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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

On the nature of structural disorder in calcium silicate hydrates with a calcium/silicon ratio similar to tobermorite

Four calcium silicate hydrates (C-S-H) with structural calcium/silicon (Ca/Si) ratios ranging from 0.82 ± 0.02 to 0.87 ± 0.02 were synthesized at room temperature, 50, 80, and 110 °C. Their structure was elucidated by collating information from electron probe micro-analysis, transmission electron microscopy, extended X-ray absorption fine structure spectroscopy, and powder X-ray diffraction (XRD). A modeling approach specific to defective minerals was used because sample turbostratism prevented analysis using usual XRD refinement techniques (e.g. Rietveld analysis). It is shown that C-S-H with Ca/Si ratio of ∼ 0.8 are structurally similar to nano-crystalline turbostratic tobermorite, a naturally occurring mineral. Their structure thus consists of sheets of calcium atoms in 7-fold coordination, covered by ribbons of silicon tetrahedra with a dreierketten (wollastonite-like) organization. In these silicate ribbons, 0.42 Si per bridging tetrahedron are missing. Random stacking faults occur systematically between successive layers (turbostratic stacking). Layer-to-layer distance is equal to 11.34 Å. Crystallites have a mean size of 10 nm in the a–b plane, and a mean number of 2.6–2.9 layers stacked coherently along the c* axis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Observation of formation and local structures of metal-organic layers via complementary electron microscopy techniques

Abstract Metal-organic layers (MOLs) are highly attractive for application in catalysis, separation, sensing and biomedicine, owing to their tunable framework structure. However, it is challenging to obtain comprehensive information about the formation and local structures of MOLs using standard electron microscopy methods due to serious damage under electron beam irradiation. Here, we investigate the growth processes and local structures of MOLs utilizing a combination of liquid-phase transmission electron microscopy, cryogenic electron microscopy and electron ptychography. Our results show a multistep formation process, where precursor clusters first form in solution, then they are complexed with ligands to form non-crystalline solids, followed by the arrangement of the cluster-ligand complex into crystalline sheets, with additional possible growth by the addition of clusters to surface edges. Moreover, high-resolution imaging allows us to identify missing clusters, dislocations, loop and flat surface terminations and ligand connectors in the MOLs. Our observations provide insights into controllable MOL crystal morphology, defect engineering, and surface modification, thus assisting novel MOL design and synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Improving tide-estuary representation in MPAS-Ocean (Final Report)

The overarching goal of this research is to produce a skill-assessed tide-estuary representation, including BGC (biogeochemistry) components of the ecosystem, into the MPAS-O global climate modeling system. Specifically, in the original proposal we proposed one-way nesting, into global MPAS-O, of a high-resolution model for TES (tidal and estuary systems) including the coupling of the BGC components. We have completed the proposed tasks during the past 4 years. Specifically, after extensive consultation with the MPAS-O group during the first year, we have decided to go beyond the original proposal and to two-way nest SCHISM into MPAS-O. As a result, SCHISM-CoSiNE (with CoSiNE being the BGC model) is now fully integrated into MPAS-O as a subroutine (more precisely, an analysis member) and can be invoked as MPAS-O is running with two-way exchange of information between the TES and global models (with proper spatial interpolation done on the fly). As originally proposed, we have also developed the new finite-volume (FV) model MPAS-OI (aka ELCIRC-sub) for TES. We have conducted several tests using the coupled MPAS-SCHISM- CoSiNE model and also MPAS-OI for Columbia River, San Francisco Bay and Chesapeake Bay. Four journal publications have been produced so far as the result of the project and 1 is under review (cf. Publication section). With these achievements, we will be ready to conduct fully coupled simulations of TES and global ocean anywhere, as soon as some missing capabilities in MPAS-O (e.g. tides, improved ability to handle the boundary conditions, and streamlining of the global BGC model) are worked out. The significance of the outcome of this project cannot be overstated. It marks the first time, as far as we know, that a global climate model is tightly coupled to a bona fide nearshore and terrestrial model (SCHISM system); in fact SCHISM is the first bona fide compound flooding model that seamlessly covers the coastal ocean, estuaries, rivers and watersheds (Ye et al. 2020; Zhang et al. 2020). The coupled model will reduce the errors and uncertainties of the MPAS-O global climate model system. It also complements and extends on-going E3SM project, as the latter seeks in the long-term to include components required to simulate impacts of sea-level change and storm surge on coastal regions, including wave models and focusing resolution in coastal and storm-track regions, as an important component of the Cryosphere-ocean system. Note that a spectral wave model (the unstructured-grid version of Wave Watch III, aka Wind Wave Model) is already incorporated inside the SCHISM system and is therefore directly accessible in the coupled system.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Analyzing Natural Language Context in Human-Machine Teaming using Supervised Machine Learning

Building a foundation for trustworthiness and trust verification in multi-asset teaming is the research challenge of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Design Reference Mission (DRM) for ATTRACTOR is a search and rescue mission objective governed by a multi-member team consisting of human and machine operators. A crucial component to the effort is the communication between humans and autonomous agents throughout both planning and execution stages of the mission. Intuitive communication methods and modalities are posited as critical enablers for certifying trust and trustworthiness. This paper reports on the data collection and analysis conducted in support of the Human Informed Natural-language GANs Evaluation (HINGE)project to attain explainable and trusted communication between human-machine assets. Two identically curated image description datasets were acquired for HINGE, both consisting of two unique input modalities (typed vs. verbal) and retrieved in two distinct contexts (general vs. specific). The gathered datasets were assessed and compared using Parts-of-Speech (POS)features, sentence similarity metrics, and linguistic analysis. Then, the datasets were modeled and tested separately and in combination with one another using machine learning algorithms. The comparison and testing results reveal a superior dataset, by which a preferred context and input is understood, for generating image representations of missing persons using a Generative Adversarial Network (GAN).

Bryan A Barrows↗
DOE OSTI2017

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO 2 Reduction

Bimetallic catalysts are promising for the most difficult thermal and electrochemical reactions, but modeling the many diverse active sites on polycrystalline samples is an open challenge. Here, we present a general framework for addressing this complexity in a systematic and predictive fashion. Active sites for every stable low-index facet of a bimetallic crystal are enumerated and cataloged, yielding hundreds of possible active sites. The activity of these sites is explored in parallel using a neural-network-based surrogate model to share information between the many density functional theory (DFT) relaxations, resulting in activity estimates with an order of magnitude fewer explicit DFT calculations. Sites with interesting activity were found and provide targets for follow-up calculations. This process was applied to the electrochemical reduction of CO 2 on nickel gallium bimetallics and indicated that most facets had similar activity to Ni surfaces, but a few exposed Ni sites with a very favorable on-top CO configuration. This motif emerged naturally from the predictive modeling and represents a class of intermetallic CO 2 reduction catalysts. These sites rationalize recent experimental reports of nickel gallium activity and why previous materials screens missed this exciting material. Most importantly these methods suggest that bimetallic catalysts will be discovered by studying facet reactivity and diversity of active sites more systematically.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

WARM AND FUZZY: TEMPERATURE AND DENSITY ANALYSIS OF AN Fe XV EUV IMAGING SPECTROMETER LOOP

The Hinode EUV Imaging Spectrometer (EIS) and X-Ray Telescope (XRT) were designed in part to work together. They have the same spatial resolution and cover different but overlapping coronal temperature ranges. These properties make a combined data set ideal for multithermal analysis, where EIS provides the best information on the cooler corona (log T < 6.5) and XRT provides the best information on the hotter corona (log T > 6.5). Here, we analyze a warm non-flaring loop detected in images made in a strong EIS Fe XV emission line with a wavelength of 284.16 A and peak formation temperature of log T = 6.3. We perform differential emission measure (DEM) analysis in three pixels at different heights above the footpoint and find multithermal results with the bulk of the emission measure in the range 6.0 < log T < 6.6. Analysis with the EIS lines alone gave a DEM with huge amounts of emission measure at very high temperatures (log T >7.2); analysis with XRT data alone resulted in a DEM that was missing most of the cooler emission measure required to produce many of the EIS lines. Thus, both results were misleading and unphysical. It was only by combining the EIS and XRT data that we were able to produce a reasonable result, one without ad hoc assumptions on the shape and range of the DEM itself.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support

Purpose: Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi-institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs. Methods: Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open-source rapid learning systems were installed, for data extraction and mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway. Results: For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. For example, the PM-predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two-year overall survival was 65% in curatively and 18% in palliatively treated patients. Conclusion: The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions, improved treatment quality and potential practice changes. The early indications from the distributed learning and radiomics pilots strengthen this. Improved routine patient data quality should strengthen such rapid learning systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Spatial Locality-Aware Cache Partitioning for Effective Cache Sharing, In: 2015 44th International Conference on Parallel Processing

In modern multi-core processors, last-level caches (LLCs) are typically shared among multiple cores. Previous works have shown that such sharing is beneficial as different workloads have different needs for cache capacity, and logical partitioning of capacity can improve system performance. However, what is missing in previous works on partitioning shared LLCs is that the heterogeneity in spatial locality among workloads has not been explored. In other words, all the cores use the same block/line size in shared LLCs. In this work, we highlight that exploiting spatial locality enables much more effective cache sharing. The fundamental reason is that for many memory intensive workloads, their cache capacity requirements can be drastically reduced when a large block size is employed, therefore they can effectively donate more capacity to other workloads. To leverage spatial locality for cache partitioning effectively, we first propose a simple yet effective mechanism to measure both spatial and temporal locality at run-time. The locality information is then used to determine both the proper block size and the capacity assigned to each workload. Our experiments show that our Spatial Locality-aware Cache Partitioning (SLCP) significantly outperforms the previous works. We also present several case studies that dissect the effectiveness of SLCP compared to the existing approaches.

Computer Science↗
DOE OSTI2011

Experimental annotation of post-translational features and translated coding regions in the pathogen Salmonella Typhimurium

Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. For example systems biology-oriented genome scale modeling efforts greatly benefit from accurate annotation of protein-coding genes to develop proper functioning models. However, determining protein-coding genes for most new genomes is almost completely performed by inference, using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function. With the ability to directly measure peptides arising from expressed proteins, mass spectrometry-based proteomics approaches can be used to augment and verify coding regions of a genomic sequence and importantly detect post-translational processing events. In this study we utilized “shotgun” proteomics to guide accurate primary genome annotation of the bacterial pathogen Salmonella Typhimurium 14028 to facilitate a systems-level understanding of Salmonella biology. The data provides protein-level experimental confirmation for 44% of predicted protein-coding genes, suggests revisions to 48 genes assigned incorrect translational start sites, and uncovers 13 non-annotated genes missed by gene prediction programs. We also present a comprehensive analysis of post-translational processing events in Salmonella, revealing a wide range of complex chemical modifications (70 distinct modifications) and confirming more than 130 signal peptide and N-terminal methionine cleavage events in Salmonella. This study highlights several ways in which proteomics data applied during the primary stages of annotation can improve the quality of genome annotations, especially with regards to the annotation of mature protein products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Value of Faster Computation for Power Grid Operation

As a result of the grid evolution meeting the information revolution, the power grid is becoming far more complex than it used to be. How to feed data in, perform analysis, and extract information in a real-time manner is a fundamental challenge in today’s power grid operation, not to mention the significantly increased complexity in the smart grid environment. Therefore, high performance computing (HPC) becomes one of the advanced technologies used to meet the requirement of real-time operation. This paper presents benefit case studies to show the value of fast computation for operation. Two fundamental operation functions, state estimation (SE) and contingency analysis (CA), are used as examples. In contrast with today’s tools, fast SE can estimate system status in a few seconds—comparable to measurement cycles. Fast CA can solve more contingencies in a shorter period, reducing the possibility of missing critical contingencies. The benefit case study results clearly show the value of faster computation for increasing the reliability and efficiency of power system operation.

high performance computing↗
DOE OSTI2020

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

quantitative structure–property relationship (QSPR↗
NASA NTRS2016

Erosion Results of the MISSE 7 Polymers Experiment and Zenith Polymers Experiment After 1.5 Years of Space Exposure

Polymers and other oxidizable materials on the exterior of spacecraft in the low Earth orbit (LEO) space environment can be eroded due to reaction with atomic oxygen (AO). Therefore, in order to design durable spacecraft it is important to know the LEO AO erosion yield (E(sub y), volume loss per incident oxygen atom) of materials susceptible to AO reaction. Two spaceflight experiments, the Polymers Experiment and the Zenith Polymers Experiment, were developed to determine the AO E(sub y) of various polymers flown in ram, wake or zenith orientations in LEO. These experiments were flown as part of the Materials International Space Station Experiment 7 (MISSE 7) mission for 1.5 years on the exterior of the International Space Station (ISS). The experiments included Kapton H(TradeMark) witness samples for AO fluence determination in ram and zenith orientations. The Polymers Experiment also included samples to determine whether AO erosion of high and low ash containing polymers is dependent on fluence. This paper provides an overview of the MISSE 7 mission, a description of the flight experiments with details on the polymers flown, the characterization techniques used, the AO fluence for each exposure orientation, and the LEO E(sub y) results. The E(sub y) values ranged from 7.99x10(exp -28)cu cm/atom for TiO2/Al2O3 coated Teflon(TradeMark) fluorinated ethylene propylene (FEP) flown in the ram orientation to 1.22x10(exp -23cu cm/atom for polyvinyl alcohol (PVOH) flown in the zenith orientation. The E(sub y) of similar samples flown in different orientations has been compared to help determine solar exposure and associated heating effects on AO erosion. The E(sub y) data from these ISS spaceflight experiments provides valuable information for LEO spacecraft design purposes.

Teflon↗
DOE OSTI2025

Comparing multi-source urban flood indicators: satellite, simulation, and citizen-reported data

Urban flooding arises from complex mechanisms, making it challenging to capture accurately with a single detection method. This study evaluates three complementary approaches to detect flooding across three Chicago neighborhoods: (i) Sentinel-1 synthetic aperture radar (SAR), offering weather-independent, high-resolution (10 m) imagery of surface inundation; (ii) the storm water management model (SWMM), simulating combined sewer overflow and drainage performance; and (iii) citizen-generated 311 service requests, capturing observed flooding impacts. By analyzing six storms ranging from severe to mild, we examine how each source uniquely contributes to identifying urban flood events. SAR imagery effectively identifies standing water but can miss brief flooding due to satellite revisit constraints. SWMM provides detailed insights into system-wide drainage behavior yet may underestimate localized street-level flooding. Meanwhile, 311 calls reflect real-world flooding impacts but are vulnerable to underreporting. Statistical overlap analysis highlights chronic flood hotspots repeatedly identified across multiple detection methods, indicating persistent infrastructure and topographic vulnerabilities. Temporal analysis further reveals that while SWMM flooding aligns closely with rainfall peaks, 311 calls typically precede or persist beyond these peaks. Our findings emphasize the value of using satellite observations, hydrological modeling, and resident-reported data in a complementary manner to better interpret patterns in flood timing, severity, and spatial distribution—providing insights that can inform targeted infrastructure improvements and contribute to urban flood resilience planning.

311↗
NASA NTRS2022

On the Effects of Cloud Water Content on Passive Microwave Snowfall Retrievals

The Bayesian passive microwave retrievals of snowfall often rely on mathematical matching of the observed vectors of brightness temperature with an a priori database of precipitation profiles and their corresponding brightness temperatures. Mathematical proximity does not necessarily lead to consistent retrievals due to limited information content of passive microwave observations. This paper defines imposter (genuine) vectors of brightness temperature as those that are mathematically close but physically inconsistent (consistent) and characterizes them through the Silhouette Coefficient (SC) analysis. The Neyman–Pearson (NP) hypothesis testing is used to separate the imposter and genuine brightness temperatures based on their associated values of cloud ice (IWP) and liquid water path (LWP), given by coincidences of CloudSat Profiling Radar (CPR) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The study determines thresholds for IWP and LWP that allow optimal identification of imposter brightness temperatures of non-snowing and snowing clouds, which can mislead the passive microwave retrieval algorithms to falsely detect or miss the snowfall events. It is demonstrated that emission signal of supercooled liquid water in snowing clouds can lead to improved passive microwave retrieval of snowfall and conditioning the retrievals to the cloud IWP and LWP can result in marginal correction of the snowfall detection probability; however, reduce the probability of false alarm by 6%–8% over sea ice and open oceans.

Snowfall↗
DOE OSTI2024

Augmenting machine learning of Grad–Shafranov equilibrium reconstruction with Green's functions

This work presents a method for predicting plasma equilibria in tokamak fusion experiments and reactors. The approach involves representing the plasma current as a linear combination of basis functions using principal component analysis of plasma toroidal current densities (J t ) from the EFIT-AI equilibrium database. Then utilizing EFIT's Green's function tables, basis functions are created for the poloidal flux (ψ) and diagnostics generated from the toroidal current (J t ). Similar to the idea of a physics-informed neural network (NN), this physically enforces consistency between ψ, J t , and the synthetic diagnostics. First, the predictive capability of a least squares technique to minimize the error on the synthetic diagnostics is employed. The results show that the method achieves high accuracy in predicting ψ and moderate accuracy in predicting J t with median R 2 = 0.9993 and R 2 = 0.978, respectively. A comprehensive NN using a network architecture search is also employed to predict the coefficients of the basis functions. The NN demonstrates significantly better performance compared to the least squares method with median R 2 = 0.9997 and 0.9916 for J t and ψ, respectively. The robustness of the method is evaluated by handling missing or incorrect data through the least squares filling of missing data, which shows that the NN prediction remains strong even with a reduced number of diagnostics. Additionally, the method is tested on plasmas outside of the training range showing reasonable results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1995

Characterizing and Assessing a Large-Scale Software Maintenance Organization

One important component of a software process is the organizational context in which the process is enacted. This component is often missing or incomplete in current process modeling approaches. One technique for modeling this perspective is the Actor-Dependency (AD) Model. This paper reports on a case study which used this approach to analyze and assess a large software maintenance organization. Our goal was to identify the approach's strengths and weaknesses while providing practical recommendations for improvement and research directions. The AD model was found to be very useful in capturing the important properties of the organizational context of the maintenance process, and aided in the understanding of the flaws found in this process. However, a number of opportunities for extending and improving the AD model were identified. Among others, there is a need to incorporate quantitative information to complement the qualitative model.

Briand, Lionel↗