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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Accommodation of Silicon in an Interconnected Copper Network for Robust Li-Ion Storage

Silicon (Si)-based materials are one of the most promising anodes to be applied in rechargeable lithium ion batteries. However, the active Si/electrolyte interface causes continuous side reactions and poor conductivity, which significantly decreases the cycling stability. Cu is the only metallic current collector that has been known to promote electron conduction and lithium-ion transfer without alloying reaction occurrence. However, to the best current knowledge, scalable interface engineering incorporating Cu has not been reported. In this study, this conductive Cu interface (CCI) is constructed through a self-assembly carbothermic reduction method to achieve efficient protection of Si/electrolyte interfaces while allowing for fast Li + diffusion. The energy barrier of lithium-ion diffusion through Cu is calculated to be 0.1965 eV, which is much lower than that through Au, Fe, and Ni films. Benefiting from the enhanced interfacial protection and kinetics of Si with CCI, a fading rate of only 0.068% is maintained for 1000 cycles and an aerial capacity of 4.78 mAh cm -2 is achieved after 280 cycles, which is comparable to the industry standards required for practical application.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Numerical simulation of flow and mixing in fracture intersections

Fluid transport through fractured geological formations is strongly influenced by the redistribution of solutes at fracture intersections. In this study, we perform detailed numerical simulations of flow and scalar transport within the intersection of two smooth, planar fractures. The analysis focuses on the mixing ratio, the proportion of solute flux exiting along the outlet branch aligned with the primary inlet flow direction, relative to the total solute flux at the outlets. We systematically investigate how the mixing ratio varies with four key parameters: Peclet number, Reynolds number, flow rate ratio between outlet branches, and fracture intersection angle. Results show that the mixing ratio decreases with increasing Peclet number and outlet flow rate ratio, consistent with reduced diffusive spreading and enhanced streamline routing. While low Reynolds numbers have minimal impact, inertial effects at higher Reynolds numbers significantly increase the mixing ratio. Additionally, acute and obtuse intersection angles alter flow partitioning and modify the solute distribution at the outlets. These findings provide a quantitative basis for incorporating physically realistic mixing behavior—intermediate between complete mixing and streamline-following assumptions—into network-scale transport models. The results have direct relevance to subsurface energy systems, including geothermal energy production, carbon sequestration, and contaminant remediation.

58 GEOSCIENCES↗
DOE OSTI2022

PurpleAir Sensors as Effective Indicators of PM Exposure in Urban Areas

Particulate matter that is 2.5 microns or less in diameter (PM 2.5 ) is a biproduct of combustion reactions used for energy production. Populations that are exposed to consistently high levels of aerosolized PM 2.5 face serious health risks. This project compared low-cost PM 2.5 sensors with federally recognized methods to look for a cost-effective way to expand the air quality monitor network. Within metropolitan areas that face inconsistent spatial distribution of PM 2.5 , there may not be the necessary network density to indicate neighborhood-levels of PM 2.5 . This project aimed to examine the sensitivity of low-cost PM 2.5 sensor measurements on a neighborhood scale (< 4 km diameter) in an urban area to prevent citizens from being exposed to unsafe levels of PM 2.5 without their knowledge. Using publicly available sensor data from Livermore, CA and Bakersfield, CA, it was determined, based on the revealed patterns, that the analyzed low-cost sensors were able to display representative PM 2.5 levels for neighborhoodscale areas exposed to pollution from PM 2.5 sources.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2019

Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees

Organic solar cells are an inexpensive, flexible alternative to traditional silicon-based solar cells but disadvantaged by low power conversion efficiency due to empirical design and complex manufacturing processes. This process can be accelerated by generating a comprehensive set of potential candidates. However, this would require a laborious trial and error method of modeling all possible polymer configurations. A machine learning model has the potential to accelerate the process of screening potential donor candidates by associating structural features of the compound using molecular fingerprints with their highest occupied molecular orbital energies. In this paper, extremely randomized tree learning models are employed for the prediction of HOMO values for donor compounds, and a web application is developed. Finally, the proposed models outperform neural networks trained on molecular fingerprints as well as SMILES, as well as other state-of-the-art architectures such as Chemception and Molecular Graph Convolution on two datasets of varying sizes.

42 ENGINEERING↗
DOE OSTI2013

Thermal stability and lifetime estimates of a high temperature epoxy by T g reduction

Thermal degradation of a high temperature epoxy network is studied in terms glass transition temperature (T g ) reduction over a temperature window encompassing the T g of the network. The T g is shown to decrease as the network is thermally aged at elevated temperatures in air and in argon. The duration of the aging experiments is extended to long time such that the absolute T g reduction approaches a long time reduction plateau. Degradation is dominated by non-oxidative pyrolysis with a small contribution from diffusion limited thermal oxidative degradation at the surface. A time–temperature superposition is constructed from the extent of T g reduction of samples aged in air and the thermal shift factors are shown to have Arrhenius scaling behavior. An activation energy is extracted that agrees with previous activation energy measurements derived from other property measurements of the same network aged under similar conditions. The agreement of the activation energy with past results shows that T g reduction is controlled by the same degradation mechanism and may be used as an observable for lifetime estimates when thermal degradation is pyrolytic in nature. The extent of T g reduction is modeled with an autocatalytic rate expression and compared to previous property measurements to show the difference in sensitivity of observable material properties on degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep learning approaches for instantaneous laser absorptance prediction in additive manufacturing

Abstract The quantification of absorbed light is essential for understanding laser-material interactions and melt pool dynamics in order to minimize defects in additively manufactured metal components. The geometry of a vapor depression formed during laser melting is closely related to laser energy absorption. This relationship has been observed by the state-of-the-art in situ high-speed synchrotron X-ray visualization and integrating sphere radiometry. These two techniques create a temporally resolved dataset consisting of vapor depression images and corresponding laser absorptance. In this work, we propose two different approaches to predict instantaneous laser absorptance. The end-to-end approach uses deep convolutional neural networks to learn implicit features of X-ray images automatically and predict the laser energy absorptance. The two-stage approach uses a semantic segmentation model to engineer geometric features and predict absorptance using classical regression models. While having distinct advantages, both approaches achieved a consistently low mean absolute error of less than 3.3%.

Chemistry↗
NASA NTRS1991

Solar-A Prelaunch Mission Operation Report (MOR)

The Solar-A mission is a Japanese-led program with the participation of the United States and the United Kingdom. The Japanese Institute of Space and Astronautical Science (ISAS) is providing the Solar-A spacecraft, two of the four science instruments, the launch vehicle and launch support, and the principal ground station with Operational Control Center. NASA is providing a science instrument, the Soft X-ray Telescope (SXT)and tracking support using the Deep Space Network (DSN) ground stations. The United Kingdom s Science and Engineering Research Council (SERC) provides the Bragg Crystal Spectrometer. The Solar-A mission will study solar flares using a cluster of instruments on a satellite in a 600 km altitude, 31 degree inclination circular orbit. The emphasis of the mission is on imaging and spectroscopy of hard and soft X-rays. The principal instruments are a pair of X-ray imaging instruments, one for the hard X-ray range and one for the soft X-ray range. The Hard X-Ray Telescope (HXT), provided by ISAS, operates in the energy range of 10-100 keV and uses an array of modulation collimators to record Fourier transform images of the non-thermal and hot plasmas that are formed during the early phases of a flare. These images are thought to be intimately associated with the sites of primary energy release. The Soft X-Ray Telescope (SXT), jointly provided by NASA and ISAS, operates in the wavelength range of 3-50 Angstroms and uses a grazing incidence mirror to form direct images of the lower temperature (but still very hot) plasmas that form as the solar atmosphere responds to the injection of energy. The SXT instrument is a joint development effort between the Lockheed Palo Alto Research Laboratory and the National Astronomical Observatory of Japan. The U.S. effort also involves Stanford University, the University of California at Berkeley and the University of Hawaii, who provide support in the areas of theory, data analysis and interpretation, and ground-based observations. The hard and soft X-ray telescopes both have an alignment sensor, operating in the visual region of the spectrum, to provide co-alignment information.

Source record↗
DOE OSTI2024

Improving the Discovery Power of the Dark Energy Survey Gravitational Wave Program in Its Fourth Observing Campaign

The universe is growing at an accelerated rate, yet the several experiments and science cases used to measure this property yield conflicting results for the rate. Known colloquially as the Hubble tension problem, this discrepancy calls for a novel measurement of the Hubble Constant, H0, in order to be resolved. The Dark Energy Survey Gravitational Wave group (DESGW) aims to relieve this tension by performing a standard sirens measurement of H0 independent of traditional methods. To do so in the current era of data abundance and limited telescope time demands a robust, optimized, and complete science software pipeline to carefully balance the transition from gravitational wave data to state-of-the-art images, images to photometric data, and photometric data to identification and analysis of electromagnetic counterparts to gravitational wave signals. This thesis details the contributions of the author to the DESGW Search & Discovery Pipeline for the fourth observing run of the LIGO/Virgo/KAGRA gravitational wave detector network, as well as the science applications that have been pursued.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

DeepShadows: Separating low surface brightness galaxies from artifacts using deep learning

Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, Galactic cirrus, star-forming regions in the arms of spiral galaxies, etc.) that have to be rejected through time consuming visual inspection. In future surveys, which are expected to collect hundreds of petabytes of data and detect billions of objects, such an approach will not be feasible. We investigate the use of convolutional neural networks (CNNs) for the problem of separating LSBGs from artifacts in survey images. We take advantage of the fact that we have available a large number of labeled LSBGs and artifacts from the Dark Energy Survey, that we use to train, validate, and test a CNN model. That model, which we call DeepShadows , achieves a test accuracy of 92.0%, a significant improvement relative to feature-based machine learning models. We also study the ability to use transfer learning to adapt this model to classify objects from the deeper Hyper-Suprime-Cam survey, and we show that after the model is retrained on a very small sample from the new survey, it can reach an accuracy of 87.6%. Finally, these results demonstrate that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.

42 ENGINEERING↗
DOE OSTI2021

Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2

The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 has continued to evolve with the emergence of several novel variants, and amino acid changes in the RBD have been implicated with increased fitness and potential for immune evasion. Reliably predicting the effect of amino acid changes on the ability of the RBD to interact more strongly with the hACE2 can help assess the implications for public health and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born solvation, hydrogen bonding, π-π packing, and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity changes using the decomposed energy terms as descriptors. The computational base achieves a validation accuracy of 82.8% for classifying single–amino acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient of 0.73 between predicted and experimentally calculated changes in binding affinities. Both metrics are calculated using a fivefold cross-validation test. Our method thus sets up a framework for screening binding affinity changes caused by unknown single– and multiple–amino acid changes offering a valuable tool to predict host adaptation of SARS-CoV-2 variants toward tighter hACE2 binding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Mass extinction efficiency and extinction hygroscopicity of ambient PM{sub 2.5} in urban China

The ambient PM{sub 2.5} pollution problem in China has drawn substantial international attentions. The mass extinction efficiency (MEE) and hygroscopicity factor (f(RH)) of PM{sub 2.5} can be readily applied to study the impacts on atmospheric visibility and climate. The few previous investigations in China only reported results from pilot studies and are lack of spatial representativeness. In this study, hourly average ambient PM{sub 2.5} mass concentration, relative humidity, and atmospheric visibility data from China national air quality and meteorological monitoring networks were retrieved and analyzed. It includes 24 major Chinese cities from nine city-clusters with the period of October 2013 to September 2014. Annual average extinction coefficient in urban China was 759.3±258.3 Mm{sup −1}, mainly caused by dry PM{sub 2.5} (305.8.2±131.0 Mm{sup −1}) and its hygroscopicity (414.6±188.1 Mm{sup −1}). High extinction coefficient values were resulted from both high ambient PM{sub 2.5} concentration (68.5±21.7 µg/m{sup 3}) and high relative humidity (69.7±8.6%). The PM{sub 2.5} mass extinction efficiency varied from 2.87 to 6.64 m{sup 2}/g with an average of 4.40±0.84 m{sup 2}/g. The average extinction hygroscopic factor f(RH=80%) was 2.63±0.45. The levels of PM{sub 2.5} mass extinction efficiency and hygroscopic factor in China were in comparable range with those found in developed countries in spite of the significant diversities among all 24 cities. Our findings help to establish quantitative relationship between ambient extinction coefficient (visual range) and PM{sub 2.5} & relative humidity. It will reduce the uncertainty of extinction coefficient estimation of ambient PM{sub 2.5} in urban China which is essential for the research of haze pollution and climate radiative forcing. - Highlights: • Data from air quality and meteorological monitoring networks were combined. • MEE and hygroscopicity of PM{sub 2.5} in China was similar to that of developed countries. • High extinction coefficient was dominated by both high PM{sub 2.5} and relative humidity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Ultrafast dynamics of liquid water: Energy relaxation and transfer processes of the OH stretch and the HOH bend

The vibrational energy relaxation and transfer processes of the OH stretching and the HOH bending vibrations in liquid water are investigated via the theoretical calculation of the pump-probe spectra obtained from non-equilibrium molecular dynamics simulations with the TTM3-F interaction potential. The excitation of the OH stretch induces an instantaneous response of the high frequency librational motions in the 600-1000 cm-1 range. In addition, the excess energy of the OH stretch of a water molecule quickly transfers to the OH stretches of molecules in its first hydration shell with a time constant of ~50 fs, followed by relaxation to the HOH bends of the surrounding molecules with a time constant of 230 fs. The excitation of the HOH bend also results in the ultrafast excitation of the high frequency librational motions. The energy of the excited HOH bend of a water molecule decays, with a time constant of 200 fs, mainly to the relaxation of the HOH bends of its surrounding molecules. The energies of the HOH bends were found to transfer quickly to the intermolecular motions via the coupling with the high frequency librational motions. The excess energy of the OH stretch or the HOH bend relaxes to the high frequency intermolecular librational motions and eventually to the hot ground state with a time scale of ~1 ps via the coupling with the librational and translational motions. The energy relaxation and transfer processes were found to depend on the local hydrogen bonding network; the relaxations of the excess energy of the OH stretch and the HOH bend of four- and five-coordinated molecules are faster than those of a three-coordinated molecule due to the delocalization of the vibrational motions of the former (four- and five-coordinated molecules) compared to those of the later (three-coordinated molecules). The present results highlight the importance of the high frequency intermolecular librational modes in facilitating the ultrafast energy relaxation process in liquid water via their strong nonlinear couplings with the intramolecular OH stretching and HOH bending vibrations. S.S.X. acknowledges the support of the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences. Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory operated for DOE by Battelle. The calculation was carried out using the computing resources at the Research Center for Computational Science in Okazaki, Japan.

Imoto, Sho↗
DOE OSTI2013

Infrastructure, Components and System Level Testing and Analysis of Electric Vehicles (CRADA Final Report)

Battery technology is critical for the development of innovative electric vehicle networks, which can enhance transportation sustainability and reduce dependence on petroleum. This cooperative research proposed by Better Place and NREL will focus on predicting the life-cycle economics of batteries, characterizing battery technologies under various operating and usage conditions, and designing optimal usage profiles for battery recharging and use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Automated Clean Chemistry for Bulk Analysis of Environmental Swipe Samples - FY17 Year End Report

Sample preparation methods for mass spectrometry are being automated using commercial-off-the-shelf (COTS) equipment to shorten lengthy and costly manual chemical purification procedures. This development addresses a serious need in the International Atomic Energy Agency’s Network of Analytical Laboratories (IAEA NWAL) to increase efficiency in the Bulk Analysis of Environmental Samples for Safeguards program with a method that allows unattended, overnight operation. In collaboration with Elemental Scientific Inc., the prepFAST-MC2 was designed based on COTS equipment. It was modified for uranium/plutonium separations using renewable columns packed with Eichrom TEVA and UTEVA resins, with a chemical separation method based on the Oak Ridge National Laboratory (ORNL) NWAL chemical procedure. The newly designed prepFAST-SR has had several upgrades compared with the original prepFAST-MC2. Both systems are currently installed in the Ultra-Trace Forensics Science Center at ORNL.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Decoding Zeolite Crystallization and Stage III in Nuclear Waste Glasses by Coupled Modeling and Experiments

Under specific conditions of pH and temperature, nuclear waste immobilization borosilicate glasses may exhibit a sudden acceleration in their corrosion kinetics (stage III)—a behavior that has been associated with the formation of zeolite crystals. Such accelerated dissolution may compromise the integrity of nuclear wasteforms placed in geological depositories. However, thus far, none of the available models is able to predict the thermodynamic propensity and kinetics of zeolite precipitation as a function of the solution conditions due to (i) a lack of fundamental knowledge regarding the nucleation & growth mechanisms of zeolitic phases, (ii) uncertainty regarding the compositions (types) of zeolites that may form and the rate-limiting step in their precipitation as a function of the solution conditions, and (iii) the complexities that arise due to the vast parametric space (i.e., solution chemistry, temperature, number of secondary phases, etc.) that encompass these systems under conditions of environmental exposure. To resolve these challenges, this project aimed to unambiguously identify the thermodynamic propensity for zeolite precipitation and the kinetics thereof as a function of the solution conditions (composition, pH, and temperature). To achieve this goal: 1) We identified the solution conditions and zeolite phases relevant to nuclear glass dissolution. 2) We performed a series of ab initio molecular dynamics (AIMD) simulations to compute the thermodynamic properties of a group of characteristic zeolites that features a large range of compositions, various hydration levels, a wide range of framework structures, and partial atomic site occupancies. 3) We released a first-of-a-kind self-consistent thermodynamic database that can be used to assess the kinetics and the stability fields of zeolitic phases within a Gibbs energy minimization (GEM) framework. 4) We developed a robust geochemical modeling method allowing us to predict the stability of secondary phases (including zeolites, calcium–silicate–hydrate gels, and clays) upon the dissolution of nuclear waste immobilization glasses. 5) We introduced a model that predicts the dissolution kinetics of a series of borosilicate nuclear waste immobilization glasses in terms of the topology of their atomic network. 6) We investigated the roles of the solution composition on the crystallization kinetics of phillipsite zeolites and tobermorite silicate hydrates. Via PNNL’s collaboration and engagement, this project directly supports DOE’s nuclear waste immobilization activities by offering a technical, science-based foundation that will (i) facilitate predictions of the long-term corrosion rates and extents of existing nuclear waste immobilization glasses to help ensure safe and successful vitrification operations, (ii) inform the development of advanced glass formulations with enhanced durability, and, (iii) enable cost-savings that result from making more decisive and hence less conservative predictions while offering higher levels of nuclear waste embedment in smaller, more compact glass volumes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Warming alters energetic structure and function but not resilience of soil food webs

Climate warming is predicted to alter the structure, stability, and functioning of food webs. Yet, despite the importance of soil food webs for energy and nutrient turnover in terrestrial ecosystems, the effects of warming on these food webs—particularly in combination with other global change drivers—are largely unknown. Here, we present results from two complementary field experiments that test the interactive effects of warming with forest canopy disturbance and drought on energy flux in boreal–temperate ecotonal forest soil food webs. The first experiment applied a simultaneous above- and belowground warming treatment (ambient, +1.7 °C, +3.4 °C) to closed-canopy and recently clear-cut forest, simulating common forest disturbance6. The second experiment crossed warming with a summer drought treatment (-40% rainfall) in the clear-cut habitats. We show that warming reduces energy flux to microbes, while forest canopy disturbance and drought facilitates warming-induced increases in energy flux to higher trophic levels and exacerbates the reduction in energy flux to microbes, respectively. Contrary to expectations, we find no change in whole-network resilience to perturbations, but significant losses in ecosystem functioning. Warming thus interacts with forest disturbance and drought, shaping the energetic structure of soil food webs and threatening the provisioning of multiple ecosystem functions in boreal–temperate ecotonal forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

High Throughput WAN Data Transfer with Hadoop-Based Storage

Hadoop distributed file system (HDFS) is becoming more popular in recent years as a key building block of integrated grid storage solution in the field of scientific computing. Wide Area Network (WAN) data transfer is one of the important data operations for large high energy physics experiments to manage, share and process datasets of PetaBytes scale in a highly distributed grid computing environment. In this paper, we present the experience of high throughput WAN data transfer with HDFS-based Storage Element. Two protocols, GridFTP and fast data transfer (FDT), are used to characterize the network performance of WAN data transfer.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗