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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

CIF Report - Information Fusion and Data Analytics for Human Lunar Exploration

This project leverages the Concept Exploration Laboratory (CEL) to collect, warehouse, and augment data relevant to human lunar exploration as a platform for NA (S&MA) to develop operational data integration techniques. The project capitalizes on 16+ years of CEL experience applied to NASA, DoD, the City of Houston, the State of Texas, and private industry. The integrated data will be utilized in the two scenarios described in a definition of concept for development of a full scale data analysis suite and storage solution, useful to all JSC organizations engaged in real time operations and safety tasks, and may be useful as pathfinders for the Digital Transformation Program.

information fusion↗
NASA NTRSDate not supplied

NASA’s Orion Crew Module Seakeeping Simulation and Test Comparisons in Ocean Wave Environments

The NASA Orion Crew Module (CM) post-landing seakeeping and uprighting capabilities are being evaluated across a variety of ocean wave conditions. This paper provides an overview of quarter-scale and full-scale test data and how it compares with the CM seakeeping dynamic simulation results evaluated using Wave Energy Convertor Simulator (WEC-Sim).This includes a discussion of the status of the dynamic modeling effort, correlation of model inputs with the wave field from the test, and a comparison of the results against the physical test data with emphasis on the full-scale results. Analysis in the frequency domain showed similar response characteristics between the model and the test and shows energy peaks corresponding to both the natural response and the forced response due to the wave environment. Time domain comparisons showed that the linear WEC-Sim model under predicted the amplitude of the pitch and roll motion of the test article, with improved matching for smaller, longer crested waves. An attempt was made to correlate the pitch/roll response to incident wave slope to account for a possible delay in the BTA’s response. The results did not show a strong relationship in the response characteristics to incident wave slope, but better correlation was seen by comparing responses in the frequency domain. Evaluation of the simulation’s ability to match test results aided in understanding the current fidelity of the CM model in WEC-Sim and identifying areas for model improvement

Abigail Lockard↗
NASA NTRS2021

Hawai‘i Island Disasters: Using NASA Earth Observations to Assess Coastal Flood Risk with Measures of Land Cover Change, Flood Extent, and Vulnerability for Adaptation and Mitigation Planning on Hawai‘i Island

As the County of Hawai‘i faces an increased risk of extreme flooding events, sea-level rise, and other hazards associated with climate change, the need for building geospatial capacity to make better-informed decisions is critical. The County of Hawai‘i and Arizona State University partnered with NASA DEVELOP to complete a macro-scale risk analysis for the island of Hawai‘i analyzing flooding, land cover, vulnerability, and exposure factors using Earth observations and socio-economic data. The team assessed the variation in urban coastal vulnerability around the entire island of Hawai‘i, using satellite imagery of coastal land cover typology from satellite products such as Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Sentinel-1 Synthetic Aperture Radar (SAR). The team made a sharable geodatabase containing datasets modeling vulnerability to coastal flooding as well as the Hawai‘i Flood Risk Toolbox (HiFloRT) which contains multiple tools for the County to map land cover, extreme rainfall and flood extent across the Island. The end products will allow the County of Hawai‘i to establish a protocol and standard framework for the utilization of Earth observations in future planning.

Garren Kalter↗
NASA NTRSDate not supplied

Using Coordinated, Multi-Agent Platforms for Dynamic Ocean Worlds Science

Planetary science missions have the opportunity to enhance science return through deployment of autonomous capabilities designed to dynamically respond to new information. Future outer solar system missions to ocean worlds in particular would benefit from this technology - intelligent science payloads (ISP) - because it would allow for a coordinated, near real-time response to ephemeral ‘events’ such as plumes, tectonism, surface implantation, volatile releases, thermal and magnetic anomalies, or radiation, as well as increasing the cadence and coverage of data collection. Prioritization and decision-making frameworks from ISP could be deployed at various scales - from analysis onboard a spacecraft with multiple instruments – to coordinated analyses among separate spacecraft in an e.g., distributed systems mission (DSM) composed of multiple SmallSats. Goddard’s Intelligent Science Payload team is developing an agile autonomous architecture for an icy ocean worlds DSM concept. Our goals are to coordinate data collection and onboard data analysis, and to make autonomous decisions for new data collection and analysis based on science priorities between multiple spacecraft with variable instrumentation and orbits. We use a range of data analysis tools to coordinate the DSM response, spanning from observations of data over a specified threshold to more computationally intensive machine learning algorithms (ML). ML algorithms here currently focus on determining the composition of an ocean world using mass spectrometry, and specifically methods for understanding ‘novelties’ and potential biosignatures. These algorithms could be used to quickly process and analyze onboard data that would be significantly delayed in downlink due to long communication delays for outer solar system missions in order to make dynamic science observations. Our ocean worlds case study ISP architecture is intended as an ‘agile’ and modular framework that could be used as a whole or as particular modules based on mission needs.

Distributed Systems↗
NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗
NASA NTRS2024

Characterizing the California Current System through Sea Surface Temperature and Salinity

Characterizing temperature and salinity (T-S) conditions is a standard framework in oceanography to identify and describe deep water masses and their dynamics. At the surface, this practice is hindered by multiple air–sea–land processes impacting T-S properties at shorter time scales than can easily be monitored. Now, however, the unsurpassed spatial and temporal coverage and resolution achieved with satellite sea surface temperature (SST) and salinity (SSS) allow us to use these variables to investigate the variability of surface processes at climate-relevant scales. In this work, we use SSS and SST data, aggregated into domains using a cluster algorithm over a T-S diagram, to describe the surface characteristics of the California Current System (CCS), validating them with in situ data from uncrewed Saildrone vessels. Despite biases and uncertainties in SSS and SST values in highly dynamic coastal areas, this T-S framework has proven useful in describing CCS regional surface properties and their variability in the past and in real time, at novel scales. This analysis also shows the capacity of remote sensing data for investigating variability in land–air–sea interactions not previously possible due to limited in situ data.

Marisol García-Reyes↗
NASA NTRSDate not supplied

Development of Shared Mounting Structure for SUSAN Aircraft Tail and Aft Engine

This paper introduces an overview of the design and development of the Subsonic Single Aft eNgine (SUSAN) aircraft aft tail and engine shared mounting structure. Unique design considerations were made here to take advantage of aerodynamic effects such as boundary layer ingestion of the aft mounted engine. Challenges that arose from sharing the same mounting structure with the T-tail and aft engine were addressed. The structural development process will be discussed for both the initial design concept for the full-scale SUSAN aircraft concept, as well as the higher fidelity development of the 25% scale research vehicle. Numerical simulations for the aerodynamic loads are detailed and were fed into the 25% scale structural analysis effort. Additionally, mockup component fabrications of the shared aft engine and tail mounting structure are also shown to demonstrate design feasibility.

Lilia R Glaser↗
DOE OSTI2014

Diverse Power Iteration Embeddings and Its Applications

Abstract—Spectral Embedding is one of the most effective dimension reduction algorithms in data mining. However, its computation complexity has to be mitigated in order to apply it for real-world large scale data analysis. Many researches have been focusing on developing approximate spectral embeddings which are more efficient, but meanwhile far less effective. This paper proposes Diverse Power Iteration Embeddings (DPIE), which not only retains the similar efficiency of power iteration methods but also produces a series of diverse and more effective embedding vectors. We test this novel method by applying it to various data mining applications (e.g. clustering, anomaly detection and feature selection) and evaluating their performance improvements. The experimental results show our proposed DPIE is more effective than popular spectral approximation methods, and obtains the similar quality of classic spectral embedding derived from eigen-decompositions. Moreover it is extremely fast on big data applications. For example in terms of clustering result, DPIE achieves as good as 95% of classic spectral clustering on the complex datasets but 4000+ times faster in limited memory environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Development of Residential Prototype Building Models and Analysis System for Large-Scale Energy Efficiency Studies Using EnergyPlus

ABSTRACT: Recent advances in residential building energy efficiency and codes have resulted in increased interest in detailed residential building energy models using the latest energy simulation software. One of the challenges of developing residential building models to characterize new residential building stock is to allow for flexibility to address variability in house features like geometry, configuration, HVAC systems etc. Researchers solved this problem in a novel way by creating a simulation structure capable of creating fully-functional EnergyPlus batch runs using a completely scalable residential EnergyPlus template system. This system was used to create a set of thirty-two residential prototype building models covering single- and multifamily buildings, four common foundation types and four common heating system types found in the United States (US). A weighting scheme with detailed state-wise and national weighting factors was designed to supplement the residential prototype models. The complete set is designed to represent a majority of new residential construction stock. The entire structure consists of a system of utility programs developed around the core EnergyPlus simulation engine to automate the creation and management of large-scale simulation studies with minimal human effort. The simulation structure and the residential prototype building models have been used for numerous large-scale studies, one of which is briefly discussed in this paper.

Mendon, Vrushali V.↗
DOE OSTI2017

ECoG Cluster Flow v1.0

ECoG ClusterFlow is a novel multi-scale visual analysis system for the detailed exploration of dynamic functional connectivity graphs obtained through electrocorticography.

MURUGESAN, SUGEERTH↗