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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Networked information: Management issues for the acquisitions librarian

Historically, libraries have been the depositories and public access points to both domestic and international government information. A change in the information seeking behavior of the public is driving a change in government information publishing and dissemination. Patrons who traditionally used libraries for access to printed government information have become familiar and comfortable with the electronic environment. These data users are showing an increased interest in certain types of government information in electronic format, including indexes, numeric files, statistics, and hypertext documents. Government response to increased demand for electronic information has led to a flurry of special initiatives, with the production of information products on diskette, CD-ROM, and dissemination via the Internet. Libraries, and acquisitions units in particular, are being challenged to provide consistent and timely management of the information. The rapidly developing communications infrastructure, which frequently redesigns access to the information environment, poses significant obstacles to, and the tremendous opportunities for, making large bodies of government information available to a broad base of users.

Lawrence, Gregory↗
NASA NTRS2001

Investigation of Nitride Morphology After Self-Aligned Contact Etch

Self-Aligned Contact (SAC) etch has emerged as a key enabling technology for the fabrication of very large-scale memory devices. However, this is also a very challenging technology to implement from an etch viewpoint. The issues that arise range from poor oxide etch selectivity to nitride to problems with post etch nitride surface morphology. Unfortunately, the mechanisms that drive nitride loss and surface behavior remain poorly understood. Using a simple langmuir site balance model, SAC nitride etch simulations have been performed and compared to actual etched results. This approach permits the study of various etch mechanisms that may play a role in determining nitride loss and surface morphology. Particle trajectories and fluxes are computed using Monte-Carlo techniques and initial data obtained from double Langmuir probe measurements. Etched surface advancement is implemented using a shock tracking algorithm. Sticking coefficients and etch yields are adjusted to obtain the best agreement between actual etched results and simulated profiles.

Hwang, Helen H.↗
NASA NTRS2018

Projections of Future Sea Level Contributions from the Greenland and Antarctic Ice Sheets: Challenges Beyond Dynamical Ice Sheet Modeling

As Earth's climate warms, rising sea levels are becoming a great concern. Providing substantiated and well-informed guidance on the amount and rate of future sea level rise is important, but remains challenging. Observations of the Greenland and Antarctic Ice Sheets-Earth's largest freshwater reservoirs-reveal their ongoing, rapid, and complex changes in response to an evolving climate. The improved understanding of ice sheet behavior resulting from observations is driving the development of ice sheet models, and is allowing better simulation of past, present, and future ice sheet evolution. However, insight into future changes requires a better understanding of how ice sheets interact with other components of the Earth system and associated feedbacks. As climate models start to include dynamical ice sheet components, our understanding of such interactions and the feedback mechanisms will advance. These new developments in ice sheet modeling and their implementation in climate models are timely, as observations indicate an accelerating contribution of ice sheets to sea level rise.

ice sheet model↗
NASA NTRSDate not supplied

TPSAS-NF1676L-29139-DND

The Arctic has been warming at a rate outpacing globally-averaged warming by 2-3 times, a phenomenon known as "Arctic Amplification" (AA). AA is evident in observations of Arctic surface temperature over the last century as well as in model predictions. The accuracy of model predictions is crucial to determine our response to climate change, as Arctic changes have consequences for the atmospheric circulation, sea level rise, and the carbon cycle. Unfortunately, models show a wide range of possible futures and intermodel spread in projections of Arctic temperature is larger than for any other region: a 2°C global temperature increase- the limit set by the Paris Climate Agreement- would lead to an Arctic temperature change between 3-7°C according to Coupled Model Intercomparison 5 (CMIP5) models. This intermodel spread represents a key deficiency in the understanding of Arctic feedback processes and how changes in various physical parameters (e.g. sea ice cover, clouds, heat transport and surface fluxes) affect the temperature response. Using a process-oriented decomposition of the simulate Arctic surface energy budget, we attribute the largest contribution to the CMIP5 intermodel spead in projected Arctic warming to the Barents-Kara Sea region. Our results reveal that the increasing seasonal fluxing of ocean heat content is a significant part of the intermodel spread in AA and relates strongly to change in surface turbulent fluxes. Our results indicate that if we are to significantly reduce the intermodel spread in projected AA we must focus on understanding the behavior and physical processes that drive surface energy flows in the B-K sea region.

Patrick Taylor↗
DOE OSTI2023

Charging Needs for Battery Electric Semi-Trucks

Battery-electric vehicles provide a pathway to decarbonize heavy-duty trucking, but the market for heavy-duty battery-electric semi-trailer trucks is nascent, and specific charging requirements remain uncertain. We leverage large-scale vehicle telematics data (>205 million miles of driving) to estimate the charging behaviors and infrastructure requirements for U.S. battery-electric semi-trailer trucks within three operating segments: local, regional, and long-haul. We model two types of charging; mid-shift (fast) and off-shift (slow), and show that off-shift charging at speeds compatible with current light-duty charging infrastructure (i.e., =350 kW) can supply 35 to 77% of total energy demand for local and regional trucks with =300-mile range. Megawatt-level speeds are required for mid-shift charging, which make up 44 to 57% of energy demand for long-haul trucks with =500-mile range. However, demand shifts from mid-shift to off-shift charging as the range for battery-electric trucks increases and when off-shift charging is widely available. Finally, we observe geographic trends in charging demand, finding that local trucks have greater demand within urban areas, whereas long-haul trucks have more demand along rural interstate corridors. As the range for battery-electric trucks increases, we show that charging demand shifts from rural to urban locations due to observed vehicle dwell tendencies.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2023

Charging Needs for Battery Electric Semi Trucks

Battery-electric vehicles provide a pathway to decarbonize heavy-duty trucking, but the market for electric trucks is nascent, and specific charging requirements remain uncertain. This paper summarizes methods and findings from Charging Needs for Electric Semi-Trailer Trucks [1] wherein we leverage large-scale vehicle telematics data (>205 million miles of driving) to estimate the charging behaviors and infrastructure requirements for U.S. battery-electric semi-trailer trucks within three operating segments: local, regional, and long-haul. We model two types of charging - mid-shift (fast en-route charging) and off-shift (slow depot charging) - and show that off-shift charging at speeds compatible with current light-duty charging infrastructure (i.e., =350 kW) can supply 35% to 77% of total energy demand for local and regional trucks with =300-mile range. Megawatt-level speeds are required for mid-shift charging, which make up 44% to 57% of energy demand for long-haul trucks with =500-mile range. However, the role of off-shift charging increases as the range for battery-electric trucks increases and when off-shift charging is widely available.

ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

Progressive Damage Failure Analysis of a Multi-Stringer Post-Buckled Panel

Validation of a progressive damage finite element analysis model using the CompDam material model was performed on a large multi-stringer panel subjected to compressive loading. The panel had a Teflon insert embedded between the skin panel and stiffener to cause an ultimate failure mode of stiffener separation. The compressive loading of the panel caused the skin between the stiffeners to locally buckle before any damage began to occur. The post-buckled behavior of the panel was the driving mechanism to the onset of damage and the damage growth that led to skin-stiffener separation. An integrated global-local modeling approach was used to validate several aspects the overall behavior of the panel from start to failure. The global region captured the pre-buckled stiffness of the test panel within 10% and the buckling load of the skin panel at the critical location within 2%. The skin’s five half-wave buckled mode shape was accurately predicted by the model. The integrated local model captured the load corresponding to the onset of damage within 5% of the average test data. Comparisons are made for key aspects of the damage morphology, such as a growth pattern that included a significant matrix split in the first ply of the skin and a migration of the delamination from the skin-stiffener interface to the skin’sply1-ply2 interface. The ultimate failure mode was shown to be an unstable growth of delamination damage under the stiffeners which led to a peak load of the analysis that was approximately 5% above the average peak load of the testing. The validated global-local modeling approach used on the multi-stringer post-buckled panel with a Teflon insert used a methodology and lessons learned using smaller specimens applied to a subcomponent that captured several aspects of a modern aircraft design.

Jason Action↗
DOE OSTI2024

Interactions of laser-driven tin ejecta microjets over phase transition boundaries

Ejecta microjets offer an experimental methodology to study high-speed particle laden-flow interactions, as microjets consist of millions of particulates traveling at velocities of several kilometers per second and are easily generated by most common shock drives. Previous experiments on the OMEGA Extended Performance laser found that collisions between two counter-propagating laser-driven tin ejecta microjets varied as a function of drive pressure; jets generated near shock pressures of 10 GPa passed through each other without interacting, whereas jets generated at shock pressures of over 100 GPa interacted strongly, forming a cloud around the center interaction point. In this paper, we present a more systematic scan of tin ejecta microjet collisions over intermediate pressure regimes to identify how and at what shock pressure interaction behavior onsets. Radiographs of interacting microjets at five different laser drive energies qualitatively demonstrate that interaction behavior onsets slowly as a function of laser drive energy. Quantitative mass and density metrics from each radiograph provide trends on jet characteristics and collisional mass dispersion. It is observed that jetting mass, jet densities, and mass dispersion increase with increasing drive pressures and that the increased jet density at the higher drive energies may account for the increased mass dispersion. This work provides an important step in the understanding of tin ejecta microjet collisions and paves the way for future studies on the physics dominating high-speed particle-laden flow interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Direct observation of annealing-driven recrystallization behavior in magnesium alloy at low strain condition

Grain-twin interactions are significant in texture modification under thermodynamic driving force. In this study, annealing-driven twinning/detwinning behavior, grain growth, and corresponding texture evolution in a pre-deformed AZ31 magnesium alloy were systematically tracked and investigated via in-situ heating synchrotron X-ray diffraction and quasi in-situ electron backscattered diffraction techniques. A twinning texture is generated in the pre-deformed sample due to the activation of {$10\bar{1}2$} tensile twinning. During annealing, dislocation annihilation occurs between 100 and 280 °C, and recrystallization occurs above 280 °C, manifesting as the initial residual matrix and twins being competitively swallowed by each other, forming a bimodal texture. The recrystallization process is completed by boundary movement, which depends on the energy difference across the boundary. In addition, it is found that the grain boundaries favor movement towards the side with higher stored energy, regardless of the boundary type or the boundary energy.

In-situ observation↗
DOE OSTI2026

Multi-ignition fire complexes drive extreme fire years and impacts

Climate change is intensifying fire behavior, with the largest and fastest-spreading fires causing the greatest impacts on people and ecosystems. Yet the mechanisms driving variability and trends in large fires remain poorly understood. Using 12-hour satellite-derived fire tracking data from 2012 to 2023, we show that the merging of separate ignitions into multi-ignition complexes is a key process amplifying fire size and destructive potential across temperate and boreal ecoregions. Multi-ignition fires account for 31% of the burned area in California and 59% in the Arctic-boreal domain, spread faster and persist longer than single-ignition fires, and disproportionately contribute to extreme fire years in California, Canada, and Siberia. They also generate stronger atmospheric feedbacks, produce more pyrocumulonimbus events, and strain firefighting capacity by dispersing suppression resources. Recognizing and accounting for fire-merging dynamics are critical for improving wildfire prediction, risk assessment, and management.

Environmental sciences↗
DOE OSTI2023

Depletion-driven thermochemistry of molten salt reactors: review, method, and analysis

Molten salt reactors (MSRs) are innovative advanced nuclear reactors that utilize nuclear fuel by dissolving it in a high-temperature liquid salt. This unique feature differentiates MSRs from other types of reactors and allows for enhanced safety and economic performance. The liquid fuel also entails several multiphysics effects that can complicate reactor design and operation. One primary effect termed here as depletion-driven thermochemistry is a driving force in altering the multiphysics behavior of the reactor. Essentially, depletion-driven thermochemistry is the effect that fuel depletion has on changing the chemical redox potential of the fuel salt over time. As the fuel is consumed, the redox potential shifts toward a more oxidizing state. Without active control, the changing chemistry due to depletion increases corrosion thereby limiting reactor component lifetimes. Additionally, the changing redox potential of the fuel salt alters the vapor pressures of chemical species dissolved in the fuel salt. Changing vapor pressures of species in the fuel salt is an important parameter to understand when off-gassing volatile species during normal reactor operation, and for source term characterization during accident scenario transients. The present work represents a fundamental step toward modeling and coupling the driving physics (i.e., neutronics and chemistry) involved in altering the redox potential in an MSR. Here, the neutronic code Griffin models the depletion of the fuel-salt system, while the chemical equilibrium code Thermochimica calculates the thermochemical state of the isotopic inventory, using the Molten Salt Thermodynamic Database - Thermochemical (MSTDB-TC). These two codes are tightly coupled to predict the impact of fuel depletion in altering the chemistry in MSR systems. Redox potential control methods are discussed and can be modeled using this multiphysics approach. The vapor pressures of chemical species that could be extracted to an off-gas system, as determined by the reactor’s thermochemical state, are examined. The neutronics-chemistry coupling developed in this work is expected to have potential application for analyzing corrosion, source term evolution, and material safeguards in MSR systems. Lastly, suggestions for areas of further improvements of the models to expand these capabilities by incorporating other coupled physics effects is provided.

Walker, Samuel A.↗
DOE OSTI2022

Lifelike behavior of chemically oscillating mobile capsules

Inspired by the self-organization of unicellular species into multicellular organisms, we use theory and simulation to design a system of mobile, active microcapsules that produce chemicals according to a catalytic reaction network (CRN). In solution, the catalytic reactions generate a force that drives the flow of the surrounding fluid. The convective flow, in turn, transports the capsules to new chemical surroundings with each successive translation. Consequently, the chemical signal produced by a cluster of capsules is critically dependent on the proximity and spatial configuration of the neighboring capsules. If all of the capsules and chemical products involved in the CRN lie within sufficient proximity, then the system develops oscillatory behavior that drives the dynamic self-assembly of capsules into larger clusters that are capable of collective action. Finally, these simulations indicate potential chemo-mechanic mechanisms that enable single cellular units, such as ameba, to organize into multicellular life forms.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Case Study: Analysis of Autonomous Center line Tracking Neural Networks

Deep neural networks have gained widespread usage in a number of applications. However, limitations such as lack of explainability and robustness inhibit building trust in their behavior, which is crucial in safety critical applications such as autonomous driving. Therefore, techniques which aid in understanding and providing guarantees for neural network behavior are the need of the hour. In this paper, we present a case study applying a recently proposed technique, Prophecy, to analyze the behavior of a neural network model, provided by our industry partner and used for autonomous guiding of airplanes on taxi runways. This regression model takes as input an image of the runway and produces two outputs, cross-track error and heading error, which represent the position of the plane relative to the center line. We use the Prophecy tool to extract neuron activation patterns for the correctness and safety properties of the model. We show the use of these patterns to identify features of the input that explain correct and incorrect behavior. We also use the patterns to provide guarantees of consistent behavior. We explore a novel idea of using sequences of images (instead of single images) to obtain good explanations and identify regions of consistent behavior.

Deep Neural Networks↗
DOE OSTI2020

Revealing transient powder-gas interaction in laser powder bed fusion process through multi-physics modeling and high-speed synchrotron x-ray imaging

Laser powder bed fusion (LPBF) is an emerging metal additive manufacturing process. The gas-driven powder motions in laser powder bed fusion have significant influence on the build quality. However, the transient powder-gas interaction has not been well understood due to the challenges in quantitative experiment measurements. In this work, the powder-gas interaction for a single pulse laser illuminating on the powder bed is studied. We establish a multi-physics model to simulate the complex liquid/gas flow as well as the gas-driven powder motions, which is substantiated by high-speed synchrotron x-ray imaging. We identify and quantify four characteristic modes of powder-gas interaction in LPBF. The motion of a powder is controlled by one or multiple interaction modes collectively. As revealed by simulations and confirmed by experiments, powders can merge into the molten pool from its rim, be ejected at different divergence angles (powder spattering), or dive into the molten pool to cause significant molten pool fluctuation. Overall, our results provide insights toward the driving forces controlling the dynamic powder behavior, which pave the way for reducing structure defects during the build process.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Oxygen Distribution and Segregation at Grain Boundaries in Nb and Ta-Encapsulated Nb Thin Films for Superconducting Qubits

We report on atomic-scale analyses of oxygen distribution and segregation at grain boundaries (GBs) of Nb and Ta-encapsulated Nb (Ta/Nb) thin films for superconducting qubits using atom probe tomography (APT) and transmission electron microscopy (TEM). We observe oxygen segregation at grain boundaries (GBs) relative to the oxygen concentration within the grains for both Nb and Ta-capped Nb thin films and find that a higher oxygen concentration in the interior of Nb grains leads to greater oxygen segregation levels at GBs. This finding reveals that the formation of a local equilibrium of oxygen concentration between GBs and grain interiors of Nb is the primary driving force of the oxygen segregation behaviors in Nb and Ta-capped Nb. The enrichment factors (CGB/Cgrain) for oxygen segregation at GBs in Nb and Ta-capped Nb range from 2.4 ± 0.3 to 2.7 ± 0.4. The current results also highlight that controlling oxygen impurities in Nb during film deposition and fabrication processing is important to concomitantly reducing the level of oxygen segregation at GBs in Nb. Finally, we find that increases in the oxygen concentration in both Nb grains and GBs correlate with a suppression in the critical temperature for superconductivity (Tc). Together, our comparative chemical and charge transport property analyses provide atomic-scale insights into a potential mechanism, contributing to the decoherence in superconducting qubits.

Lee, Jaeyel [Fermilab] (ORCID:0000000185429612)↗
DOE OSTI2023

Oligosaccharide production and signaling correlate with delayed flowering in an Arabidopsis genotype grown and selected in high [CO 2 ]

Since industrialization began, atmospheric CO 2 ([CO 2 ]) has increased from 270 to 415 ppm and is projected to reach 800–1000 ppm this century. Some Arabidopsis thaliana (Arabidopsis) genotypes delayed flowering in elevated [CO 2 ] relative to current [CO 2 ], while others showed no change or accelerations. To predict genotype-specific flowering behaviors, we must understand the mechanisms driving flowering response to rising [CO 2 ]. [CO 2 ] changes alter photosynthesis and carbohydrates in plants. Plants sense carbohydrate levels, and exogenous carbohydrate application influences flowering time and flowering transcript levels. We asked how organismal changes in carbohydrates and transcription correlate with changes in flowering time under elevated [CO 2 ]. We used a genotype (SG) of Arabidopsis that was selected for high fitness at elevated [CO 2 ] (700 ppm). SG delays flowering under elevated [CO 2 ] (700 ppm) relative to current [CO 2 ] (400 ppm). We compared SG to a closely related control genotype (CG) that shows no [CO 2 ]-induced flowering change. We compared metabolomic and transcriptomic profiles in these genotypes at current and elevated [CO 2 ] to assess correlations with flowering in these conditions. While both genotypes altered carbohydrates in response to elevated [CO 2 ], SG had higher levels of sucrose than CG and showed a stronger increase in glucose and fructose in elevated [CO 2 ]. Both genotypes demonstrated transcriptional changes, with CG increasing genes related to fructose 1,6-bisphosphate breakdown, amino acid synthesis, and secondary metabolites; and SG decreasing genes related to starch and sugar metabolism, but increasing genes involved in oligosaccharide production and sugar modifications. Genes associated with flowering regulation within the photoperiod, vernalization, and meristem identity pathways were altered in these genotypes. Elevated [CO 2 ] may alter carbohydrates to influence transcription in both genotypes and delayed flowering in SG. Changes in the oligosaccharide pool may contribute to delayed flowering in SG. This work extends the literature exploring genotypic-specific flowering responses to elevated [CO 2 ].

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Potential Impacts of Dynamic Electricity Pricing in California: Load Shape and Customer Bill Impacts Under Elastic Customer Response

The increasing penetration of renewable energy in California has intensified grid management challenges, exemplified by the “duck curve” and the resulting need for steep ramping and curtailment of renewables. To address these issues, dynamic electricity tariffs that vary in near-real time are being considered to incentivize customers to shift demand and support the grid. This study extends previous work on the bill impacts of such tariffs in the absence of load response by quantifying the system-level and customer impacts of load response based on customer price elasticity. Customer-level load response modeling was conducted using meter data from 411,000 customers across residential, commercial, and industrial sectors. Customer demand elasticity was estimated using literature-based values, with scenarios ranging from low to high elasticity, including an automation-enhanced scenario. Results indicate that universal adoption of, and response to, dynamic tariffs can significantly reduce peak net load (by 15%) and maximum ramping requirements (by 20%) with moderate elasticity, delivering demand response resources comparable to or exceeding current programs at all elasticity levels. Bill analysis shows that, when responding elastically to dynamic prices, most non-PV customers experience modest savings, while PV customers may see higher effective rates due to lower compensation for exports during low-price periods. Emissions analysis reveals a reduction in per-kWh emissions system-wide, with a total absolute load increase of 2% accompanied by a negligible absolute emissions increase. The study concludes that while dynamic tariffs offer substantial grid benefits, customer bill savings under modeled response behaviors may be too modest to drive widespread adoption without additional incentives or enabling technologies. Future research should model flexible loads and advanced control technologies with greater fidelity to better represent the potential opportunities of dynamic tariffs.

24 POWER TRANSMISSION AND DISTRIBUTION↗