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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Structural elucidation of the cis -prenyltransferase NgBR/DHDDS complex reveals insights in regulation of protein glycosylation

Cis-prenyltransferase (cis-PTase) catalyzes the rate-limiting step in the synthesis of glycosyl carrier lipids required for protein glycosylation in the lumen of endoplasmic reticulum. Here, we report the crystal structure of the human NgBR/DHDDS complex, which represents an atomic resolution structure for any heterodimeric cis-PTase. The crystal structure sheds light on how NgBR stabilizes DHDDS through dimerization, participates in the enzyme’s active site through its C-terminal -RXG- motif, and how phospholipids markedly stimulate cis-PTase activity. Comparison of NgBR/DHDDS with homodimeric cis-PTase structures leads to a model where the elongating isoprene chain extends beyond the enzyme’s active site tunnel, and an insert within the α3 helix helps to stabilize this energetically unfavorable state to enable long-chain synthesis to occur. These data provide unique insights into how heterodimeric cis-PTases have evolved from their ancestral, homodimeric forms to fulfill their function in long-chain polyprenol synthesis.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1995

SAVS: A Space and Atmospheric Visualization Science system

The research environment faced by space and atmospheric scientists in the 1990s is characterized by unprecedented volumes of new data, by ever-increasing repositories of unexploited mission files, and by the widespread use of empirical and large-scale computational models needed for the synthesis of understanding across data sets and discipline boundaries. The effective analysis and interpretation of such massive amounts of information have become the subjects of legitimate concern. With SAVS (a Space and Atmospheric Visualization Science System), we address these issues by creating a 'push-button' software environment that mimics the logical scientific processes in data acquisition, reduction, and analysis without requiring a detailed understanding of the methods, networks, and modules that link the tools and effectively execute the functions. SAVS provides (1) a customizable framework for accessing a powerful set of visualization tools based on the popular AVS visualization software with hooks to PV-Wave and access to Khoros modules, (2) a set of mathematical and statistical tools, (3) an extensible library of discipline-specific functions and models (e.g., MSIS, IRI, Feldstein Oval, IGRF, satellite tracking with CADRE-3, etc.), and (4) capabilities for local and remote data base access. The system treats scalar, vector, and image data, and runs on most common Unix workstations. We present a description of SAVS and its components, followed by several applications based on generic research interests in interplanetary and magnetospheric physics (IMP/ISTP), active experiments in space (CRRES), and mission planning focused on the Earth's thermospheric, ionospheric, and mesospheric domains (TIMED).

Szuszczewicz, E. P.↗
NASA NTRS2004

Retrofitting the AutoBayes Program Synthesis System with Concrete Syntax

AutoBayes is a fully automatic, schema-based program synthesis system for statistical data analysis applications. Its core component is a schema library. i.e., a collection of generic code templates with associated applicability constraints which are instantiated in a problem-specific way during synthesis. Currently, AutoBayes is implemented in Prolog; the schemas thus use abstract syntax (i.e., Prolog terms) to formulate the templates. However, the conceptual distance between this abstract representation and the concrete syntax of the generated programs makes the schemas hard to create and maintain. In this paper we describe how AutoBayes is retrofitted with concrete syntax. We show how it is integrated into Prolog and describe how the seamless interaction of concrete syntax fragments with AutoBayes's remaining legacy meta-programming kernel based on abstract syntax is achieved. We apply the approach to gradually mitigate individual schemas without forcing a disruptive migration of the entire system to a different First experiences show that a smooth migration can be achieved. Moreover, it can result in a considerable reduction of the code size and improved readability of the code. In particular, abstracting out fresh-variable generation and second-order term construction allows the formulation of larger continuous fragments.

Fischer, Bernd↗
DOE OSTI2021

A reporting format for field measurements of soil respiration

Field observations of the soil-to-atmosphere CO2 flux–soil respiration, RS–are a prime example of ‘long tail’ data that historically have had neither centralized databases nor an agreed-upon reporting format. This has hindered scientific transparency, analytical reproducibility, and novel syntheses with respect to this globally-important component of the carbon cycle. Here we propose a new data and metadata reporting format for RS data, based on engagement with a wide range of researchers in the field as well as expert advisory panels. Our goal was a reporting format that would be relevant and useful for synthesis activities, and optimizing data discoverability and usability while not placing an undue burden on data contributors. We describe previous RS data collection efforts, lessons learned from related databases and data-oriented networks (e.g. FLUXNET) in earth and ecological sciences, and the process of community consultation. The proposed reporting format focuses on chamber-level data and metadata, specifying measurement conditions and, for a given measurement period defined by beginning and ending timestamps, a mean RS flux (or CO2 concentration) and associated ancillary measurements. Fundamentally, this format aims to enable findable, accessible, interoperable, and reusable data, while providing ‘future-proofing’ capabilities to support reanalyses using as yet unknown algorithms or approaches. Finally, this proposed RS reporting format is available online, and is intended to be a dynamic document, subject to further community feedback and/or change in the future.

Bond-Lamberty, Benjamin↗
NASA NTRS1984

Analysis of Doppler lidar wind measurements

New analysis methods were developed and refined for the synthesis of wind fields from multiple Doppler radar data. Considerable software was written and debugged for wind field synthesis. The analysis methods were applied to radar data collected during the CCOPE experiment in which lidar wind measurements were also made. The analysis method differs from existing methods in that interpolation (using filters with assigned weights) and advection of data, to account for time differential between data acquisition at different points, are not used. Instead, functional fits were used in space and time coordinates to transfer radial velocity data to a space-time grid. Subsequently the wind synthesis is performed. This method gives better space resolution in the synthesized winds, compared to other methods in common use, by better preserving the gradients of velocities. The wind fields show the cellular structure characteristic of boundary-layer convection. Also evident in the analysis are the sharp transitions and gradients in the wind field which were preserved by the synthesis.

Srivastava, R. C.↗
DOE OSTI2020

Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction

While quantitative structure-properties relations (QSPRs) have been developed successfully in multiple fields, catalyst synthesis affects structure and in turn performance, making simple QSPRs inadequate. Furthermore, catalysts often have multiple active sites preventing one from obtaining insights into structure-property relations. Here, we develop a data-driven quantitative synthesis-structure-property relation (QS2PRs) methodology to elucidate correlations between catalyst synthesis conditions, structural properties as well as observed performance and to provide fundamental insights into active sites and a systematic way to optimize practical catalysts. Here, we demonstrate the approach to the synthesis of nitrogen-doped catalysts (NDC) made via pyrolysis for the performance of the electrochemical hydrogen evolution reaction (HER), quantified by the onset potential and the current density. We determine crystallinity, nitrogen species type and fraction, surface area, and pore structure of the NDC’s using XRD, XPS, and BET characterization. We demonstrated that an active learning-based optimization combined with various elementary machine learning tools (regression, principal component analysis, partial least squares) can efficiently identify optimum pyrolysis conditions to tune structural characteristics and performance with concomitant savings in materials and experimental time. Unlike previous reports on the importance of pyridinic or graphitic nitrogen, we discover that the electrochemical performance is not driven by a single catalyst property; rather, it arises from a multivariate influence of nitrogen dopants, pore structure and disorder in the NDC materials. Identification of active sites can help mechanistic understanding and further catalyst improvement.

42 ENGINEERING↗
DOE OSTI2021

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities

Abstract As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

MacroSheds: A synthesis of long-term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies

The US Federal Government supports hundreds of watershed monitoring efforts from which solute fluxes can be calculated. Although instrumentation and methods vary between studies, the data collected and their motivating questions are remarkably similar. Nevertheless, little effort toward their compilation has previously been made. The MacroSheds project has developed a future-friendly system for harmonizing daily time series of streamflow, precipitation, and solute chemistry from 169+ watersheds, and supplementing each with watershed attributes. Here, we describe the breadth of MacroSheds data, and detail the steps involved in rendering each data product. We provide recommendations for usage and discuss when other datasets might be more suitable. The MacroSheds dataset is an unprecedented resource for watershed science, and for hydrology, as a small-watershed supplement to existing collections of streamflow predictors, like CAMELS and GAGES-II. The MacroSheds platform includes a web dashboard for visualization and an R package for data access and analysis.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2019

The Arctic-Boreal vulnerability experiment model benchmarking system

NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) integrates field and airborne data into modeling and synthesis activities for understanding Arctic and Boreal ecosystem dynamics. The ABoVE Benchmarking System (ABS) is an operational software package to evaluate terrestrial biosphere models against key indicators of Arctic and Boreal ecosystem dynamics, i.e.: carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance. The ABS utilizes satellite remote sensing data, airborne data, and field data from ABoVE as well as collaborating research networks in the region, e.g.: the Permafrost Carbon Network, the International Soil Carbon Network, the Northern Circumpolar Soil Carbon Database, AmeriFlux sites, the Moderate Resolution Imaging Spectroradiometer, the Orbiting Carbon Observatory 2, and the Soil Moisture Active Passive mission. The ABS is designed to be interactive for researchers interested in having their models accurately represent observations of key Arctic indicators: a user submits model results to the system, the system evaluates the model results against a set of Arctic-Boreal benchmarks outlined in the ABoVE Concise Experiment Plan, and the user then receives a quantitative scoring of model strengths and deficiencies through a web interface. This interactivity allows model developers to iteratively improve their model for the Arctic-Boreal Region by evaluating results from successive model versions. We show here, for illustration, the improvement of the Lund–Potsdam–Jena-Wald Schnee und Landschaft (LPJwsl) version model through the ABoVE ABS as a new permafrost module is coupled to the existing model framework. The ABS will continue to incorporate new benchmarks that address indicators of Arctic-Boreal ecosystem dynamics as they become available.

ABoVE↗
NASA NTRS1999

Systems Engineering Design Via Experimental Operation Research: Complex Organizational Metric for Programmatic Risk Environments (COMPRE)

Unique and innovative graph theory, neural network, organizational modeling, and genetic algorithms are applied to the design and evolution of programmatic and organizational architectures. Graph theory representations of programs and organizations increase modeling capabilities and flexibility, while illuminating preferable programmatic/organizational design features. Treating programs and organizations as neural networks results in better system synthesis, and more robust data modeling. Organizational modeling using covariance structures enhances the determination of organizational risk factors. Genetic algorithms improve programmatic evolution characteristics, while shedding light on rulebase requirements for achieving specified technological readiness levels, given budget and schedule resources. This program of research improves the robustness and verifiability of systems synthesis tools, including the Complex Organizational Metric for Programmatic Risk Environments (COMPRE).

Mog, Robert A.↗
DOE OSTI2024

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.

36 MATERIALS SCIENCE↗
NASA NTRS1997

NASA as a Catalyst: Use of Satellite Data in the States

NASA revolutionized our view of the world in 1972 with the launch of the first satellite to monitor the Earth. Recognizing the importance of states in governing the United States, NASA then established a program in the late 1970s to educate and assist states in using satellite data products. This report reviews this brief, but beneficial program that laid a foundation and catalyzed satellite data work that continues today in several states. More recently, outreach efforts as part of NASAs Mission to Planet Earth program and growing state government roles, responsibilities, and initiatives led NASA to begin a new effort in 1994 to understand and work effectively with states. This effort included an investigation and synthesis of current satellite data conditions in each of the 50 states that are included in this report. It provided strong evidence that some state governments are applying satellite data to an increasing array of government needs, while other states have very limited applications to date. A wide range of satellite data applications in executive branch agencies are described, as well as the recent status of the Gap Analysis Program in each of the states with this program. The report also reviews the status of satellite data and geographic information coordination efforts in each of the 50 states. In addition to this investigation, NASA convened a meeting of representatives of 12 states experienced with satellite data to identify future satellite data uses and needs, as well as NASA opportunities to enhance the utility of satellite data products. The findings and recommendations from this meeting, the 50 state investigations, and NASAs past state programs are also included in the report; they provide the rationale for NASA to establish a new outreach effort with state governments in the late 1990s.

Warnecke, Lisa↗
DOE OSTI2024

Bi-fidelity variational auto-encoder for uncertainty quantification

Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary objective in model validation. However, achieving this goal entails balancing the need for computational efficiency with the requirement for numerical accuracy. To address this trade-off, we propose a novel bi-fidelity formulation of variational auto-encoders (BF-VAE) designed to estimate the uncertainty associated with a QoI from low-fidelity (LF) and high-fidelity (HF) samples of the QoI. Here, this model allows for the approximation of the statistics of the HF QoI by leveraging information derived from its LF counterpart. Specifically, we design a bi-fidelity auto-regressive model in the latent space which is integrated within the VAE’s probabilistic encoder–decoder structure. An effective algorithm is proposed to maximize the variational lower bound of the HF log-likelihood in the presence of limited HF data, resulting in the synthesis of HF realizations with a reduced computational cost. Additionally, we introduce the concept of the bi-fidelity information bottleneck (BF-IB) to provide an information-theoretic interpretation of the proposed BF-VAE model. Our numerical results demonstrate that the BF-VAE leads to considerably improved accuracy, as compared to a VAE trained using only HF data, when limited HF data is available.

42 ENGINEERING↗
NASA NTRS1979

Synthesis of regional crust and upper-mantle structure from seismic and gravity data

Available seismic and ground based gravity data are combined to infer the three dimensional crust and upper mantle structure in selected regions. This synthesis and interpretation proceeds from large-scale average models suitable for early comparison with high-altitude satellite potential field data to more detailed delineation of structural boundaries and other variations that may be significant in natural resource assessment. Seismic and ground based gravity data are the primary focal point, but other relevant information (e.g. magnetic field, heat flow, Landsat imagery, geodetic leveling, and natural resources maps) is used to constrain the structure inferred and to assist in defining structural domains and boundaries. The seismic data consists of regional refraction lines, limited reflection coverage, surface wave dispersion, teleseismic P and S wave delay times, anelastic absorption, and regional seismicity patterns. The gravity data base consists of available point gravity determinations for the areas considered.

Alexander, S. S.↗
NASA NTRS2019

Progress in Atmospheric Carbon Monitoring Using NASA's GEOS Model and Data from the OCO and GOSAT Missions

NASA's Global Modeling and Assimilation Office (GMAO) produces a variety of carbon products based the synthesis of satellite remote sensing data and outputs of the Goddard Earth Observing System (GEOS). This includes bottom-up surface fluxes due to fossil fuel emissions, biomass burning, terrestrial biospheric exchange, and ocean exchangeconstrained by measurements of nighttime lights, fire radiative power, normalized difference vegetation index, and ocean color. These fluxes are the basis of top-down estimates of carbon concentrations and fluxes. In particular, the GMAO system processes retrievals of column carbon dioxide (XCO2) from GOSAT and OCO-2 to produce a high-resolution, long-term global analysis of CO2 in three dimensions every 6 hours. Here, we discuss the potential applications of such products for satellite intercomparison and evaluation against independent, non-coincident data. We also highlight the ability to provide monthly global atmospheric growth rates inferred from the assimilated CO2 concentration product. Finally, we discuss the challenges facing such products including bias correction and the estimation and analysis of model transport errors.

Weir, B.↗
DOE OSTI2022

Aqueous spray-drying synthesis of alluaudite Na 2+2x Fe 2–x (SO 4 ) 3 sodium insertion material: studies of electrochemical activity, thermodynamic stability, and humidity-induced phase transition

In pursuit of high-energy density sodium insertion materials, polyanionic frameworks can be designed with tuneable high-voltage operation stemming from inductive effect. Alluaudite Na 2 Fe 2 (SO 4 ) 3 polysulfate forms one such earth-abundant compound registering the highest Fe 3+ /Fe 2+ redox potential (ca. 3.8 V vs. Na/Na + ). While this SO 4 -based system exhibits high voltage operation, it is prone to thermal decomposition and moisture attack leading to hydrated derivatives, making its synthesis cumbersome. Also, the Na–Fe–S–O quaternary system is rich with (anhydrous to hydrated) phase transitions. Herein, we demonstrate scalable aqueous-based spray drying synthesis of alluaudite Na 2+2x Fe 2–x (SO 4 ) 3 sodium insertion material involving the formation of bloedite Na 2 Fe(SO 4 ) 2 ·4H 2 O as an intermediate phase. Moreover, a reversible phase transition from alluaudite to bloedite under controlled conditions of temperature and relative humidity is reported for the first time. Thermochemistry measurements revealed the enthalpies of formation (ΔH° f ) of alluaudite and bloedite are exothermic. Hydrated bloedite (ΔH° f = –117.16 ± 1.10 kJ/mol) was found to be significantly more energetically stable than anhydrous alluaudite (ΔH° f = –11.76 ± 1.25 kJ/mol). The calorimetric data support the observed synthesis and transformation (hydration-dehydration) pathways. Spray drying route led to spherical morphology delivering capacity ~80 mAh/g. Spray drying can be extended for rapid economic synthesis of sulfate class of battery materials.

25 ENERGY STORAGE↗