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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Deflection-Based Structural Loads Estimation From the Active Aeroelastic Wing F/A-18 Aircraft

Traditional techniques in structural load measurement entail the correlation of a known load with strain-gage output from the individual components of a structure or machine. The use of strain gages has proved successful and is considered the standard approach for load measurement. However, remotely measuring aerodynamic loads using deflection measurement systems to determine aeroelastic deformation as a substitute to strain gages may yield lower testing costs while improving aircraft performance through reduced instrumentation weight. This technique was examined using a reliable strain and structural deformation measurement system. The objective of this study was to explore the utility of a deflection-based load estimation, using the active aeroelastic wing F/A-18 aircraft. Calibration data from ground tests performed on the aircraft were used to derive left wing-root and wing-fold bending-moment and torque load equations based on strain gages, however, for this study, point deflections were used to derive deflection-based load equations. Comparisons between the strain-gage and deflection-based methods are presented. Flight data from the phase-1 active aeroelastic wing flight program were used to validate the deflection-based load estimation method. Flight validation revealed a strong bending-moment correlation and slightly weaker torque correlation. Development of current techniques, and future studies are discussed.

Lizotte, Andrew M.↗
NASA NTRS2022

Should’ve Could’ve: Progress in the Systems Engineering of the Mars2020 On-Board Planner

Mars rover operations has traditionally controlled behavior using meticulously compiled, so-called “Master” sequences that enforce deterministic timing and ordering of activities. While the approach is effective at managing risk and complexity, it can also be inefficient. Extended operations of the Mars2020 rover envisions a fundamentally different scheduling and execution approach. Atomic activities are constrained by mission operators in time, resource usage, and a structure of dependencies. The rover flight software will create and re-create schedules, responding to available energy, data volume, actual activity durations and execution status, observed temperatures, and other on-board state. Schedules are expected to change substantially in the course of execution. The solution space is prohibitively large and probabilistic in nature. A range of emergent behavior is accessible depending on the interaction between actual on-board state and a given formulation of constraints. For years, the specification and implementation of this capability have been considered and refined, as testing and analysis have mapped the contours of the system. Many concepts that could have been adopted were rejected, and in some cases, emergent properties have been exposed and beaten back with approaches to steer the behavior. This paper discusses some of the design history and underlying reasoning in the systems engineering of the first autonomous activity planner on Mars.

Kuhn, Stephen R↗
NASA NTRS1992

The interpretation of crustal dynamics data in terms of plate interactions and active tectonics of the Anatolian plate and surrounding regions in the Middle East

A detailed study was made of the consequences of the Arabian plate convergence against Eurasia and its effects on the tectonics of Anatolia and surrounding regions of the eastern Mediterranean. A primary source of information is time rates of change of baseline lengths and relative heights determined by repeated SLR measurements. These SLR observations are augmented by a network of GPS stations in Anatolia, Aegea, and Greece, established and twice surveyed since 1988. The existing SLR and GPS networks provide the spatial resolution necessary to reveal the details of ongoing tectonic processes in this area of continental collision. The effort has involved examining the state of stress in the lithosphere and relative plate motions as revealed by these space based geodetic measurements, seismicity, and earthquake mechanisms as well as the aseismic deformations of the plates from conventional geodetic data and geological evidence. These observations are used to constrain theoretical calculations of the relative effects of: (1) the push of the Arabian plate; (2) high topography of Eastern Anatolia; (3) the geometry and properties of African-Eurasian plate boundary; (4) subduction under the Hellenic Arc and southwestern Turkey; and (5) internal deformation and rotation of the Anatolian plate.

Toksoz, M. Nafi↗
DOE OSTI2017

Revealing Physical Activity of GRB Central Engine with Macronova/Kilonova Data

The modeling of Li-Paczyński macronova/kilonova signals gives a reasonable estimate on the neutron-rich material ejected during the neutron star mergers. Usually the accretion disk is more massive than the macronova ejecta, with which the efficiencies of converting the disk mass into prompt emission of three merger-driven GRBs can hence be directly constrained. Supposing the macronovae/kilonovae associated with GRB 050709, GRB 060614, and GRB 130603B arose from radioactive decay of the r -process material, the upper limit on energy conversion efficiencies are found to be as low as ∼10{sup −6}–10{sup −4}. Moreover, for all three events, neutrino annihilation is likely powerful enough to account for the brief gamma-ray flashes. Neutrino annihilation can also explain the “extended” emission lasting ∼100 s in GRB 050709, but does not work for the one in GRB 060614. These progresses demonstrate that the macronova can serve as a novel probe of the central engine activity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Markov chain Monte Carlo (MCMC) Bayesian inference approach to analyze apparent activation barriers and reaction orders from microreactor data

Statistical analysis of steady-state catalytic kinetic data is often limited by data sparsity due to the slow pace at which the data is collected. Data sparsity and limitations in statistical analysis make it difficult to differentiate between mechanistic models and catalytic sites. A Bayesian inference tool is reported for catalysis researchers to estimate error in the determination of reaction orders from steady state microreactor data. The benefits of a Bayesian inference approach are discussed, as an alternative to the more common frequentist approach. The approach incorporates prior knowledge of the system and the data collected to form an error estimate on reaction orders. We investigated the effects of three distinct data treatments—individual fitting of trials, pooled analysis, and constrained regression methods—on the precision and uncertainty of reaction order determinations. To assess the robustness of our findings, we conducted sensitivity analyses to evaluate the influence of Bayesian parameters on uncertainty estimation. Additionally, we utilized synthetic data to illustrate how data quality impacts the precision of uncertainty assessments. We show Bayesian analysis can obtain a more precise estimation of error with a sparse data set than a frequentist analysis. Finally, this work provides strong evidence that the adoption of Bayesian analysis of kinetic data may help researchers make more precise arguments as to the strength of their evidence for a particular mechanistic hypothesis, or in comparing across different catalysts.

42 ENGINEERING↗
NASA NTRS1994

FOC SV support and science data analysis

The main activity of NASA grant NAG5-1733 has been the study of Planetary Nebulae in the Magellanic Clouds through observations using the ESA Faint Object Camera conducted during Hubble Space Telescope (HST) Observing Cycles 1 and 2 in the pre-Costar era and in Cycle 4 with Costar and the FOC. A table of the observations obtained during these observing periods is attached along with figures showing the final images. Both the table and the figures are intended for publication later this year in the Astrophysical journal. Work that had been carried out under this grant included: astrometric measurement of ground-based images and preparation of detailed observing proposals; re-calibration of FOC images; deconvolution; and data analysis and measurement. The work has resulted in two papers, the first already published and the second in preparation by Blades, Osmer and Barlow. A reprint of the first paper is attached. The second paper will enlarge upon the results described in the first paper, bearing in mind the much larger sample of objects. Finally, the recently acquired images in Cycle 4 with the corrected optics will allow us to make direct and quantitative comparison between the pre- and post-correction for FOC. This work will be carried out under a separate grant. All the observations concerning Planetary Nebulae as described in an earlier status report have been collected and measured. In addition, a preliminary study of the photometric response of the FOC was begun under this grant but not completed because the analysis of the Cycle 2 Planetary Nebulae took much longer and was much more difficult than originally envisaged (due to the spherical aberration problem). However, because our data covers a long time period, we shall be able to deduce the photometric properties now using the actual PN themselves. This work will be carried out under a separate grant.

Blades, J. Chris↗
DOE OSTI2018

Structure of the activated Edc1-Dcp1-Dcp2-Edc3 mRNA decapping complex with substrate analog poised for catalysis

The conserved decapping enzyme Dcp2 recognizes and removes the 5' eukaryotic cap from mRNA transcripts in a critical step of many cellular RNA decay pathways. Dcp2 is a dynamic enzyme that functions in concert with the essential activator Dcp1 and a diverse set of coactivators to selectively and efficiently decap target mRNAs in the cell. Here we present a 2.84 Å crystal structure of K . lactis Dcp1–Dcp2 in complex with coactivators Edc1 and Edc3, and with substrate analog bound to the Dcp2 active site. Our structure shows how Dcp2 recognizes cap substrate in the catalytically active conformation of the enzyme, and how coactivator Edc1 forms a three-way interface that bridges the domains of Dcp2 to consolidate the active conformation. Kinetic data reveal Dcp2 has selectivity for the first transcribed nucleotide during the catalytic step. The heterotetrameric Edc1–Dcp1–Dcp2–Edc3 structure shows how coactivators Edc1 and Edc3 can act simultaneously to activate decapping catalysis.

Science & Technology - Other Topics↗
DOE OSTI2016

United States Department of Energy severe accident research following the Fukushima Daiichi accidents

The U.S. Department of Energy (DOE) has played a major role in the U.S. response to the events at Fukushima Daiichi. During the first several weeks following the accident, U.S. assistance efforts were guided by results from a significant and diverse set of analyses. In the months that followed, a coordinated analysis activity aimed at gaining a more thorough understanding of the accident sequence was completed using laboratory-developed, system-level best-estimate accident analysis codes, while a parallel analysis was conducted by U.S. industry. A comparison of predictions for Unit 1 from these two studies indicated significant differences between MAAP and MELCOR results for key plant parameters, such as in-core hydrogen production. On that basis, a crosswalk was completed to determine the key modeling variations that led to these differences. In parallel with these activities, it became clear that there was a need to perform a technology gap evaluation on accident-tolerant components and severe accident analysis methodologies with the goal of identifying any data and/or knowledge gaps that may exist given the current state of light water reactor (LWR) severe accident research and augmented by insights from Fukushima. In addition, there is growing international recognition that data from Fukushima could significantly reduce uncertainties related to severe accident progression, particularly for boiling water reactors. On these bases, a group of U. S. experts in LWR safety and plant operations was convened by the DOE Office of Nuclear Energy (DOE-NE) to complete technology gap analysis and Fukushima forensics data needs identification activities. The results from these activities were used as the basis for refining DOE-NE's severe accident research and development (R&D) plan. Finally, this paper provides a high-level review of DOE-sponsored R&D efforts in these areas, including planned activities on accident-tolerant components and accident analysis methods.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2014

Active workflow system for near real-time extreme-scale science. In: PPAA '14 Proceedings of the first workshop on Parallel programming for analytics applications

In recent years, streaming-based data processing has been gaining substantial traction for dealing with overwhelming data generated by real-time applications, from both enterprise sources and scientific computing. In this work, however, we look at an emerging class of scientific data with Near Real-Time (NRT) requirement, in which data is typically generated in a bursty fashion with the near real-time constraints being applied primarily between bursts, rather than within a stream. A key challenge for this types of data sources is that the processing time per data element is not uniform, and not always feasible to predict. Given the observations on the increasing unpredictability of compute load and system dynamics, this work looks to adapt streaming-based approach to the context of this new class of large experiments and simulations that have complex run-time control and analysis issues. In particular, we deploy a novel two-tier scheme for handling the increasing unpredictability of runtime behaviors: Instead of relying on determining what and where to run the scientific workflows beforehand or partial dynamically, the decision will also be adaptively enhanced online according to system runtime status. This is enabled by embedding workflow along with data streams. Specifically, we break data outputs generated from experiments or simulations into multiple self-describing "chunks", which we call active data objects. As such, if there is a transient hotspot observed, a data object with unfinished workflow pipeline can break its previous schedule and search for a least loaded location to continue the execution. Our preliminary experiment results based on synthetic workloads demonstrate the proposed active workflow system as a very promising solution by outperforming the state-of-the-art semi-dynamic workflow schedulers with an improved workflow completion time, as well as a good scalability.

Zhang, Yanwei↗
DOE OSTI2024

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with ∼ 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers ∼ 20% shorter training time without any loss of accuracy.

Wang, Tianle [Brookhaven National Laboratory (BNL)↗
NASA NTRSDate not supplied

Extravehicular Activity Mission System Software (EMSS) - Enabling Human Planetary Exploration Data Within The Broader Planetary Data Ecosystem

The planetary science community is once again on the verge of generating, capturing and analyzing human planetary exploration data, this time via the Artemis program. Artemis missions will involve robotic missions in addition to human extravehicular activity (EVA) where crew will be generating scientific data [1]. Present-day robotic mission data expectations for data archiving involves ingesting data into the Planetary Data System (PDS), but how might PDS be leveraged/adapted/ready (or not) for human spaceflight mission data, particularly EVA data that includes non-scientific data that provides important context to the scientific data gathered on the lunar surface? This question has broader implications than what this abstract can answer, but we wanted to pose the question to 1) get conversations started and 2) highlight how operations software data handling could play a role in overall data curation.

M J Miller↗
NASA NTRSDate not supplied

Machine-Learning-Based Adaptive Thinning of CrIS Radiances to Improve Global Tropical Cyclone Analysis and Forecasts

This work is focused on optimizing the assimilation of hyperspectral infrared (IR) radiances from the Cross-track Infrared Sounder (CrIS) with the goal of improving the representation of tropical cyclones (TCs) in global analyses and forecasts. Current operational assimilation systems rely on subsampling IR radiances on a regular thinning grid. A new and improved adaptive methodology based on machine learning (ML) recognizes TCs from geostationary satellite imagery and is implemented in the Goddard Earth Observing System (GEOS) model and data assimilation framework. The ML methodology is extensively trained on existing TC data sets and creates for each TC a dynamic mask, based on the evolving shape and life cycle of that specific event. Once a TC mask is created, a switch is then activated in the data assimilation system to alter the thinning, ingesting more CrIS radiances within the moving mask, thus increasing the TC sampling. After the TC dissipates, the assimilation of CrIS radiances reverts to normal data density. Results of TC segmentation provided by a state-of-the-art generative machine learning model known as the Denoising Diffusion Probabilistic Model (DDPM) are compared to the previously used U-Net model. The new approach surpasses the performance of the previously developed one. The methodology is applied to both clear-sky and cloud-cleared radiances. Benefits from the latter methodology, particularly in improving the structure of TCs and the intensity forecasts, are presented.

Oreste Reale↗
NASA NTRS2018

Enriching the Twitter Stream Increasing Data Mining Yield and Quality Using Machine Learning

Social media data streams are important sources of real-time and historical global information for science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are exploring the Twitter data stream for its potential in augmenting the validation program of NASA Earth science missions, specifically the Global Precipitation Measurement (GPM) mission. We have implemented a tweet processing infrastructure that outputs classified precipitation tweets. Inputs are "passive" tweets, along with a smaller number of tweets from "active" participants, i.e., those knowingly contributing to our effort. The "active" tweets, presumably of higher quality, enrich the Twitter stream. "Active" sources include data scraped from other social media (e.g., public Facebook posts) and data from existing crowdsourcing programs (e.g., mPING reports). In addition, there is likely relevant precipitation information in images and documents that are the end points of links often included in tweets. Information derived from these "active" sources could then be tweeted into the Twitter stream, thus enriching its quality. The objective of our current work is to mine these tweet­ linked images and documents, using neural networks, to increase the information content and quality related to precipitation. For images, we classified them as either precipitation-related or not. For training and validation, we used images obtained via the Google custom search API. We created two models: (1) by training a simple Convolutional Neural Network and (2) by using transfer learning principles to adapt a pre-trained object recognition model. For documents, both those linked to tweets and the tweet contents, we trained Hierarchical Attention Networks to determine precipitation occurrence, type, and intensity. For training and validation, we used a keyword-filtered tweet data set labelled with ground truth data from Dark Sky (an API to retrieve weather-related labels) and the National Severe Storms Laboratory's Multi­ Radar/Multi-Sensor (MRMS) system. Our results demonstrated the efficacy of our machine learning approaches for enriching the Twitter stream, to derive information potentially useful for validation of earth science satellite data.

Albayrak, Arif↗
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↗
DOE OSTI2021

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Gas Adsorption Analyzer

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

3Flex,BET specific surface area and pore volume, A↗
DOE OSTI2021

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) X-Ray Diffraction

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Aerodynamic Particle Sizer, Condensation Particle Counter, and Meteorological Instrument Data Analysis

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

54 ENVIRONMENTAL SCIENCES↗