factors in units of grams of carbon dioxide equivalent per kilowatt hour of generation (g CO2e/kWh) are provided for the following life cycle stages: one-time upstream, ongoing combustion, ongoing non-combustion, one-time downstream, and total. Literature estimates were compiled by the LCA Harmonization study and subsequent updates, as detailed in the factsheet which accompanies this dataset, https://www.nlr.gov/docs/fy21osti/80580.pdf .

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1969

X-ray line emission from Sco X-1.

X ray line emission from Sco X-1 using Monte Carlo estimates of spectral distribution of scattered photons in terms of Fe line and cosmic abundance

Angel, J. R. P.↗
NASA NTRS1975

Intrepretation of surface features and surface processes on Mars

Eolian erosion and deposition on earth was studied in order to interpret the eolian land forms of Mars. Emphasis of the wind tunnel studies was on the flow field around models of eolian forms. Areas of the wind tunnel studies include: simulation of the atmospheric boundary layer; velocity profile measurements around different models in the desert boundary layer, and estimating shear stress distributions on the model surfaces; flow visualization techniques; streamline mapping using tuft photographs; and roughness contrasts.

Gad-El-hak, M.↗
NASA NTRS1976

Modal density function and number of propagating modes in ducts

The question of the number of propagating modes within a small range of mode cut off ratio was raised. The population density of modes were shown to be greatest near cut off and least for the well propagating modes. It was shown that modes of nearly the same cut off ratio behave nearly the same in a sound absorbing duct as well as in the way they propagate to the far. Handling all of the propagating modes individually, they can be grouped into several cut off ratio ranges. It is important to know the modal density function to estimate acoustic power distribution.

Rice, E. J.↗
NASA NTRS1976

Remote sensing of chlorophyll concentration from high altitude

A series of remote sensing experiments, using an airborne Ocean Color Scanner (OCS), has been carried out to demonstrate the feasibility of detecting surface chlorophyll concentrations in coastal water from high altitude. Upwelling radiance from the sea surface was recorded by 10 narrow bandwidth wavelength channels of the OCS, at an altitude of 19.8 km. Measurements were made over areas with vastly different biological activities. A strong correlation between the OCS radiance measurements and the surface chlorophyll measurements was found. The extracted chlorophyll signature agreed qualitatively with results from low altitude observations, except in the blue region. In addition, it was found that a simple algorithm could be used to estimate reliable chlorophyll distributions from OCS measurements.

Leung, K. C.↗