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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Suit Port Aft Bulkhead Mockup Test Results and Lessons Learned

The Small Pressurized Rover (SPR) is currently being carried as an integral part of the current Lunar Surface Architectures under consideration in the Constellation program. One element of the SPR is the suit port, the means by which the crew performs Extravehicular Activities (EVAs). Two suit port deliverables were produced in fiscal year 2008: an aft bulkhead mockup for functional integrated testing with the 1-G SPR mockup and a functional and pressurizable engineering unit. This paper focuses on the test results and lessons learned on the aft bulkhead mockup. The suit port aft bulkhead mockup was integrated with the mockup of the SPR cabin and chassis. It is located on the aft bulkhead of the SPR cabin structure and includes hatches, a locking mechanism, seals, interior and exterior suit don/doff aids, and exterior platforms to accommodate different crewmember heights. A lightweight mockup of the Mark III suit was tested with the suit port aft bulkhead mockup. There are several limitations to the suit port and mockup suits, and results of the suit port evaluation are presented and interpreted within the context of the limitations.

Romig, Barbara A.↗
DOE OSTI2022

McLennan Community College Requirements Analysis Report

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. In March of 2022, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from LEARN and McLennan Community College (MCC) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

St. Mary’s University Requirements Analysis Report

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Between September and December 2022, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from LEARN and St. Mary’s University for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

South Plains College Requirements Analysis Report

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Between October 2021 and January 2022, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from LEARN and South Plains College (SPC) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1994

Langley Research Center Strategic Plan for Education

Research assignment centered on the preparation of final draft of the NASA Langley Strategic Plan for Education. Primary research activity consisted of data collection, through interviews with LaRC Office of Education and NASA Headquarters staff, university administrators and faculty, and school administrators / teachers; and documentary analysis. Pre-college and university programs were critically reviewed to assure effectiveness, support of NASA and Langley's mission and goals; National Education Goals; and educational reform strategies. In addition to these mandates, pre-college programs were reviewed to address present and future LaRC activities for teacher enhancement and preparation. University programs were reviewed with emphasis on student support and recruitment; faculty development and enhancement; and LaRC's role in promoting the utilization of educational technologies and distance learning. The LaRC Strategic Plan for Education will enable the Office of Education to provide a focused and well planned continuum of education programs for students, teachers and faculty. It will serve to direct and focus present activities and programs while simultaneously offering the flexibility to address new and emerging directions based on changing national, state, and agency trends.

Proctor, Sandra B.↗
DOE OSTI2025

ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model

Wildfires have shown increasing trends in both frequency and severity across the contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth system models (ESMs). Alternatively, fire models based on machine learning (ML), which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ELM2.1-XGBFire1.0) that integrates an eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran–C–Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001–2019, the ELM2.1-XGBFire1.0 outperforms process-based fire models in terms of spatial distribution and seasonal variations. The ELM2.1-XGBFire1.0 has proven to be a new tool for studying vegetation–fire interactions and, more importantly, enables seamless exploration of climate–fire feedback, working as an active component of E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Advancing Vision-based Feedback and Convolutional Neural Networks for Visual Outlier Detection

Machine learning has matured into a technology that has immediate applicability to the surveillance needs of nuclear material storage containers. These containers at LANL are the barrier preventing release of radioactive material to the workers, public, and environment during the storage period of the material. Annual surveillance activities can only provide coverage on a handful of containers. There is a significant need for surveillance tools to identify potential issues and precursors to containment failure that can be used during opportunistic inspections and, more generally, outside of annual surveillance activities. In this report we provide details on the advancement of our proposed embodiment that combines an automation system for taking pictures and a high-accuracy machine learning-driven object detection software. We further showcase the improvements on the software side with progress on extracting unique identification features and advances in detecting damage. The current state of the system captures subject matter expert training and a space-conscious design whose implementation is envisioned in the near future.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2019

Developing a Machine-Learning-Based Processing Framework for Twitter and Other Crowdsourced Data

Crowdsourced data streams such as Twitter and other social media are important sources of real-time and historical global information for Earth science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we have been exploring the Twitter data stream for its potential in augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. To realize this potential, we need to increase the information density and enhance the quality of filtered precipitation tweets. We have implemented various components of a machine learning (ML)-based processing infrastructure for crowdsourced data that outputs, in this instance, useful and usable information derived from precipitation tweets. We have test enriched the Twitter stream with higher quality active tweets from those knowingly contributing to our effort and from existing crowdsourced programs (e.g., mPING, CoCoRaHS). We have experimented with various algorithms for processing tweets, including Naà ve Bayes, Convolutional Neural Network (CNN), Hierarchical Attention Network (HAN), and semi-supervised learning (with tri-training). Our current work focuses on (1) automated review of Earth science-related publications to determine relationships between discipline research needs and ML algorithms; (2) investigating Sequential Generative Adversarial Network (SeqGAN) for processing precipitation tweets for anomaly detection; and (3) managing crowdsourced data in a way that is compatible with existing NASA satellite data archives and using the data for ML applications. Key results include (1) network visualization of NLP-processed publications in various Earth science disciplines; (2) difference between GPM-linked, generated tweets and collected actual tweets that is small for GPM-determined light to moderate rain cases and high for GPM-determined heavy rain cases; and (3) identification of MongoDB for storing raw tweets and Zarr format for gridded tweets (compatible with GPM data). Our results have taken us a step closer to an operational ML-based tweet processing infrastructure and have already demonstrated that tweet-derived precipitation information is potentially useful for validation of Earth science satellite data.

Teng, William↗
NASA NTRS2005

Welding in Space: Lessons Learned for Future In Space Repair Development

Welds have been made in the harsh environment of space only twice in the history of manned space flight. The United States conducted the M5 12 experiment on Skylab and the former Soviet Union conducted an Extravehicular Activity. Both experiments demonstrated electron beam welding. A third attempt to demonstrate and advance space welding was made by the Marshall Space Flight Center in the 90's but the experiment was demanifested as a Space Shuttle payload. This presentation summarizes the lessons learned from these three historical experiences in the areas of safety, design, operations and implementation so that welding in space can become an option for in space repair applications.

Russell, C. K.↗
NASA NTRSDate not supplied

Lunar Mining and Processing: Considerations for Responsible Space Mining & Connections to Terrestrial Mining

The National Aeronautics and Space Administration (NASA) of the United States of America (US) has initiated the Artemis Moon to Mars program to send astronauts (the first woman and person of color) back to the lunar surface, create a sustainable human lunar exploration program, and lead the first human exploration mission to the Mars surface in the late 2030’s [1]. Besides reinvigorating human exploration beyond low Earth orbit not seen since the Apollo program and enabling new scientific activities and discoveries, a major objective of this program is to characterize the resources that exist on the Moon and Mars, and learn how to utilize them for human exploration and the commercialization of cis-lunar space. Commonly known as In Situ Resource Utilization (ISRU), the search for, acquisition, and processing of resources in space has the potential to greatly reduce the dependency on transporting mission consumables and infrastructure from Earth, thereby reducing mission costs, risks, and dependency on Earth. With the launch of Artemis I in November 2022 and the anticipation of several robotic missions to the Moon under the Commercial Lunar Payload Services (CLPS) program, greater recognition and excitement about NASA’s Artemis program and lunar exploration activities is growing in the public. With the recognition that past statements and concept videos of human exploration of the Moon are actually becoming real, there is also a growing awareness of the possible positive and negative consequences and impacts these exploration activities may have on the Moon and Mars. On the positive side, the development of ISRU and lunar mining and processing can enable and grow lunar surface exploration and cis-lunar commercial activities, as well as provide benefits to terrestrial industries through spin-in and spin-back of advanced technologies and autonomous operations. On the negative side, there is a perception that space mining will impact the lunar surface and environment negatively for science, and that cultural beliefs about the Moon need to be addressed and considered before these operations occur. This paper will begin to explore the potential driving attributes and guidelines that will address how best to maximize the lessons and connections to terrestrial mining to reduce the risk and cost of lunar ISRU and space commercial activities, enhance efforts to achieve the terrestrial ‘mine of the future’, and provide viable markets for space-derived technologies until commercial space mining is established. This paper will also begin to explore the potential driving attributes and guidelines that could address how to minimize the environmental and surface impacts of lunar ISRU and foster ‘responsible’ space mining that can be implemented until more official agreements and treaties are signed. The existing robust mining regulations adopted globally will be used as a basis for this examination and suggestions will be presented to adopt these agreements for use in space mining.

ISRU↗
DOE OSTI2020

Load altering attack-tolerant defense strategy for load frequency control system

Cyber attacks are emerging threats to every information-oriented energy management system. By violating the cyber systems, the hacker can disrupt the security and stability due to the strong coupling between the cyber and physical facilities. In this paper, one type of cyber attacks designated as the load altering attack is studied for the power system frequency control, and corresponding defense strategies are proposed to improve the frequency control performance. Considering the difficulty of the application of model-based controller into large-scale power systems, a novel model-free defense framework is for the first time presented. Under this framework, both active defense and passive defense strategies are designed. The former assumes that the defender has the initiative to learn different attack scenarios. Adaptive defense strategies are implemented using the online attack identification information and off-line trained strategy pool. The latter assumes that the defender passively tolerates various attack scenarios via the pre-trained off-line strategy. Both approaches prove to be effective through validation based on the IEEE benchmark systems. The proposed defense framework and defense strategies can be extended to other energy control systems to enhance their attack tolerance capability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A general mechanistic framework for cross-scale understanding of hot spots and hot moments in carbon and water fluxes

Semi-arid ecosystems, like those in the American Southwest, exert a massive impact on the interannual variability of carbon and water cycling. Unfortunately, these carbon and water fluxes are notoriously difficult to predict due to their high spatial and temporal variability, which is poorly captured by the current generation of vegetation models. Indeed, this region is exemplified by the ‘hot spots and hot moments’ concept, which states that small areas in space (‘hot spots’) and transient moments in time (‘hot moments’) exert an outsized influence on biogeochemical cycling. However, the factors that regulate these pulses in biogeochemical activity are unknown, as is their variability across space and time. These uncertainties severely limit efforts to better represent hot spots and hot moments in models. Here, we seek to develop a generalized method for detecting and quantifying the importance of hot spots and hot moments from individual plant to regional scales. Underpinning this method is our recently developed statistical approach for identifying hot spots and hot moments. By applying this method to semi-continuous measurements of plant water status, a depth profile of soil water potential, and ecosystem fluxes via eddy covariance, we will track the fate of water through the soil-plant-atmosphere continuum and identify the mechanistic drivers of these transient pulses in biogeochemical activity. Then, we will expand this approach across a broad network of Ameriflux towers, and apply a machine learning approach that will allow us to upscale measurements of hot spots and hot moments across the American Southwest and quantify their impact on carbon and water cycles. These products will allow us to identify hot spots and hot moments across spatio-temporal scales and will serve as crucial data sources for validating a new generation of models that can better capture highly dynamic carbon and water fluxes. The proposed method will be easily transferable across biomes and will serve as a framework for future research on hot spots and hot moments across the plant ecophysiology, biometeorology, and vegetation modeling communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A global DSCOVR/EPIC-based hourly/daily shortwave radiation/PAR dataset

Accurate characterization of the global downward shortwave (SW) and photosynthetically active radiation (PAR) is fundamental for Earth system modeling and global change research. Combined with a machine-learning method, we used the Earth Polychromatic Imaging Camera (EPIC) data onboard the Deep Space Climate Observatory (DSCOVR) to generate the global SW/PAR dataset at both hourly and daily temporal scales and 0.1degree×0.1degree spatial resolution. Covering a period from June, 2015 to June (about 4 years), 2019, the dataset consists of total SW/PAR and their direct and diffuse components. Our dataset will be valuable for understanding the diurnal cycles and seasonal patterns of ecosystem processes, as well as the solar energy utilizations

14 SOLAR ENERGY↗
DOE OSTI2022

SDSS-IV MaNGA: Unveiling Galaxy Interaction by Merger Stages with Machine Learning

We use machine-learning techniques to classify galaxy merger stages, which can unveil physical processes that drive the star formation and active galactic nucleus (AGN) activities during galaxy interaction. The sample contains 4690 galaxies from the integral field spectroscopy survey SDSS-IV MaNGA and can be separated into 1060 merging galaxies and 3630 nonmerging or unclassified galaxies. For the merger sample, there are 468, 125, 293, and 174 galaxies (1) in the incoming pair phase, (2) in the first pericentric passage phase, (3) approaching or just passing the apocenter, and (4) in the final coalescence phase or post-mergers. With the information of projected separation, line-of-sight velocity difference, Sloan Digital Sky Survey (SDSS) gri images, and MaNGA Hα velocity map, we are able to classify the mergers and their stages with good precision, which is the most important score to identify interacting galaxies. For the two-phase classification (binary; nonmerger and merger), the performance can be high (precision > 0.90) with LGBMClassifier . We find that sample size can be increased by rotation, so the five-phase classification (nonmerger, and merger stages 1, 2, 3, and 4) can also be good (precision > 0.85). The most important features come from SDSS gri images. The contribution from the MaNGA Hα velocity map, projected separation, and line-of-sight velocity difference can further improve the performance by 0%–20%. In other words, the image and the velocity information are sufficient to capture important features of galaxy interactions, and our results can apply to all the MaNGA data, as well as future all-sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1981

CF 6 engine diagnostics

A summary of the activities which led to defining deterioration rates of the CF6 family of engines, a description of what was learned, and an identification of means of conserving fuel based upon the program findings are presented. The program to define the deterioration levels and modes for the CF6 family of engines involved four distinct phases: analysis of inbound engine test results, analysis of airline cruise data, analysis of airline test cell data resulting from testing of refurbished engines, and inspection of engine hardware.

Stricklin, R.↗
NASA NTRS1991

Terminal Attractors In Neural Networks

Neural networks made to "learn" faster. Report presents theoretical study of terminal attractors in neural networks. Includes systematic analysis of applications to activation dynamics of neural networks.

Zak, Michail A.↗
NASA NTRS1991

SEEDS: A Celebration of Science

The major goal of the project of Space Exposed Experiment Developed for Students (SEEDS) was to stimulate interest in science through the active involvement of all participants. Youthful investigators utilized the basic and integrated science process skills as they conducted the research necessary to complete the data reports used in the compilation of this document. Participants described many unique activities designed to promote critical thinking and problem solving. Seeds made a significant impact toward enhancing the teaching, learning, and enjoyment of science for students worldwide.

Melton, Bob↗
NASA NTRS2010

GN and C Design Overview and Flight Test Results from NASA's Max Launch Abort System (MLAS)

The National Aeronautics and Space Administration (NASA) Engineering and Safety Center (NESC) designed, developed and flew the alternative Max Launch Abort System (MLAS) as risk mitigation for the baseline Orion spacecraft launch abort system (LAS) already in development. The NESC was tasked with both formulating a conceptual objective system (OS) design of this alternative MLAS as well as demonstrating this concept with a simulated pad abort flight test. The goal was to obtain sufficient flight test data to assess performance, validate models/tools, and to reduce the design and development risks for a MLAS OS. Less than 2 years after Project start the MLAS simulated pad abort flight test was successfully conducted from Wallops Island on July 8, 2009. The entire flight test duration was 88 seconds during which time multiple staging events were performed and nine separate critically timed parachute deployments occurred as scheduled. Overall, the as-flown flight performance was as predicted prior to launch. This paper provides an overview of the guidance navigation and control (GN&C) technical approaches employed on this rapid prototyping activity. This paper describes the methodology used to design the MLAS flight test vehicle (FTV). Lessons that were learned during this rapid prototyping project are also summarized.

Dennehy, Cornelius J.↗