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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Solar System Exploration Technology Requirements and Planning Process

The NASA Office of Space Science, in alliance with the Office of Advanced Concepts and Technology, has developed an Integrated Technology Strategy to guide its investment in technology for space science missions. This strategy establishes goals and strategic objectives that ensure that NASA and the nation derive the greatest possible scientific and economic benefits from investments in space science missions and technologies. Within the Office of Space Science, the Solar System Exploration Division in collaboration with the Office of Space Access and Technology, is developing a Technology Plan to implement these goals and to identify specific goals and to identify specific technology needs for solar system missions. This paper describes the philosophy, processes, and preliminary results of the systematic process being established for the identification and prioritization of solar system exploration technology needs.

space↗
NASA NTRS2008

Electronic Escape Trails for Firefighters

A proposed wireless-communication and data-processing system would exploit recent advances in radio-frequency identification devices (RFIDs) and software to establish information lifelines between firefighters in a burning building and a fire chief at a control station near but outside the building. The system would enable identification of trails that firefighters and others could follow to escape from the building, including identification of new trails should previously established trails become blocked. The system would include a transceiver unit and a computer at the control station, portable transceiver units carried by the firefighters in the building, and RFID tags that the firefighters would place at multiple locations as they move into and through the building (see figure). Each RFID tag, having a size of the order of a few centimeters, would include at least standard RFID circuitry and possibly sensors for measuring such other relevant environmental parameters as temperature, levels of light and sound, concentration of oxygen, concentrations of hazardous chemicals in smoke, and/or levels of nuclear radiation. The RFID tags would be activated and interrogated by the firefighters and control-station transceivers. Preferably, RFID tags would be configured to communicate with each other and with the firefighters units and the control station in an ordered sequence, with built-in redundancy. In a typical scenario, as firefighters moved through a building, they would scatter many RFID tags into smoke-obscured areas by use of a compressed-air gun. Alternatively or in addition, they would mark escape trails by dropping RFID tags at such points of interest as mantraps, hot spots, and trail waypoints. The RFID tags could be of different types, operating at different frequencies to identify their functions, and possibly responding by emitting audible beeps when activated by signals transmitted by transceiver units carried by nearby firefighters.

Jorgensen, Charles↗
DOE OSTI2012

PHASE II VAULT TESTING OF THE ARGONNE RFID SYSTEM

The U.S. Department of Energy (DOE) (Environmental Management [EM], Office of Packaging and Transportation [EM-45]) Packaging and Certification Program (DOE PCP) has developed a Radio Frequency Identification (RFID) tracking and monitoring system, called ARG-US, for the management of nuclear materials packages during transportation and storage. The performance of the ARG-US RFID equipment and system has been fully tested in two demonstration projects in April 2008 and August 2009. With the strong support of DOE-SR and DOE PCP, a field testing program was completed in Savannah River Site's K-Area Material Storage (KAMS) Facility, an active Category I Plutonium Storage Facility, in 2010. As the next step (Phase II) of continued vault testing for the ARG-US system, the Savannah River Site K Area Material Storage facility has placed the ARG-US RFIDs into the 910B storage vault for operational testing. This latest version (Mark III) of the Argonne RFID system now has the capability to measure radiation dose and dose rate. This paper will report field testing progress of the ARG-US RFID equipment in KAMS, the operability and reliability trend results associated with the applications of the system, and discuss the potential benefits in enhancing safety, security and materials accountability. The purpose of this Phase II K Area test is to verify the accuracy of the radiation monitoring and proper functionality of the ARG-US RFID equipment and system under a realistic environment in the KAMS facility. Deploying the ARG-US RFID system leads to a reduced need for manned surveillance and increased inventory periods by providing real-time access to status and event history traceability, including environmental condition monitoring and radiation monitoring. The successful completion of the testing program will provide field data to support a future development and testing. This will increase Operation efficiency and cost effectiveness for vault operation. As the next step (Phase II) of continued vault testing for the ARG-US system, the Savannah River Site K Area Material Storage facility has placed the ARG-US RFIDs into the 910B storage vault. Deploying the ARG-US RFID system lends to a reduced need for manned surveillance and increased inventory periods by providing real-time access to status and event history traceability, including radiation and environmental monitoring. The successful completion of the testing program will provide field data to support future development and testing.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Comparison of observed and simulated spatial patterns of ice microphysical processes in tropical oceanic mesoscale convective systems

To equitably compare the spatial pattern of ice microphysical processes produced by three microphysical parameterizations with each other, observations, and theory, simulations of tropical oceanic mesoscale convective systems (MCSs) in the Weather Research and Forecasting (WRF) model were forced to develop the same mesoscale circulations as observations by assimilating radial velocity data from a Doppler radar. The same general layering of microphysical processes was found in observations and simulations with deposition anywhere above the 0°C level, aggregation at and above the 0°C level, melting at and below the 0°C level, and riming near the 0°C level. Thus, this study is consistent with the layered ice microphysical pattern portrayed in previous conceptual models and indicated by dual-polarization radar data. Spatial variability of riming in the simulations suggests that riming in the midlevel inflow is related to convective-scale vertical velocity perturbations. Lastly, this study sheds light on limitations of current generally available bulk microphysical parameterizations. In each parameterization, the layers in which aggregation and riming took place were generally too thick and the frequency of riming was generally too high compared to the observations and theory. Additionally, none of the parameterizations produced similar details in every microphysical spatial pattern. Discrepancies in the patterns of microphysical processes between parameterizations likely factor into creating substantial differences in model reflectivity patterns. It is concluded that improved parameterizations of ice-phase microphysics will be essential to obtain reliable, consistent model simulations of tropical oceanic MCSs.

58 GEOSCIENCES↗
DOE OSTI2022

SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID-19 pandemic

Managing wildlife populations in the face of global change requires regular data on the abundance and distribution of wild animals, but acquiring these over appropriate spatial scales in a sustainable way has proven challenging. Here, in this study, we present the data from Snapshot USA 2020, a second annual national mammal survey of the USA. This project involved 152 scientists setting camera traps in a standardized protocol at 1485 locations across 103 arrays in 43 states for a total of 52,710 trap-nights of survey effort. Most (58) of these arrays were also sampled during the same months (September and October) in 2019, providing a direct comparison of animal populations in 2 years that includes data from both during and before the COVID-19 pandemic. All data were managed by the eMammal system, with all species identifications checked by at least two reviewers. In total, we recorded 117,415 detections of 78 species of wild mammals, 9236 detections of at least 43 species of birds, 15,851 detections of six domestic animals and 23,825 detections of humans or their vehicles. Spatial differences across arrays explained more variation in the relative abundance than temporal variation across years for all 38 species modeled, although there are examples of significant site-level differences among years for many species. Temporal results show how species allocate their time and can be used to study species interactions, including between humans and wildlife. These data provide a snapshot of the mammal community of the USA for 2020 and will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, and the impacts of species interactions on daily activity patterns. There are no copyright restrictions, and please cite this paper when using these data, or a subset of these data, for publication.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Data-Driven Method for Modeling Creep-Fatigue Stress- Strain Behavior Using Neural ODEs

In this paper, we introduce a data-driven machine learning approach for modeling one-dimensional stress–strain behavior under cyclic loading, utilizing experimental data from the nickel-based Alloy 617. The study employs uniaxial creep–fatigue test data acquired under various loading histories and compares two distinct neural network-based ODE models. The first model, known as the black-box model, comprehensively describes the strain–stress relationship using a Neural ODE equation. To interpret this black-box model, we apply the Sparse Identification of Nonlinear Dynamical Systems (SINDy) technique, transforming the black-box model into an equation-based model using symbolic regression. The second model, the Neural flow rule model, incorporates Hooke’s Law for the linear elastic component, with the nonlinear part characterized by a Neural ODE. Both models are trained with experimental data to accurately reflect the observed stress–strain behavior. We conduct a detailed comparison with the standard Chaboche model, which includes three back stresses. Our results demonstrate that the neural network-based ODE models precisely capture the experimental creep–fatigue mechanical behavior, exceeding the standard Chaboche model’s accuracy. Furthermore, an interpretable model derived from the black-box neural ODE model through symbolic regression achieves accuracy comparable to the Chaboche model, enhancing its interpretability. The results highlight the potential of neural network-based ODE models to depict complex creep–fatigue behavior, eliminating the necessity for experts to define a specific, material-focused model form.

creep-fatigue↗
DOE OSTI2020

A complete grain-level assessment of the stress-strain evolution and associated deformation response in polycrystalline alloys

Polycrystalline alloys are used pervasively across structural applications contingent upon extensive experimental testing. A statistically representative number of tests are required to expose the variability in the material's performance, as a result of non-uniform microstructures and associated micromechanical fields. In a more direct means of capturing this pertinent information, multi-modal experimental techniques are presented to measure and track the complete micromechanical state, evolving during loading, of each and every grain within the regions of interest. Specifically, a combination of high-energy X-ray diffraction microscopy and digital image correlation coupled with electron backscatter diffraction are conducted on a specimen for each of the alloys, Haynes 282 and Ti7Al. The results of the multi-modal analysis definitively demonstrate that the degree of heterogeneity increases with deformation level and is used to assess the number of grains necessary for a representative volume element description of the stress state for each of these materials. Moreover, higher resolution imaging is used for identification of the slip system activity and subsequently used to study slip transmission events. An accurate knowledge of the resolved shear stress in adjacent grains (grain interactions) is demonstrated to be a key descriptor of the slip transmission events.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Evolution and Degradation Patterns of Electrochemical Cells Based on the Analysis of Interfacial Phenomena at Li Metal Anode/Electrolyte Interfaces

In this work, we report the results of a theoretical–computational analysis of the solid electrolyte interphase (SEI) growth and degradation dynamics occurring in lithium metal batteries during cycling. We use ab initio-kinetic Monte Carlo simulations to generate a synthetic data set, which is analyzed by machine learning methods. We aim to determine: (i) how modifications in interfacial interaction energies between solid electrolyte interphase (SEI) blocks and between Li ions and SEI facets impact the Coulombic efficiency (CE) of the battery and (ii) what factors, including reactions, microscopic transport, and other interfacial events, may lead to cell performance “failure” during prolonged charge and discharge cycles, signaled as a sharp decay in the CE over cycling. The demonstration of our approach is done on a cell including a Li metal surface interfacing with a previously introduced state-of-the-art electrolyte, and the idea can be applied to any electrochemical system. Outcomes include the identification of the leading chemical, physical, and structural variables causing cell failure and relating them to the electrolyte formulation, thus paving the way to future more refined analysis and electrolyte design.

batteries↗
DOE OSTI2019

MAGI: A Method for Metabolite Annotation and Gene Integration

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (that is, gene annotations). Metabolite Annotation and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network. This is calculated by a method that emphasizes consensus between metabolites and genes via biochemical reactions. To demonstrate the potential of this method, we applied MAGI to integrate sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. Our findings suggest that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations in this organism. MAGI also made biochemical predictions for poorly annotated genes that were consistent with the extensive literature on this important organism. Furthermore, this limited analysis suggests that using metabolomics data has the potential to improve annotations in sequenced organisms and also provides testable hypotheses for specific biochemical functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Dynamic Multiplexed Control and Modeling of Optogenetic Systems Using the High-Throughput Optogenetic Platform, Lustro

The ability to control cellular processes using optogenetics is inducer-limited, with most optogenetic systems responding to blue light. To address this limitation, we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems. Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive split transcription factors in the budding yeast, Saccharomyces cerevisiae. We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control. This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs, with broad implications for biotechnology and bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

CALDER: Cryogenic light detectors for background-free searches

The development of background-free detectors is essential for experiments searching for rare events. Bolometers, that are among the most competitive devices for the study of neutrino-less double beta decay (0νDBD) and Dark Matter interactions, suffer from the absence of techniques that allow to identify the nature of the interacting particles. This limit can be overcome by coupling the bolometer to an independent device for the measurement of the light emitted by interactions, as the combined read-out of the bolometric and light signals allows to identify and reject particles different from those of interest. CUORE, the most advanced bolometric experiment for 0νDBD searches, could disentangle the electrons produced by 0νDBD from the dangerous background due to α particles, by measuring the (tiny) Cherenkov light emitted by electrons and not by α’s. LUCIFER, a project based on ZnSe scintillating bolometers for the study of {sup 82}Se 0νDBD, would be competitive also in the search of Dark Matter interactions if equipped with light detectors that allow to distinguish and reject the background due to electrons and γ’s. These advances require cryogenic detectors characterized by noise lower than 20 eV, large active area, wide temperature range of operation, high radio-purity and ease in fabricating hundreds of channels. The CALDER collaboration aims to develop such detectors by exploiting the superb energy resolution and natural multiplexed read-out provided by Kinetic Inductance Detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

In situ visualization of multicomponents coevolution in a battery pouch cell

Lithium-ion battery (LIB) is a broadly adopted technology for energy storage. With increasing demands to improve the rate capability, cyclability, energy density, safety, and cost efficiency, it is crucial to establish an in-depth understanding of the detailed structural evolution and cell-degradation mechanisms during battery operation. Here, we present a laboratory-based high-resolution and high-throughput X-ray micro–computed laminography approach, which is capable of in situ visualizing of an industry-relevant lithium-ion (Li-ion) pouch cell with superior detection fidelity, resolution, and reliability. This technique enables imaging of the pouch cell at a spatial resolution of 0.5 μm in a laboratory system and permits the identification of submicron features within cathode and anode electrodes. We also demonstrate direct visualization of the lithium plating in the imaged pouch cell, which is an important phenomenon relevant to battery fast charging and low-temperature cycling. Our development presents an avenue toward a thorough understanding of the correlation among multiscale structures, chemomechanical degradation, and electrochemical behavior of industry-scale battery pouch cells.

25 ENERGY STORAGE↗
DOE OSTI2017

Neutrinos from type Ia supernovae: The gravitationally confined detonation scenario

Despite their use as cosmological distance indicators and their importance in the chemical evolution of galaxies, the unequivocal identification of the progenitor systems and explosion mechanism of normal type Ia supernovae (SNe Ia) remains elusive. The leading hypothesis is that such a supernova is a thermonuclear explosion of a carbon-oxygen white dwarf, but the exact explosion mechanism is still a matter of debate. Observation of a galactic SN Ia would be of immense value in answering the many open questions related to these events. One potentially useful source of information about the explosion mechanism and progenitor is the neutrino signal because the neutrinos from the different mechanisms possess distinct spectra as a function of time and energy. Here in this paper, we compute the expected neutrino signal from a gravitationally confined detonation (GCD) explosion scenario for a SN Ia and show how the flux at Earth contains features in time and energy unique to this scenario. We then calculate the expected event rates in the Super-K, Hyper-K, JUNO, DUNE, and IceCube detectors and find both Hyper-K and IceCube will see a few events for a GCD supernova at 1 kpc or closer, while Super-K, JUNO, and DUNE will see events if the supernova is closer than ~0.3 kpc. The distance and detector criteria needed to resolve the time and spectral features arising from the explosion mechanism, neutrino production, and neutrino oscillation processes are also discussed. The neutrino signal from the GCD is then compared with the signal from a deflagration-to-detonation transition (DDT) explosion model computed previously. We find the overall event rate is the most discriminating feature between the two scenarios followed by the event rate time structure. Using the event rate in the Hyper-K detector alone, the DDT can be distinguished from the GCD at 2σ if the distance to the supernova is less than 2.3 kpc for a normal mass ordering and 3.6 kpc for an inverted ordering.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗
DOE OSTI2020

Deep Learning for Intelligent Bubble Size Detection in the Spallation Neutron Source Visual Target

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) will undergo proton power upgrade (PPU), increasing the proton beam power from 1.4 MW to 2.8 MW. From 2.8 MW, 2.0 MW will go to the current First Target Station and the rest will go to the future Second Target Station (STS). The First Target Station uses a liquid mercury target that is contained in a 316L stainless steel vessel. The proton beam is pulsed at 60 Hz, with a pulse of about 0.7μs. When the proton beam hits the target, the intense energy deposition leads to a rapid rise in temperature in the mercury. This temperature rise creates pressure waves that propagate through the mercury and cause cavitation erosion. The power upgrade will cause stronger pressure waves that will further increase damage because of cavitation. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at 1.4 MW. However, at higher power, additional mitigation is necessary. Therefore, the 2 MW target vessel will be equipped with swirl bubblers and an additional gas injection port near the nose to inject more gas in the target. To develop a gas injection strategy and design, flow visualization in water with a transparent prototypical target (“visual target”) was performed. Bubble sizes and their spatial distribution in the flow loop are crucial to understanding the effectiveness of the bubbles in mitigating pressure waves. Bubbles were generated in the visual target under varied conditions of input pressures with helium and air. Images were captured using a high-speed camera at varied frame rates at different positions away from the swirl bubbler and different depths in the flow loop under varying lighting conditions. Initially, methods such as circular Hough transforms were applied after a series of images processing to obtain a general distribution of bubble sizes. Bubbles smaller than 500 μm are preferred to effectively mitigate the effect of pressure waves, which demands an accurate bubble detection and sizing system. Intelligent detection and identification of bubble sizes alleviate misdetection and improves accuracies. Employing neural networks, intelligent detection of bubble sizes and their distribution was developed and provides a robust alternative to traditional techniques. Human intervention was employed to label in-focus and out-of-focus bubbles in the set of training images. An object detection network using a pretrained convolutional neural network was created that extracted the features from the training images. Data augmentation was used to improve network accuracy through a random transformation of the original data.

Rasheed, Fayaz↗
DOE OSTI2015

Retrospective North American CFL Experience Curve Analysis and Correlation to Deployment Programs

Retrospective experience curves are a useful tool for understanding historic technology development, and can contribute to investment program analysis and future cost estimation efforts. This work documents our development of an analysis approach for deriving retrospective experience curves with a variable learning rate, and its application to develop an experience curve for compact fluorescent lamps for the global and North American markets over the years 1990-2007. Uncertainties and assumptions involved in interpreting data for our experience curve development are discussed, including the processing and transformation of empirical data, the selection of system boundaries, and the identification of historical changes in the learning rate over the course of 15 years. In the results that follow, we find that that the learning rate has changed at least once from 1990-2007. We also explore if, and to what degree, public deployment programs may have contributed to an increased technology learning rate in North America. We observe correlations between the changes in the learning rate and the initiation of new policies, abrupt technological advances, including improvements to ballast technology, and economic and political events such as trade tariffs and electricity prices. Finally, we discuss how the findings of this work (1) support the use of segmented experience curves for retrospective and prospective analysis and (2) may imply that investments in technological research and development have contributed to a change in market adoption and penetration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Transition metal mediated transformations of small molecules

Catalysis at metal centers is of great scientific, as well as practical, importance because of the high efficiency, high specificity, and low energy demands often associated with such systems. The two major themes of our research are (a) the design of metal-based systems for the synthesis of novel classes of polymers and (b) the identification of new metal-catalyzed systems for the conversion of biomass to fuels and chemicals, and related “green” chemical processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗