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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Nanocomposites based on thermoplastic elastomers with functional basis of nano titanium dioxide

Nanocomposites based on a thermoplastic elastomer (TPE) (low-density polyethylene (LDPE) and 1,2-polybutadiene in a ratio of 60/40) with functional titanium dioxide nanoparticles of different nature, TiO{sub 2}/TPE, have been prepared and investigated by a complex of methods (X-ray diffraction analysis using X-ray and synchrotron radiation beams, scanning electron microscopy, transmission electron microscopy, and X-ray energy-dispersive spectroscopy). The morphology of the composites is found to be somewhat different, depending on the TiO{sub 2} characteristics. It is revealed that nanocomposites with cellular or porous structures containing nano-TiO{sub 2} aggregates with a large specific surface and large sizes of crystallites and nanoparticles exhibit the best deformation‒strength and fatigue properties and stability to the effect of active media under conditions of ozone and vapor‒air aging.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Effect of a por-Si Buffer Layer on the Structure and Morphology of Epitaxial In{sub x}Ga{sub 1 – x}N/Si(111) Heterostructures

Integrated heterostructures exhibiting nanocolumnar morphology of the In{sub x}Ga{sub 1 – x}N/Si(111) film are grown on a single-crystal silicon substrate (c-Si(111)) and a substrate with a nanoporous buffer sublayer (por-Si) by molecular-beam epitaxy with the plasma activation of nitrogen. Using a complex of structural and microscopic methods of analysis, it is shown that the growth of In{sub x}Ga{sub 1 – x}N nanocolumns on a nanoporous buffer layer offers a number of advantages over growth on c-Si. The por-Si substrate predetermines the preferential orientation of the growth of In{sub x}Ga{sub 1 – x}N nanocolumns closer to the Si(111) orientation direction and makes it possible to produce In{sub x}Ga{sub 1 – x}N nanocolumns with a higher degree of crystallographic uniformity and with a nanocolumn lateral size of ~40 nm unified over the entire surface. The growth of In{sub x}Ga{sub 1 – x}N nanocolumns on a por-Si layer yields a decrease in the strain components ε{sub xx} and ε{sub zz} and in the density of edge and screw dislocations compared to the corresponding parameters for In{sub x}Ga{sub 1 – x}N nanocolumns grown on c-Si. The In{sub x}Ga{sub 1 – x}N nanocolumnar layer fabricated on por-Si exhibits a 20% higher charge-carrier concentration compared to the layer grown on c-Si as well as a higher intensity of the photoluminescence quantum yield (+25%).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

plexos-coad (Plexos Class-Object-Attribute Data manipulation tool)

Clean energy systems including the future power grid are enabled via the development, analysis and application of advanced simulation of complex systems based on large datasets. The ability to handle and enable the simulation of complex systems allows researchers and analysts to address significant problems with greater confidence and greater levels of integrity. Parallel execution of complex systems is paramount to addressing increasing complexity. Programmatic interfaces for software configuration and parallel execution enable handling greater complexity and improving simulation integrity. The Class Object Attribute Data (COAD) software is written as a programmatic interface to the PLEXOS dataset that enables the decomposition of the dataset in time and execution in parallel without loss of integrity. COAD also enable the recomposition of solution datasets that are the output of distributed parallel execution of the core simulator.

Jones, Wes↗
DOE OSTI2014

Situational Awareness Geospatial Application (iSAGA)

Situational Awareness Geospatial Application (iSAGA) is a geospatial situational awareness software tool that uses an algorithm to extract location data from nearly any internet-based, or custom data source and display it geospatially; allows user-friendly conduct of spatial analysis using custom-developed tools; searches complex Geographic Information System (GIS) databases and accesses high resolution imagery. iSAGA has application at the federal, state and local levels of emergency response, consequence management, law enforcement, emergency operations and other decision makers as a tool to provide complete, visual, situational awareness using data feeds and tools selected by the individual agency or organization. Feeds may be layered and custom tools developed to uniquely suit each subscribing agency or organization. iSAGA may similarly be applied to international agencies and organizations.

Sher, Benjamin↗
DOE OSTI2019

CDR3α drives selection of the immunodominant Epstein Barr virus (EBV) BRLF1-specific CD8 T cell receptor repertoire in primary infection

The T cell receptor (TCR) repertoire is an essential component of the CD8 T-cell immune response. Here, we seek to investigate factors that drive selection of TCR repertoires specific to the HLA-A2-restricted immunodominant epitope BRLF1 109-117 (YVLDHLIVV) over the course of primary Epstein Barr virus (EBV) infection. Using single-cell paired TCRαβ sequencing of tetramer sorted CD8 T cells ex vivo , we show at the clonal level that recognition of the HLA-A2-restricted BRLF1 (YVL-BR, BRLF-1109) epitope is mainly driven by the TCRα chain. For the first time, we identify a CDR3α (complementarity determining region 3 α) motif, KDTDKL, resulting from an obligate AV8.1-AJ34 pairing that was shared by all four individuals studied. This observation coupled with the fact that this public AV8.1-KDTDKL-AJ34 TCR pairs with multiple different TCRβ chains within the same donor (median 4; range: 1–9), suggests that there are some unique structural features of the interaction between the YVL-BR/MHC and the AV8.1-KDTDKL-AJ34 TCR that leads to this high level of selection. Newly developed TCR motif algorithms identified a lysine at position 1 of the CDR3α motif that is highly conserved and likely important for antigen recognition. Crystal structure analysis of the YVL-BR/HLA-A2 complex revealed that the MHC-bound peptide bulges at position 4, exposing a negatively charged aspartic acid that may interact with the positively charged lysine of CDR3α. TCR cloning and site-directed mutagenesis of the CDR3α lysine ablated YVL-BR-tetramer staining and substantially reduced CD69 upregulation on TCR mutant-transduced cells following antigen-specific stimulation. Reduced activation of T cells expressing this CDR3 motif was also observed following exposure to mutated (D4A) peptide. In summary, we show that a highly public TCR repertoire to an immunodominant epitope of a common human virus is almost completely selected on the basis of CDR3α and provide a likely structural basis for the selection. These studies emphasize the importance of examining TCRα, as well as TCRβ, in understanding the CD8 T cell receptor repertoire.

Microbiology↗
DOE OSTI2014

Studies on the Mechanisms of Methyl Iodide Adsorption and Iodine Retention on Silver-Mordenite

Silver-containing mordenite (MOR) is a longstanding benchmark for radioiodine capture, reacting with molecular iodine (I 2 ) to form AgI. However the mechanisms for organoiodine capture are not well understood. Here we investigate the capture of methyl iodide from complex mixed gas streams by combining chemical analysis of the effluent gas stream with in depth characterization of the recovered sorbent.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

IMS Rapid Response FY21 Summary Report for: Integrating Patterned Probes with Four-Dimensional Scanning Transmission Electron Microscopy for Unrivaled Crystallographic Structure Determination in Nanomaterials

The initial goal of our 4-dimensional scanning transmission electron microscopy (4D-STEM)-based project was to develop strain resolution two orders of magnitude better than what is now currently possible with electron-based scattering techniques, all while collecting scattering information from 7 different tilt axes at one time [multi-beam electron diffraction (MBED)1 ] through the development of a new electron probe-forming aperture with non-circular features (patterned probes 2 ). We set out to accomplish this through a collaboration with Drs. Colin Ophus and Ben Savitsky at Lawrence Berkeley Laboratory (they are the world-leading experts in developing the complex computational codes required to perform orientation analysis and quantitative strain mapping on our 4D-STEM data sets. We are motivated to invest in this area as it will be the only technique sensitive enough to perform three- dimensional automated crystallographic orientation mapping (ACOM) and strain mapping for materials exposed to external stimulus (a focus of our larger efforts).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Discovering new governing equations using ML

A hallmark of the scientific process since the time of Newton has been the derivation of mathematical equations meant to capture relationships between observables. As the field of mathematical modeling evolved, practitioners specifically emphasized mathematical formulations that were predictive, generalizable, and interpretable. Machine learning’s ability to interrogate complex processes is particularly useful for the analysis of highly heterogeneous, anisotropic materials where idealized descriptions often fail. As we move into this new era, we anticipate the need to leverage machine learning to aid scientists in extracting meaningful, but yet sometimes elusive, relationships between observed quantities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Vehicle Automation Benefits and Challenges for Passenger Transport System Beyond Automated Driving

The National Renewable Energy Laboratory has been researching the implementation of fully automated passenger transport systems to be operated within dense urban settings, referred to as Automated Mobility Districts, based on roadway vehicle automation as opposed to track or train-based automation. This research, now in its third phase, is presently addressing the full spectrum of benefits and challenges of full automation of passenger transport systems with respect to fleet electrification and the associated multimodal, large fleet operational management, benefits beyond simply automating the driving tasks. The focal points summarized in the paper and presentation address the benefit-analysis of fleet automation to address added complexities imposed on the multi-fleet operational management when there is a simultaneous implementation of an on-demand service mode that connects with and optimizes the effectiveness of legacy transit systems and new sub-regional autonomous vehicle fleets, with specific emphasis on enhanced ability to better meet peak ridership demand. The research also begins to address challenges in operations arising from lack of personnel present to handle unexpected customer and system needs. Combined, this research articulates vehicle automation benefits and challenges beyond simply automating the driving tasks, addressing additional operational benefits automation provides to address the added complexities imposed by electrification and on-demand modes of operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

A Meta-Analysis of Stressors from the Total Environment Associated with Children’s General Cognitive Ability

General cognitive ability, often referred to as ‘general intelligence’, comprises a variety of correlated abilities. Childhood general cognitive ability is a well-studied area of research and can be used to predict social outcomes and perceived success. Early life stage (e.g., prenatal, postnatal, toddler) exposures to stressors (i.e., chemical and non-chemical stressors from the total (built, natural, social) environment) can impact the development of childhood cognitive ability. Building from our systematic scoping review (Ruiz et al., 2016), we conducted a meta-analysis to evaluate more than 100 stressors related to cognitive development. Our meta-analysis identified 23 stressors with a significant increase in their likelihood to influence childhood cognitive ability by 10% or more, and 80 stressors were observed to have a statistically significant effect on cognitive ability. Stressors most impactful to cognition during the prenatal period were related to maternal health and the mother’s ability to access information relevant to a healthy pregnancy (e.g., diet, lifestyle). Stressors most impactful to cognition during the early childhood period were dietary nutrients (infancy), quality of social interaction (toddler), and exposure to toxic substances (throughout early childhood). In conducting this analysis, we examined the relative impact of real-world exposures on cognitive development to attempt to understand the inter-relationships between exposures to both chemical and non-chemical stressors and early developmental life stages. Our findings suggest that the stressors observed to be the most influential to childhood cognitive ability are not permanent and can be broadly categorized as activities/behaviors which can be modified to improve childhood cognition. This meta-analysis supports the idea that there are complex relationships between a child’s total environment and early cognitive development.

Nilsen, Frances M. (ORCID:0000000241936951)↗
DOE OSTI2023

Editorial for Special Issue “Advances in Soil Microbiome”

The soil microbiome (the community of all soil microorganisms and their surrounding environment) is a critical part of our ecological network. These microbial communities carry out several important functions, including the promotion of plant growth and the cycling of carbon and nitrogen sources. Despite the importance of this community, and the recent advances in multi-omic analysis and other methods designed to query complex systems, there is a still a large knowledge gap regarding the soil microbiome, its response to changing climates and moisture levels, the specific molecular mechanisms that drive its role in the environment, and how these questions can be answered through the use of model systems. Here, we present eleven new papers that seek to answer these questions and expand our knowledge of the soil microbiome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Metagenomic Insight into Environmentally Challenged Methane-Fed Microbial Communities

In this study, we aimed to investigate, through high-resolution metagenomics and metatranscriptomics, the composition and the trajectories of microbial communities originating from a natural sample, fed exclusively with methane, over 14 weeks of laboratory incubation. This study builds on our prior data, suggesting that multiple functional guilds feed on methane, likely through guild-to-guild carbon transfer, and potentially through intraguild and intraspecies interactions. We observed that, under two simulated dioxygen partial pressures—low versus high—community trajectories were different, with considerable variability among the replicates. In all microcosms, four major functional guilds were prominently present, representing Methylococcaceae (the true methanotrophs), Methylophilaceae (the nonmethanotrophic methylotrophs), Burkholderiales, and Bacteroidetes. Additional functional guilds were detected in multiple samples, such as members of Opitutae, as well as the predatory species, suggesting additional complexity for methane-oxidizing communities. Metatranscriptomic analysis suggested simultaneous expression of the two alternative types of methanol dehydrogenases in both Methylococcaceae and Methylophilaceae, while high expression of the oxidative/nitrosative stress response genes suggested competition for dioxygen among the community members. The transcriptomic analysis further suggested that Burkholderiales likely feed on acetate that is produced by Methylococcaceae under hypoxic conditions, while Bacteroidetes likely feed on biopolymers produced by both Methylococcaceae and Methylophilaceae.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

ROTATIONAL DYNAMICS AND STAR FORMATION IN THE NEARBY DWARF GALAXY NGC 5238

We present new H i spectral-line images of the nearby low-mass galaxy NGC 5238, acquired with the Karl G. Jansky Very Large Array. Located at a distance of 4.51 ± 0.04 Mpc, NGC 5238 is an actively star-forming galaxy with widespread H α and ultraviolet (UV) continuum emission. The source is included in many ongoing and recent nearby galaxy surveys, but until this work the spatially resolved qualities of its neutral interstellar medium have remained unstudied. Our H i images resolve the disk on physical scales of ∼400 pc, allowing us to undertake a detailed comparative study of the gaseous and stellar components. The H i disk is asymmetric in the outer regions, and the areas of high H i mass surface density display a crescent-shaped morphology that is slightly offset from the center of the stellar populations. The H i column density exceeds 10{sup 21} cm{sup −2} in much of the disk. We quantify the degree of co-spatiality of dense H i gas and sites of ongoing star formation as traced by far-UV and H α emission. The neutral gas kinematics are complex; using a spatially resolved position–velocity analysis, we infer a rotational velocity of 31 ± 5 km s{sup −1}. We place NGC 5238 on the baryonic Tully–Fisher relation and contextualize the system among other low-mass galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Dynamical estimate of post-main-sequence stellar masses in 47 Tucanae

We use the effects of mass segregation on the radial distribution of different stellar populations in the core of 47 Tucanae to find estimates for the masses of stars at different post-main-sequence evolutionary stages. We take samples of main-sequence (MS) stars from the core of 47 Tucanae, at different magnitudes (i.e., different masses), and use the effects of this dynamical process to develop a relation between the radial distance (RD) at which the cumulative distribution reaches the 20th and 50th percentile and stellar mass. From these relations we estimate the masses of different post-MS populations. We find that mass remains constant for stars going through the evolutionary stages from the upper MS up to the horizontal branch (HB). By comparing RDs of the HB stars with stars of lower masses, we can exclude a mass loss greater than 0.09 M{sub ⊙} during the red giant branch (RGB) stage at nearly the 3σ level. The slightly higher mass estimates for the asymptotic giant branch (AGB) are consistent with the AGB having evolved from somewhat more massive stars. The AGB also exhibits evidence of contamination by more massive stars, possibly blue straggler stars (BSSs), going through the RGB phase. We do not include the BSSs in this paper due to the complexity of these objects; instead, the complete analysis of this population is left for a companion paper. The process to estimate the masses described in this paper is exclusive to the core of 47 Tuc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Ideas and perspectives: Enhancing research and monitoring of carbon pools and land-to-atmosphere greenhouse gases exchange in developing countries

Carbon (C) and greenhouse gas (GHG) research has traditionally required data collection and analysis using advanced and often expensive instruments, complex and proprietary software, and highly specialized research technicians. Partly as a result, relatively little C and GHG research has been conducted in resource-constrained developing countries. At the same time, these are often the same countries and regions in which climate change impacts will likely be strongest and in which major science uncertainties are centered, given the importance of dryland and tropical systems to the global C cycle. Increasingly, scientific communities have adopted appropriate technology and approach (AT&A) for C and GHG research, which focuses on low-cost and low-technology instruments, open-source software and data, and participatory and networking-based research approaches. Adopting AT&A can mean acquiring data with fewer technical constraints and lower economic burden and is thus a strategy for enhancing C and GHG research in developing countries. However, AT&A can have higher uncertainties; these can often be mitigated by carefully designing experiments, providing clear protocols for data collection, and monitoring and validating the quality of obtained data. For implementing this approach in developing countries, it is first necessary to recognize the scientific and moral importance of AT&A. At the same time, new AT&A techniques should be identified and further developed. All these processes should be promoted in collaboration with local researchers and through training local staff and encouraged for wide use and further innovation in developing countries.

Kim, Dong-Gill↗
DOE OSTI2017

RISK MONITORING CAPABILITES FROM DYNAMIC PRA DATA

Risk Monitors (RMs) are software tools that are employed in nuclear power plants to monitor the safety properties of the plant itself during operation. These safety properties are generated using classical PRA methods which are typically based on static Boolean structures such as Fault-Trees (FTs) and Event-Trees (ETs). The outcomes of these methods are Core Damage Frequency (CDF) and risk importance values of plant components (e.g., valves and pumps). Examples of Risk Importance Measures (RIMs) commonly used are: Risk Achievement Worth (RAW), Risk Reduction Worth (RRW), Birnbaum (B) and Fussell-Vesely (FV) [1,2]. In a RM these values are updated in real time once plant components status is updated (e.g., under maintenance, off-line, testing). In addition, RMs can be used to optimize maintenance schedules so those safety features of the plant are below plant standard values. This paper presents an approach to create temporal profiles of RIMs (i.e., RM capabilities) given a temporal change of the plant configuration. Instead of employing data generated by ETs and FTs, the proposed approach will employ data generated by Dynamic PRA methods. In contrast to classical PRA methods, Dynamic PRA methods couple stochastic methods with safety analysis codes to determine risk associate to complex systems such as nuclear plants. Compared to classical PRA methods, they can evaluate with higher resolution the safety impact of timing and sequencing of events on the accident progression. We will describe how classical RIMs can be generated by Dynamic PRA data and then we will show how the temporal profiles of the RIMs can be generated given a temporal profile of the system configuration.

97 MATHEMATICS AND COMPUTING↗