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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

A WRF-Chem Flash Rate Parameterization Scheme and LNOx Analysis of the 29-30 May 2012 Convective Event in Oklahoma During DC3

The Deep Convective Clouds and Chemistry (DC3) field campaign in 2012 provided a plethora of aircraft and ground-based observations (e.g., trace gases, lightning and radar) to study deep convective storms, their convective transport of trace gases, and associated lightning occurrence and production of nitrogen oxides (NOx). Based on the measurements taken of the 29-30 May 2012 Oklahoma thunderstorm, an analysis against a Weather Research and Forecasting Chemistry (WRF-Chem) model simulation of the same event at 3-km horizontal resolution was performed. One of the main objectives was to include various flash rate parameterization schemes (FRPSs) in the model and identify which scheme(s) best captured the flash rates observed by the National Lightning Detection Network (NLDN) and Oklahoma Lightning Mapping Array (LMA). The comparison indicates how well the schemes predicted the timing, location, and number of lightning flashes. The FRPSs implemented in the model were based on the simulated thunderstorms physical features, such as maximum vertical velocity, cloud top height, and updraft volume. Adjustment factors were applied to each FRPS to best capture the observed flash trend and a sensitivity study was performed to compare the range in model-simulated lightning-generated nitrogen oxides (LNOx) generated by each FRPS over the storms lifetime. Based on the best FRPS, model-simulated LNOx was compared against aircraft measured NOx. The trace gas analysis, along with the increased detail in the model specification of the vertical distribution of lightning flashes as suggested by the LMA data, provide guidance in determining the scenario of NO production per intracloud and cloud-to-ground flash that best matches the NOx mixing ratios observed by the aircraft.

Atmospheric chemistry↗
NASA NTRS2015

Towards a Generic and Adaptive System-On-Chip Controller for Space Exploration Instrumentation

This paper introduces one of the first efforts conducted at NASA’s Jet Propulsion Laboratory (JPL) to develop a generic System-on-Chip (SoC) platform to control science instruments that are proposed for future NASA missions. The SoC platform is named APEX-SoC, where APEX stands for Advanced Processor for space Exploration, and is based on a hybrid Xilinx Zynq that combines an FPGA and an ARM Cortex-A9 dual-core processor on a single chip. The Zynq implements a generic and customizable on-chip infrastructure that can be reused with a variety of instruments, and it has been coupled with a set of off-chip components that are necessary to deal with the different instruments. We have taken JPL’s Compositional InfraRed Imaging Spectrometer (CIRIS), which is proposed for NASA icy moons missions, as a use-case scenario to demonstrate that the entire data processing, control and interface of an instrument can be implemented on a single device using the on-chip infrastructure described in this paper. We show that the performance results achieved in this preliminary version of the instrumentation controller are sufficient to fulfill the science requirements demanded to the CIRIS instrument in future NASA missions, such as Europa.

embedded system↗
NASA NTRSDate not supplied

Evaluating the Use of High-Fidelity Simulator Research Methods to Study Airline Flight Crew Resilience

As it evolves, aviation will continue to require integration of a wide range of safety systems and practices, some of which are already in place and others that are yet to be developed. New concepts in system safety thinking have emerged to consider not only what may go wrong, but also what can be learned when things go right during commercial flight operations. Taken together, these complementary perspectives form a more comprehensive approach to systemsafety thinking that can help to recognize and preserve the resilient performance capabilities currently provided by humans. A need exists, however, for research methods to enable better understanding of the human contributions to aviation safety. NASA’s System-Wide Safety Project supports research on using flight simulation methods to study operator resilience and safety-producing behaviors. Building on prior NASA efforts investigating procedural non-adherences during area navigation standard terminal route arrivals, a high-fidelity commercial aviation line operational simulation (LOS) experiment has been designed to study how flight crews anticipate, monitor for, respond to, and learn from expected and unexpected disturbances during these operations. A diverse set of LOS scenarios were developed to simulate highly realistic, complex, but routinely encountered operational situations. Each scenario provided multiple opportunities to collect data on how flight crews manage threats and errors, as well as novel opportunities to observe resilient and safety-producing behaviors. The experimental design, implications for the study of safety-producing behaviors using simulation, and considerations for airline pilot training will be discussed.

Chad L Stephens↗
NASA NTRS2022

Merging of the Case 2 Regional Coast Colour and Maximum‑Peak Height Chlorophyll‑a Algorithms: Validation and Demonstration of Satellite‑derived Retrievals Across US Lakes

Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll-a (chl-a, µg L−1) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl-a with in-water algorithms. Validation of chl-a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl-a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH(P)) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl-a data. The best performance based on mean absolute multiplicative error (MAEmult) was demonstrated by the merged algorithm referred to as C15−M10 (MAEmult = 1.8, biasmult = 0.97, n = 836). In the C15−M10 algorithm, the MPH(P) chl-a value was retained if it was > 10 µg L−1; if the MPH(P) value was ≤ 10 µg L−1, the C2RCC value was selected, as long as that value was < 15 µg L−1. Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl-a algorithms previously reported independently. Additionally, it contributes to the transition of chl-a algorithm maturity by quantifying error statistics for a number of locations and times.

Satellite↗
NASA NTRS2019

Everybody Needs Somebody Sometimes: Validation of Adaptive Recovery in Robotic Space Operations

This work assesses an adaptive approach to fault recovery in autonomous robotic space operations, which uses indicators of opportunity, such as physiological state measurements and observations of past human assistant performance, to inform future selections. We validated our reinforcement learning approach using data we collected from humans executing simulated mission scenarios. We present a method of structuring human-factors experiments that permits collection of relevant indicator of opportunity and assigned assistance task performance data, as well as evaluation of our adaptive approach, without requiring large numbers of test subjects. Application of our reinforcement learning algorithm to our experimental data shows that our adaptive assistant selection approach can achieve lower cumulative regret compared to existing non-adaptive baseline approaches when using real human data. Our work has applications beyond space robotics to any application where autonomy failures may occur that require external intervention.

Human-centered robotics↗
NASA NTRS2022

Robust Controller Synthesis for Vision-based Spacecraft Guidance and Control

This work develops a method for Robust Controller for Vision-based Spacecraft (RCVS) guidance and control, integral to the robust autonomy framework for multi-spacecraft for- mation control and reconfiguration applications. The method is built around the use of a photo-realistic simulator, where a camera is deployed on a tracking spacecraft (ego) in order to observe an uncontrolled spacecraft (target) in a Low Earth Orbit (LEO). In this direction, the proposed approach performs the relative state (attitude and position) estimation of the target spacecraft using Convolutional Neural Network (CNN). The state estimation error is then modeled and the corresponding error-bounds are obtained around a nominal trajectory of the ego and target spacecraft. Next, this work proposes a linear matrix inequalities (LMIs) based approach to controller synthesis, guaranteed to be robust against both model uncertainties and measurement errors, resulting from vision-based estimation. This controller is comprised of two distinct components, one synthesized based on the nominal trajectory, while the “robust” component corrects for deviations from the nominal trajectory. Finally, a tracking scenario that directly utilize the image data for spacecraft guidance and control, is presented to showcase the performance of the proposed robust autonomy framework.

Rahmani, Amir↗
NASA NTRS2023

Double Asteroid Redirection Test (DART) Mission

NASA’s Double Asteroid Redirection Test (DART) mission was humanity’s first attempt to move a celestial body, demonstrating the capability to perform a kinetic impact on a planetary defense–relevant sized asteroid. DART was part of the international collaboration known as the Asteroid Impact & Deflection Assessment (AIDA), involving NASA, the European Space Agency (ESA), the Agenzia Spaziale Italiana (ASI), and scientists around the world. DART was a key step to demonstrating preparedness to respond to planetary defense scenarios, and it provides a crucial data point for likely outcomes. Near-Earth objects (NEOs) greater than 140 m in size are of particular interest to planetary defense because they have the potential to cause significant damage if they were to impact Earth, and also because they are difficult to detect, with less than 50% of the predicted population discovered as of 2022 (National Academies Press, 2022). With an appropriately sized spacecraft and enough warning (typically many years to decades), a kinetic impact can slightly alter the orbit of an asteroid in a way that, over time, prevents the asteroid from colliding with Earth in the future. DART’s target was Dimorphos, the smaller (~150-m-diameter) member of the binary asteroid system (65803) Didymos, which is a near-Earth, potentially hazardous, and well-characterized asteroid system. By simply observing changes to the system after impact and comparing them with a pre-impact reference, it was possible to use ground-based telescopes to observe the deflection in the orbit of Dimorphos after impact. Developed and operated by the Johns Hopkins University Applied Physics Laboratory (APL), the mission entered formulation in 2015 after multiple years of concept development. The project was administered according to NPR 7120.5, with technical oversight and funding through the Planetary Missions Program Office (PMPO) at Marshall Space Flight Center (MSFC) and overall support as a directed mission from NASA’s Planetary Defense Coordination Office (PDCO). The DART spacecraft hosted a singular payload, the Didymos Reconnaissance and Asteroid Camera for Optical navigation (DRACO), and a deployable CubeSat contributed by ASI named the Light Italian CubeSat for Imaging of Asteroids (LICIACube). On 11 September 2022, DART deployed LICIACube, which subsequently followed the DART spacecraft at a safe distance and observed the immediate aftermath of the DART impact. DART was designed to autonomously detect, navigate to, and impact Dimorphos. This autonomous design was chosen to maximize the probability of impact, since commanding from the ground could result in course corrections arriving too late. On the day of impact, 26 September 2022, the spacecraft’s autonomous systems successfully detected and locked on to Dimorphos, impacting its surface within 2 m of the center of the illuminated figure (Jensenius et al., 2023). No human intervention was required for a successful impact, demonstrating that humanity possesses the technology to perform a kinetic impact. Within 2 weeks of impact, it was clear that the orbit of Dimorphos had been significantly altered. On 11 October 2022, NASA Administrator Bill Nelson announced that the new orbital period of Dimorphos was shortened by approximately 32 ± 2 min, from 11 h and 55 min before impact to 11 h and 23 min after impact. With additional observations over the following months, the accuracy of this measurement improved to a –33.24 min ± 1.4 s orbital period change (Naidu et al., 2023; Scheirich et al., 2023), and Beta (β), the momentum transfer enhancement parameter, was reported to be 3.6 (Cheng et al., 2023). Subsequent studies examined the details of DART’s impact site, modeled the impact event, investigated the ejecta produced, and analyzed the dynamics of the Didymos system. These combined results clearly demonstrate that the project met all Level 1 mission requirements.

asteroid↗
DOE OSTI2023

Identifying Potential Candidates for Renewable Energy Zones (REZs) in Bangladesh

Bangladesh faces several hurdles to achieving its renewable energy objectives, such as land availability and transmission congestion. Renewable Energy Zones (REZ), which are geographic areas with high-quality utility-scale renewable resources, suitable land topography, and commercial interest, can help address these challenges and support long-term generation and transmission planning. This study focuses on solar PV (fixed-tilt) and onshore wind, leveraging recently developed high temporal and spatial resolution resource data. In the moderate land exclusion scenario, large "study areas" are identified in Bangladesh with capacity factors in the top 25% for the entire country - 5 for wind and 8 for solar. Within these study areas, 19 candidates for REZ are identified based on the overlap between these study areas and upazilas (i.e., administrative subdivisions) containing economic development zones. Four of the identified candidate zones are opportunities for priority development, given the combination of strong wind and solar resources and the presence of economic zones. Furthermore, the geographic diversity of wind and solar resources in Bangladesh could help increase grid resilience by not concentrating all renewable energy development in the same region. Finally, pairing REZ with economic zones can bolster economic development, take advantage of large electricity demand, and leverage existing infrastructure investments.

Bangladesh↗
DOE OSTI2023

FORCE Output Data Visualization

The Framework for Optimization of ResourCes and Economics (FORCE) is a collection of software tools, models, and datasets acquired and developed under the Integrated Energy Systems (IES) program to enable analysis of technical and economic viability of myriad IES configurations. Idaho National Laboratory is hosting a training session for FORCE users and developers, which is "Integrated Energy Systems Tools FORCE Overview and Training 2023." We will present data output visualization results with FORCE scenarios as examples and show how does the new design improve existing automated visualization for analysis results.

25 ENERGY STORAGE↗
NASA NTRS1992

NGC 4550 - A laboratory for testing galaxy formation

The kinematic and photometric structure of the two components of the E7/S0 galaxy NGC 4550 are evaluated quantitatively to determine the system's morphology. Attention is given to whether the data indicate a rotating bulge with a counterrotating disk or a pair of counterstreaming disks, and formation scenarios are discussed in the light of the findings. The data examined include long-slit spectroscopy with evidence of bimodality in the line-of-sight velocity distribution. Two photometrically inseparable counterstreaming disk components are identified and their velocities given. The structure and the formation scenarios given point to the feasibility of absorption of external material by galaxy disks without extreme heating. The possibility of detecting stellar counterrotation in S0 disks is discussed based on the study of NGC 4550.

Rix, Hans-Walter↗
NASA NTRS2019

Modelling above-ground biomass stock over Norway using national forest inventory data with ArcticDEM and Sentinel-2 data

Boreal forests constitute a large portion of the global forest area, yet they are undersampled through field surveys, and only a few remotely sensed data sources provide structural information wall-to-wall throughout the boreal domain. ArcticDEM is a collection of high-resolution (2 m) space-borne stereogrammetric digital surface models (DSM) covering the entire land area north of 60° of latitude. The free-availability of ArcticDEM data offers new possibilities for aboveground biomass mapping (AGB) across boreal forests, and thus it is necessary to evaluate the potential for these data to map AGB over alternative open-data sources (i.e., Sentinel-2). This study was performed over the entire land area of Norway north of 60° of latitude, and the Norwegian national forest inventory (NFI) was used as a source of field data composed of accurately geolocated field plots (n=7710) systematically distributed across the study area. Separate random forest models were fitted using NFI data, and corresponding remotely sensed data consisting of either: i) a canopy height model (ArcticCHM) obtained by subtracting a high-quality digital terrain model (DTM) from the ArcticDEM DSM height values, ii) Sentinel-2 (S2), or iii) a combination of the two (ArcticCHM+S2). Furthermore, we assessed the effect of the forest- and terrain-specific factors on the models’ predictive accuracy. The best model (,i.e., ArcticCHM+S2) explained nearly 60% of the variance of the training set, which translated in the largest accuracy in terms of root mean square error (RMSE=41.4 t/ha). This result highlights the synergy between 3D and multispectral data in AGB modelling. Furthermore, this study showed that despite the importance of ArcticCHM variables, the S2 model performed slightly better than ArcticCHM model. This finding highlights some of the limitations of ArcticDEM, which, despite the unprecedented spatial resolution, is highly heterogeneous due to the blending of multiple acquisitions across different years and seasons. We found that both forest- and terrain-specific characteristics affected the uncertainty of the ArcticCHM+S2 model and concluded that the combined use of ArcticCHM and Sentinel-2 represents a viable solution for AGB mapping across boreal forests. The synergy between the two data sources allowed for a reduction of the saturation effects typical of multispectral data while ensuring the spatial consistency in the output predictions due to the removal of artifacts and data voids present in ArcticCHM data. While the main contribution of this study is to provide the first evidence of the best-case-scenario (i.e., availability of accurate terrain models) that ArcticDEM data can provide for large-scale AGB modelling, it remains critically important for other studies to investigate how ArcticDEM may be used in areas where no DTMs are available as is the case for large portions of the boreal zone.

space-borne imagery↗
NASA NTRSDate not supplied

Integrating Multi-agency Data Products in a Cloud-based Platform for Streamlined Discovery, Visualization, and Use

Earth science data users almost always have an interest in utilizing geospatial data from multiple agencies. As computing capability and cloud-based infrastructures accelerate the pace at which scientific research can be done, there is a growing need to enable search, discovery, and use of multi-agency geospatial observations relevant for a common use case - without undergoing the search and discovery process in a less efficient, disparate path with each agency. NASA’s Earth Observing System Data and Information System (EOSDIS) and NOAA’s National Environmental Satellite, Data and Information Service (NESDIS) both support a wide range of Earth science disciplines’ research, operations, and applications activities. Presently, however, there are few examples of data discovery frameworks supporting an inquiry of both NASA’s and NOAA’s extensive archives of Earth observations that are equally suitable for a particular science scenario, regardless of the agency that “owns” the data. NASA and NOAA are collaborating on a data expedition platform for exploring fire weather using data products from both agencies. Users will be able to search, discover, and visualize NASA and NOAA products in one interface. Each agency will curate metadata for its respective datasets, providing for a rich search experience. The collaboration will pilot a shared search interface into these metadata datastores. Data products will be stored in the cloud in cloud-optimized format(s). These formats will allow for optimized data access and visualization to support the “data expedition”. Avenues for further development and application of this cloud-based, multi-agency data provisioning platform will also be discussed.

cloud-based technology↗
DOE OSTI2024

ARPA-E Grid Optimization (GO) Competition Challenge 1

The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases but was not used in the Competition. Although most datasets were synthetic and generated by GRIDDATA, a few came from industry and were only used in the Final Event. All synthetic Input Data and Team Results for the GO Competition Challenge 1 for the Sandbox, Trial Events 1 to 3, and the Final Event along with problem, format, scoring and rules descriptions are available here. Data for industry scenarios will not be made public. Challenge 1, a minimization problem, required two computational steps. Solver 1 or Code 1 solved the base SCOPF problem under a strict wall clock time limit, as would be the case in industry, and reported the base case operating point as output, which was used to compute the Objective Function value that was used as the scenario score. The feasibility of the solution was provided by the Solver 2 or Code 2, which solves the power flow problem for all contingencies based on the results from Solver 1. This is not normally done in industry, so the time limits were relaxed. In fact, there were no time limits for Trial Event 1. This proved to be a mistake, with some codes running for more than 90 hours, and a time limit of 2 seconds per contingency was imposed for all other events. Entrants were free to use their own Solver 2 or use an open-source version provided by the Competition. Containers, such as Docker, were considered to improve the portability of codes, but none that could reliably support a multi-node parallel computing environment, e.g., MPI, could be found. For more information on the competition and challenge see the "GO Competition Challenge 1 Information" and "GO Competition Challenge 1 Additional Information" resources below.

ACOPF↗
DOE OSTI2021

A statistical analysis of the COHERENT data and applications to new physics

The observation of coherent elastic neutrino nucleus scattering (CEvNS) by the COHERENT collaboration in 2017 has opened a new window to both test Standard Model predictions at relatively low energies and probe new physics scenarios. Our investigations show, however, that a careful treatment of the statistical methods used to analyze the data is essential to derive correct constraints and bounds on new physics parameters. In this manuscript we perform a detailed analysis of the publicly available COHERENT CsI data making use of all available background data. We point out that Wilks’ theorem is not fulfilled in general and a calculation of the confidence regions via Monte Carlo simulations following a Feldman-Cousins procedure is necessary. As an example for the necessity of this approach to test new physics scenarios we quantify the allowed ranges for several scenarios with neutrino non-standard interactions. Furthermore, we provide accompanying code to enable an easy implementation of other new physics scenarios as well as data files of our results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

MultiTaskDeltaNet: change detection-based image segmentation for operando ETEM with application to carbon gasification kinetics

Transforming in situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often face limitations due to the scarcity of labeled data, visually ambiguous features of interest, and scenarios involving small objects. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively leverages minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to exploit correlations between physical features of interest. In an evaluation using data from in situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.

08 HYDROGEN↗
DOE OSTI2023

Long-run Marginal Emission Rates for Electricity - Workbooks for 2022 Cambium Data

These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States. A LRMER is an estimate of the rate of emissions that would be either induced or avoided by a change in electric demand, taking into account how the change could influence both the operation as well as the structure of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treat grid assets as fixed. Long-run marginal emissions rates are generally appropriate to use when trying to comprehensively estimate the impact of a long-lived (i.e., more than several years) intervention. There are two workbooks that supply the data at two different geographic resolutions: states and GEA regions (20 regions that are similar to, but not exactly the same as, the US EPA's eGRID regions). For more data underlying these emissions factors, see the Cambium 2022 project at https://scenarioviewer.nrel.gov/. For more details on input assumptions and methodology see the associated report (Cambium 2022 Scenario Descriptions and Documentation, https://www.nrel.gov/docs/fy23osti/84916.pdf). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nrel.gov/analysis/cambium.html.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1985

Advanced local area network concepts

Development of a good model of the data traffic requirements for Local Area Networks (LANs) onboard the Space Station is the driving problem in this work. A parameterized workload model is under development. An analysis contract has been started specifically to capture the distributed processing requirements for the Space Station and then to develop a top level model to simulate how various processing scenarios can handle the workload and what data communication patterns result. A summary of the Local Area Network Extendsible Simulator 2 Requirements Specification and excerpts from a grant report on the topological design of fiber optic local area networks with application to Expressnet are given.

Grant, Terry↗