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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Discriminating Accretion States via Rotational Symmetry in Simulated Polarimetric Images of M87

In 2017 April, the Event Horizon Telescope observed the shadow of the supermassive black hole at the core of the elliptical galaxy Messier 87. While the original image was constructed from measurements of the total intensity, full polarimetric data were also collected, and linear polarimetric images are expected in the near future. We propose a modal image decomposition of the linear polarization field into basis functions with varying azimuthal dependence of the electric vector position angle. We apply this decomposition to images of ray traced general relativistic magnetohydrodynamics simulations of the Messier 87 accretion disk. Furthermore, for simulated images that are physically consistent with previous observations, the magnitude of the coefficient associated with rotational symmetry, β2, is a useful discriminator between accretion states. We find that at 20 μas resolution, $| {\beta }_{2}| $ is greater than 0.2 only for models of disks with horizon-scale magnetic pressures large enough to disrupt steady accretion. We also find that images with a more radially directed electric vector position angle correspond to models with higher black hole spin. Our analysis demonstrates the utility of the proposed decomposition as a diagnostic framework to improve constraints on theoretical models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Reliability modeling in a predictive maintenance context: A margin-based approach

Current system reliability methods (typically based on fault trees or reliability block diagrams) can effectively propagate reliability data from the asset to the system level in order to identify system critical points. However, employed asset reliability data are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers on the most critical assets. In addition, the propagation of quantitative health data from the asset to the system level is a challenge given the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, expected failure time). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Here, this paper is directly addressing these two goals by proposing a different approach for reliability modeling that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. The propagation of health data from the asset to the system level is performed through fault tree models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of an undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical assets.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Human-induced changes in the global meridional overturning circulation are emerging from the Southern Ocean

In a warming climate, the Global Meridional Overturning Circulation (GMOC) is expected to change significantly with a risk of disrupting the global redistribution of ocean properties that sustains marine ecosystems, carbon cycle, and others. Here we make a novel attempt to utilize a diagnostic ocean & sea-ice model to estimate the GMOC and its interdecadal changes since the mid-1950s that are consistent with historical hydrographic observations. We find that significant changes in the GMOC have already occurred, most notably in the upper and lower overturning cells in the Southern Ocean. The former has expanded poleward and into denser water and strengthened by 3–4 Sv since the mid-1970s, while the latter has contracted and weakened by a similar rate during the same period. These changes are driven by the increasing Southern Hemisphere (SH) Ferrel cell and associated increases in the westerlies and the surface buoyancy loss over its sinking branch, and the increasing Antarctic meltwater discharge, in response to ozone depletion in the SH stratosphere and increasing atmospheric CO 2 . A large-scale readjustment of the GMOC seems to be underway in the South Atlantic and Indo-Pacific Oceans since the mid-2000s in response to the Southern Ocean changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The effect of areal density asymmetries on scattered neutron spectra in ICF implosions

Scattered neutron spectroscopy is a diagnostic technique commonly used to measure areal density in inertial confinement fusion experiments. Deleterious areal density asymmetries modify the shape of the scattered neutron spectrum. In this work, a novel analysis is developed, which can be used to fit the shape change. This will allow experimental scattered neutron spectroscopy to directly infer the amplitude and mode of the areal density asymmetries, with little sensitivity to confounding factors that affect other diagnostics for areal density. The model is tested on spectra produced by a neutron transport calculation with both isotropic and anisotropic primary fusion neutron sources. Multiple lines of sight are required to infer the areal density distribution over the whole sphere—we investigate the error propagation and optimal detector arrangement associated with the inference of mode 1 asymmetries.

Crilly, A. J. (ORCID:0000000204299332)↗
DOE OSTI2021

Three-dimensional diagnostics and measurements of inertial confinement fusion plasmas

Recent inertial confinement fusion measurements have highlighted the importance of 3D asymmetry effects on implosion performance. One prominent example is the bulk drift velocity of the deuterium–tritium plasma undergoing fusion (“hotspot”), v HS . Upgrades to the National Ignition Facility neutron time-of-flight diagnostics now provide v HS to better than 1 part in 10 4 and enable cross correlations with other measurements. Here we present the impact of v HS on the neutron yield, downscatter ratio, apparent ion temperature, electron temperature, and 2D x-ray emission. The necessary improvements to diagnostic suites to take these measurements are also detailed. The benefits of using cross-diagnostic analysis to test hotspot models and theory are discussed, and cross-shot trends are shown.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Photochemistry in terrestrial exoplanet atmospheres. III. Photochemistry and thermochemistry in thick atmospheres on super Earths and mini Neptunes

Some super Earths and mini Neptunes will likely have thick atmospheres that are not H{sub 2}-dominated. We have developed a photochemistry-thermochemistry kinetic-transport model for exploring the compositions of thick atmospheres on super Earths and mini Neptunes, applicable for both H{sub 2}-dominated atmospheres and non-H{sub 2}-dominated atmospheres. Using this model to study thick atmospheres for wide ranges of temperatures and elemental abundances, we classify them into hydrogen-rich atmospheres, water-rich atmospheres, oxygen-rich atmospheres, and hydrocarbon-rich atmospheres. We find that carbon has to be in the form of CO{sub 2} rather than CH{sub 4} or CO in a H{sub 2}-depleted water-dominated thick atmosphere and that the preferred loss of light elements from an oxygen-poor carbon-rich atmosphere leads to the formation of unsaturated hydrocarbons (C{sub 2}H{sub 2} and C{sub 2}H{sub 4}). We apply our self-consistent atmosphere models to compute spectra and diagnostic features for known transiting low-mass exoplanets GJ 1214 b, HD 97658 b, and 55 Cnc e. For GJ 1214 b, we find that (1) C{sub 2}H{sub 2} features at 1.0 and 1.5 μm in transmission and C{sub 2}H{sub 2} and C{sub 2}H{sub 4} features at 9-14 μm in thermal emission are diagnostic for hydrocarbon-rich atmospheres; (2) a detection of water-vapor features and a confirmation of the nonexistence of methane features would provide sufficient evidence for a water-dominated atmosphere. In general, our simulations show that chemical stability has to be taken into account when interpreting the spectrum of a super Earth/mini Neptune. Water-dominated atmospheres only exist for carbon to oxygen ratios much lower than the solar ratio, suggesting that this kind of atmospheres could be rare.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Quantifying Stern layer water alignment before and during the oxygen evolution reaction

While water’s oxygen is the electron source in the industrially important oxygen evolution reaction, the strong absorber problem clouds our view of how the Stern layer water molecules orient themselves in response to applied potentials. Here, we report nonlinear optical measurements on nickel electrodes held at pH 13 indicating a disorder-to-order transition in the Stern layer water molecules before the onset of Faradaic current. A full water monolayer (1.1 × 10 15 centimeter −2 ) aligns with oxygen atoms pointing toward the electrode at +0.8 volt and the associated work is 80 kilojoule per mole. Our experiments identify water flipping energetics as a target for understanding overpotentials, advance molecular electrochemistry, provide benchmarks for electrical double layer models, and serve as a diagnostic tool for understanding electrocatalysis.

Science & Technology - Other Topics↗
DOE OSTI2019

Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella

Nontyphoidal Salmonella species are the leading bacterial cause of food-borne disease in the United States. Whole genome sequences and paired antimicrobial susceptibility data are available for Salmonella strains because of surveillance efforts from public health agencies. In this study, a collection of 5,278 nontyphoidal Salmonella genomes, collected over 15 years in the United States, were used to generate XGBoost-based machine learning models for predicting minimum inhibitory concentrations (MICs) for 15 antibiotics. The MIC prediction models have an overall average accuracy of 95% within ± 1 two-fold dilution step (confidence interval of 95-95%), an average very major error rate of 2.7% (confidence interval of 2.4-3.0%) and an average major error rate of 0.1% (confidence interval of 0.1-0.2%). The model predicts MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for training sets, we show that highly accurate MIC prediction models can be generated with fewer than 500 genomes. We also show that our approach for predicting MICs is stable over time despite annual fluctuations in antimicrobial resistance gene content in the sampled genomes. Finally, using feature selection, we explore the important genomic regions identified by the models for predicting MICs. To date, this is one of the largest MIC modeling studies to be published. Furthermore, our strategy for developing whole genome sequence-based models for surveillance and clinical diagnostics can be readily applied to other important human pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

elm-diagnostics

elm-diagnostics is a Python package for computing diagnostic analyses and visualizations for the E3SM Land Model (ELM) component and is meant to support new feature development in ELM. The tool reads model history files and performs quantitative analyses including budget-closure checking, variable transformations, temporal aggregations, and statistical summaries to support model evaluation, validation, and scientific interpretation. The framework is designed for extensibility, with modular architecture enabling straightforward addition of new diagnostic methods, derived variables, analysis types, visualization approaches, and model-specific adaptations

Hoffman, Matt [Los Alamos National Laboratory]↗
DOE OSTI2021

Tracker Fits for shot 1s1674

Gas gun shock-to-detonation experiments provide data for calibrating HE burn models. At LANL the principal diagnostic is a magnetic gauge package that provides data on the lead shock trajectory, which is used to determine the run-distance to detonation, and the lead shock speed with run distance, which together with jump-off velocity of the velocity gauges determines Hugoniot locus points for the reactants.

42 ENGINEERING↗
DOE OSTI2023

Fundamental Understanding of Dynamic Interfacial Phenomena in Solid State Batteries

Solid-state batteries (SSBs) are considered as one of the most promising battery technologies for resolving the intrinsic limitations of current lithium-ion batteries, such as safety and insufficient energy density. However, the degradation associated with interfaces in SSBs is still a critical issue leading to short cycle life. To get fundamental understanding of the failure mechanism of the interfaces, we first developed a comprehensive set of in situ diagnostic techniques combined with atomic/continuum modeling schemes to investigate and understand the coupled mechanical/chemical degradation associated with dynamic interfacial phenomena in SSBs. Specifically, we focused on in situ observations and characterizations of lithium plating-stripping processes, lithium dendrite formation, interphase formation, and the induced interfacial stresses, as well as the mechanical and electrochemical properties of interfaces and interphases. The final project report first gives an overview of the project background, objectives, technical approaches, and final deliverables for all the budget periods; then follows with a summary on main accomplishments based on main tasks. We particularly would like to highlight some capabilities developed from this project which can also be implemented to our advanced battery development.

25 ENERGY STORAGE↗
DOE OSTI2026

Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels (Final Scientific/Technical Report)

The project, "Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels," represents a significant advancement in nuclear fuel recycling technology. It integrates cutting-edge multimodal sensor fusion, machine learning (ML), and digital twin (DT) technologies to address challenges in material safeguarding, process optimization, and regulatory compliance for pyroprocessing facilities. This research has significantly enhanced the understanding of pyrochemical fuel recycling processes by developing innovative tools and methodologies. The Multimodal Safeguards Monitoring Unit (MSMU) combines electroanalytical techniques, Raman spectroscopy, and differential thermal analysis (DTA) to enable high-fidelity, near-real-time material accountancy measurements. Machine learning techniques, such as Long Short-Term Memory (LSTM) autoencoders, are utilized to detect anomalies in material balances and sensor data, improving the reliability of safeguards monitoring. Additionally, digital twin technology has been established to provide real-time system-level monitoring and diagnostics, integrating physics-based models with sensor data to optimize process safety and efficiency.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Intracardiac Electrical Imaging using the 12-lead ECG: A Machine Learning Approach using Synthetic Data

Current state-of-the-art techniques for non-invasive imaging of cardiac electrical phenomena require voltage recordings from dozens of different torso locations and anatomical models built from expensive medical diagnostic imaging procedures. Here this study aimed to assess if recent machine learning advances could alternatively reconstruct electroanatomical maps at clinically relevant resolutions using only the standard 12-lead electrocardiogram (ECG) as input. To that end, a computational study was conducted to generate a dataset of over 16000 detailed cardiac simulations, which was then used to train neural network (NN) architectures designed to exploit both spatial and temporal correlations in the ECG signal. Analysis over a validation set showed average errors in activation map reconstruction below 1.7 msec over 75 intracardiac locations. Furthermore, phenotypical patterns of activation and the morphology of the activation potential were correctly reconstructed. The approach offers opportunities to stratify patients non-invasively, both retrospectively and prospectively, using metrics otherwise only available through invasive clinical procedures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams

Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1979

On the ionization equilibrium balance

Ionization equilibrium for the most astrophysically abundant elements are calculated using multilevel atomic models for several stages of ionization. It is found that the relative abundance of the lithium-, beryllium-, and boron-like ions at electron densities as low as 1 million per cu cm is density-dependent. Because of this dependence, the intensity of lines arising from ions of the Li, Be, and B isoelectronic sequences are density-sensitive. The general implications of these results as applied to solar and stellar atmospheric models, coronal abundances, and spectroscopic diagnostic techniques are discussed.

Vernazza, J. E.↗
NASA NTRS1979

High-pressure transition regions in stellar model chromospheres

The implications of recent ultraviolet observations of stellar transition-region lines for calculations of the Ca II and Mg II resonance lines are investigated. It is found that the adoption of high transition-region pressures for stars with active chromospheres, such as Lambda And and Alpha Aur, can be consistent with observed Ca II fluxes, contrary to the results obtained by Kelch et al. (1978) for Alpha Aur. Furthermore, the adoption of the high-pressure models removes a long-standing difficulty in the line profile calculations, since the deep central absorption present in earlier calculations is less pronounced or absent, in closer agreement with observations. The apparent contradiction between these models and the recent density diagnostic of Doschek et al. (1978) is also discussed.

Baliunas, S. L.↗
NASA NTRS1981

Symposium /International/ on Combustion, 18th, University of Waterloo, Waterloo, Ontario, Canada, August 17-22, 1980, Proceedings

Problems related to combustion generated pollution are explored, taking into account the mechanism of NO formation from nitrogen compounds in hydrogen flames studied by laser fluorescence, the structure and similarity of nitric oxide production in turbulent diffusion flames, the effect of steam addition on NO formation, and the formation of NO2 by laminar flames. Other topics considered are concerned with propellant combustion, fluidized bed combustion, the combustion of droplets and sprays, premixed flame studies, fire studies, and flame stabilization. Attention is also given to coal flammability, chemical kinetics, turbulent combustion, soot, coal combustion, the modeling of combustion processes, combustion diagnostics, detonations and explosions, ignition, internal combustion engines, combustion studies, and furnaces.

Source record↗