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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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NASA NTRS2011

Mission Operations Planning and Scheduling System (MOPSS)

MOPSS is a generic framework that can be configured on the fly to support a wide range of planning and scheduling applications. It is currently used to support seven missions at Goddard Space Flight Center (GSFC) in roles that include science planning, mission planning, and real-time control. Prior to MOPSS, each spacecraft project built its own planning and scheduling capability to plan satellite activities and communications and to create the commands to be uplinked to the spacecraft. This approach required creating a data repository for storing planning and scheduling information, building user interfaces to display data, generating needed scheduling algorithms, and implementing customized external interfaces. Complex scheduling problems that involved reacting to multiple variable situations were analyzed manually. Operators then used the results to add commands to the schedule. Each architecture was unique to specific satellite requirements. MOPSS is an expert system that automates mission operations and frees the flight operations team to concentrate on critical activities. It is easily reconfigured by the flight operations team as the mission evolves. The heart of the system is a custom object-oriented data layer mapped onto an Oracle relational database. The combination of these two technologies allows a user or system engineer to capture any type of scheduling or planning data in the system's generic data storage via a GUI.

Wood, Terri↗
NASA NTRS2012

Innovative Technologies for Global Space Exploration

Under the direction of NASA's Exploration Systems Mission Directorate (ESMD), Directorate Integration Office (DIO), The Tauri Group with NASA's Technology Assessment and Integration Team (TAIT) completed several studies and white papers that identify novel technologies for human exploration. These studies provide technical inputs to space exploration roadmaps, identify potential organizations for exploration partnerships, and detail crosscutting technologies that may meet some of NASA's critical needs. These studies are supported by a relational database of more than 400 externally funded technologies relevant to current exploration challenges. The identified technologies can be integrated into existing and developing roadmaps to leverage external resources, thereby reducing the cost of space exploration. This approach to identifying potential spin-in technologies and partnerships could apply to other national space programs, as well as international and multi-government activities. This paper highlights innovative technologies and potential partnerships from economic sectors that historically are less connected to space exploration. It includes breakthrough concepts that could have a significant impact on space exploration and discusses the role of breakthrough concepts in technology planning. Technologies and partnerships are from NASA's Technology Horizons and Technology Frontiers game-changing and breakthrough technology reports as well as the External Government Technology Dataset, briefly described in the paper. The paper highlights example novel technologies that could be spun-in from government and commercial sources, including virtual worlds, synthetic biology, and human augmentation. It will consider how these technologies can impact space exploration and will discuss ongoing activities for planning and preparing them.

Hay, Jason↗
NASA NTRS2011

From Science to e-Science to Semantic e-Science: A Heliosphysics Case Study

The past few years have witnessed unparalleled efforts to make scientific data web accessible. The Semantic Web has proven invaluable in this effort; however, much of the literature is devoted to system design, ontology creation, and trials and tribulations of current technologies. In order to fully develop the nascent field of Semantic e-Science we must also evaluate systems in real-world settings. We describe a case study within the field of Heliophysics and provide a comparison of the evolutionary stages of data discovery, from manual to semantically enable. We describe the socio-technical implications of moving toward automated and intelligent data discovery. In doing so, we highlight how this process enhances what is currently being done manually in various scientific disciplines. Our case study illustrates that Semantic e-Science is more than just semantic search. The integration of search with web services, relational databases, and other cyberinfrastructure is a central tenet of our case study and one that we believe has applicability as a generalized research area within Semantic e-Science. This case study illustrates a specific example of the benefits, and limitations, of semantically replicating data discovery. We show examples of significant reductions in time and effort enable by Semantic e-Science; yet, we argue that a "complete" solution requires integrating semantic search with other research areas such as data provenance and web services.

Narock, Thomas↗
NASA NTRS2014

Physics Based Model for Cryogenic Chilldown and Loading. Part IV: Code Structure

This is the fourth report in a series of technical reports that describe separated two-phase flow model application to the cryogenic loading operation. In this report we present the structure of the code. The code consists of five major modules: (1) geometry module; (2) solver; (3) material properties; (4) correlations; and finally (5) stability control module. The two key modules - solver and correlations - are further divided into a number of submodules. Most of the physics and knowledge databases related to the properties of cryogenic two-phase flow are included into the cryogenic correlations module. The functional form of those correlations is not well established and is a subject of extensive research. Multiple parametric forms for various correlations are currently available. Some of them are included into correlations module as will be described in details in a separate technical report. Here we describe the overall structure of the code and focus on the details of the solver and stability control modules.

cryogenic fluid management↗
NASA NTRS2016

The IPAC Image Subtraction and Discovery Pipeline for the Intermediate Palomar Transient Factory

We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, bogus candidates from processing artifacts and imperfect image subtractions outnumber real transients by approximately equal to 10:1. This can be considerably higher for image data with inaccurate astrometric and/or PSF-matching solutions. Despite this occasionally high contamination rate, the ML classifier is able to identify real transients with an efficiency (or completeness) of approximately equal to 97% for a maximum tolerable false-positive rate of 1% when classifying raw candidates. All subtraction-image metrics, source features, ML probability-based real-bogus scores, contextual metadata from other surveys, and possible associations with known Solar System objects are stored in a relational database for retrieval by the various science working groups. We review our efforts in mitigating false-positives and our experience in optimizing the overall system in response to the multitude of science projects underway with iPTF.

methods: analytical – methods: data analysis –↗
NASA NTRS2019

Quantitative Metrics from 20 Years of Terra Data Usage

NASA's Terra flagship satellite carries five Earth-observing instruments that have collected data for almost 20 years. NASA's Earth Science Data and Information System (ESDIS) Project makes these data, along with derived products, available to worldwide data users. Since the launch of Terra on December 18, 1999, more than 10,000 data products have been archived and distributed by NASA-funded Distributed Active Archive Centers (DAACs) that are part of NASA's Earth Observing System Data and Information System (EOSDIS). At the end of the 2018 Fiscal Year, about 1,000 Terra data products constituted almost 22% of the entire EOSDIS data archive volume (6 PB out of approximately 27.5 PB), and 6 PB of Terra data were distributed to over half-a-million public users worldwide.By categorizing the Terra data products and their distribution, we can get a quantitative assessment of Terra data usage. NASA's ESDIS Project has collected archive, distribution, and user information from EOSDIS data users since February 2000. These metrics are available through the ESDIS Metrics System (EMS). EMS information is stored in a relational database from which quantitative metrics of Terra data use can be retrieved and analyzed.The purposes of this study are to: 1) perform a comprehensive investigation of the 20-year trend in the archive and distribution of Terra data products; 2) identify and characterize data product usage over the last 20 years; and 3) identify and characterize the global user community for these data. In addition to revealing how Terra data use has evolved over time, the results of this study provide insights on identifying the various user communities for different kinds of Earth science data products. Also, because of the enormous quantity of data handled by EOSDIS DAACs, the study provides guidance of the requirements for future data systems that will be needed to effectively and efficiently handle the ever-increasing amounts of Earth science data produced by future (and ongoing) Earth science missions.

Wanchoo, Lalit↗
NASA NTRS2019

Using Arcane Data: Past Performance in NASA Science’s Principal Investigator (PI) Development Programs and Improving Future Peer Review for Space Flight Missions

This panel will explore challenges and initial findings from three qualitative studies that use the National Aeronautics and Space Administration's (NASA) Science Mission Directorate's(SMD) internal administrative data to begin to assess program effectiveness and document outputs and outcomes. SMD has funded both at and outside NASA, projects that promotethe development of SMD's future principal investigators (PI) workforce and science and technology investigations on space flight missions that advance the high priority science,technology, and exploration objectives. NASA's internal administrative data are arcane because they developed outside of relational databases. The studies reveal unique challenges to locating and using these data to document effective program processes, i.e.,peer review criteria for large flight missions, and results from the PI-development projects,e.g. the Hands-On Project Experience (HOPE) Training Opportunity solicitations limited to NASA Centers the Jet Propulsion Laboratory; and graduate student/postdoctoral opportunities at higher education institutions.

Sladek, Mary Frances↗
NASA NTRS2018

Applications of the Dynamic N-Dimensional K-Vector

The n-dimensional k-vector (NDKV) is an appealing alternative to binary tress for resolving complex queries in large relational databases. The method has excelled in several applications involving static databases. The present paper extends the theory supporting the NDKV to handle dynamic databases, where the data is updated frequently. This includes deleting records, adding new entries, or editing existing elements. The merit of this new version of the NDKV, the dynamic n-dimensional k-vector (DNDKV), is that it is no longer necessary to recompute the entire k-vector (the main structure that indexes the data) every time a record changes. The algorithm updates the four constituents of the standard NDKV on the fly: the database, sorted database, index, and k-vector tables. As a result, the DNDKV becomes comparable in terms of capabilities and flexibility to stateof-the-art storage engines relying on structured query languages (SQL). The performance of the DNDKV is assessed by running typical read/write operations on a database that contains millions of pre-computed missions to celestial bodies. This database requires frequent updates whenever an orbit solution is refined or new bodies are discovered. The DNDKV is faster than rebuilding the k-vector tables completely, provided that the number of elements being added or removed is not excessively large. Direct runtime comparisons with MySQL suggest that the DNDKV is several times faster for reading but might be slower for writing and updating the database. One limit of the technique is the elements being added must be within the range of the current k-vector tables. If this is not the case, the technique cannot be used and the k-vector tables must be rebuilt from scratch.

Mortari, Daniele↗
NASA NTRS2021

A Survey of CubeSat Deployable Structures: The First Decade

In the past decade CubeSats have made their way into the spotlight. They have evolved from small, university educational opportunities, to industry and governments using them make new discoveries and monetize space. However, with the small, constrained CubeSat form factor; there is often a need to expand the CubeSat through deployable mechanisms once the satellite is in space. This paper is a survey of deployable structures and their actuating mechanisms for CubeSats. The goal of this paper is to provide the applications within which deployable structures have been used in the past for CubeSats, the mechanisms with regards to how they deploy, the lessons learned, and limitations of the various types of deployables. The inputs to this paper come from a relational database in development to track launched CubeSat missions with deployable structures. From this database we can find insightful trends. This paper specifically focuses on the first decade of CubeSat deployables, from 2000 to 2010.

Arya, Manan↗
NASA NTRS2021

The Io GIS Database 1.0: A Proto-Io Planetary Spatial Data Infrastructure

We collected a set of published, higher-order data products of Jupiterʼs volcanic moon Io and assembled them in an ArcGISTM database we are calling the Io GIS Database, version 1.0. The purpose of this database is to collect image, topographic, geologic, and thermal emission data of Io in one geospatially registered location to form the data component of an Io planetary spatial data infrastructure (PSDI). The goals of an Io PSDI are (1) to make higher-order data products more accessible and usable to the broader planetary science community, particularly to new scientists that were not associated with the projects that obtained the data; (2) to enable new scientific studies with the data; and (3) to create a tool to support observation planning for future Io-focused planetary missions. In this paper we describe the motivation behind our project, discuss the data sets acquired for this first version of the database, and demonstrate how they can be used. We conclude with a discussion of how our database relates to other PSDIs, our plans for future updates, and a request for additional Io data sets.

David A Williams↗
NASA NTRSDate not supplied

Quantitative Highlights of 20 years Aqua Data Archive and Data Usage

NASA’s Aqua satellite carries six Earth-observing instruments Atmospheric Infrared Sounder (AIRS), Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Advanced Microwave Sounding Unit (AMSU), Clouds and the Earth’s Radiant Energy System (CERES), Humidity Sounder for Brazil (HSB) and Moderate Resolution Imaging Spectroradiometer (MODIS). Currently only four of six instruments are collecting data, two instruments that stopped transmitting data are AMSR-E that suffered a major anomaly in October 2011 and was powered off in March 2016 while as HSB failed in February 2003. NASA’s Earth Science Data and Information System (ESDIS) Project makes these data, along with derived products, available to worldwide data users. Since the launch of Aqua on May 4, 2002, more than 10,000 data products have been archived and distributed by NASA-funded Distributed Active Archive Centers (DAACs) that are part of NASA’s Earth Observing System Data and Information System (EOSDIS). At the end of the 2021 Fiscal Year with over 100,000 orbits data, about 1,000 Aqua data products constituted almost 16.5 % of the entire EOSDIS data archive volume (8.6 PB out of approximately 55.2 PB), and 7.5 PB of Aqua data were distributed to over half-a-million public users worldwide. By categorizing the Aqua data products and their distribution, we can get a quantitative assessment of Aqua data usage. NASA’s ESDIS Project has collected archive, distribution, and user information from EOSDIS data users since February 2000. These metrics are available through the ESDIS Metrics System (EMS). EMS information is stored in a relational database from which quantitative metrics of Aqua data use can be retrieved and analyzed. The purposes of this study are to: 1) perform a comprehensive investigation of the 20-year trend in the archive and distribution of Aqua data products; 2) identify and characterize data product usage over the last 20 years; and 3) identify and characterize the global user community for these data. In addition to revealing how Aqua data use has evolved over time, the results of this study provide insights on identifying the various user communities for different kinds of Earth science data products. Also, because of the enormous quantity of data handled by EOSDIS DAACs, the study provides guidance of the requirements for future data systems that will be needed to effectively and efficiently handle the ever-increasing amounts of Earth science data produced by future (and ongoing) Earth science missions.

Lalit Wanchoo↗
NASA NTRSDate not supplied

The GeneLab Buffet: A Bioinformatic MATRIX of MANGO and TOAST

The GeneLab data repository provides an unparalleled resource for exploring how spaceflight affects organisms with omics-level insights. However, two major interlinked challenges to capitalizing on the information within these data are their vast breadth and the often-specialized expertise that has been required in the past for their analysis. How do you compare responses within and between studies, especially if you are a non-bioinformatics specialist? This presentation will discuss how Space Biology data can be accessed using software to help provide these data resources to address research questions and generate new hypotheses. The presentation will cover a wide range of the available space life science tools but will focus on TOAST, MANGO, the MATRIX, RadBioApp and other interactive relational databases (https://genelab.nasa.gov/external-vis-apps). These exploration environments have been developed to search the GeneLab data repository for new insights that inform how model organisms respond to microgravity, radiation and other factors associated with spaceflight. The presentation will be interactive, and participants will have the opportunity to ask questions and learn more about the data viz and modeling tools that are available to them.

AstroBotany↗
NASA NTRSDate not supplied

NASA EOSDIS 20 Years of Data Usage and User Assessment in Support of Open Science Initiative

NASA EOS Data and Information System (EOSDIS) has been distributing data to world-wide users free with open access. Since the launch of NASA’s Terra satellite in 1999, more than 10,000 distinct EOS data products have been archived and distributed by NASA-funded Earth Science data centers encompassed by the EOSDIS. As of September 30, 2022, more than 90 PB of data archived by EOSDIS have been made available to public users and during FY 2023 over 60 PB have been distributed to public users worldwide. Over these twenty and more years, it has shown significant increase in the distribution of various data products. This has been possible due to free and open access of the data thereby a step towards open science initiative. The purposes of this study are 1) to perform a comprehensive investigation of the archive and distribution patterns of EOSDIS data products for last 20 years, 2) to identify and characterize the global user community for those data, 3) analyze the increased demand for data products, 4) evaluate distribution of higher level products because those are the ones most frequently used in the studies of natural disasters by public users (those data requestors not involved directly in the production or validation of the data products.) and contribute globally to the advance scientific understanding of the Earth-Atmosphere Systems. Funded by the Earth Science Data and Information System (ESDIS) Project, the ESDIS Metrics System (EMS) collects archive, distribution, and user information from EOSDIS data centers. The information (comprising all data products including heritage datasets going back to the 1990s) is stored in a relational database from which it can be analyzed in many ways. We present several metrics analyses that include data distribution patterns for all, as well as the most frequently requested data products; and user characterizations by country, domain, and Earth Science discipline (e.g., Land, Ocean, Cryosphere) of the requested products. Due to the enormous quantity of data handled by EOSDIS data centers and requirements of future data systems to archive increasing amounts of Earth Science data from future and current Earth Science missions effectively, the results of this study can provide insight on how the user communities have accessed the data and provide guidance for open science initiative.

Lalit Wanchoo↗
NASA NTRS2011

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykhian, Gholam Ali↗
NASA NTRS2011

Improve Data Mining and Knowledge Discovery through the use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykahian, Gholan Ali↗
NASA NTRS1991

Performance related issues in distributed database systems

The key elements of research performed during the year long effort of this project are: Investigate the effects of heterogeneity in distributed real time systems; Study the requirements to TRAC towards building a heterogeneous database system; Study the effects of performance modeling on distributed database performance; and Experiment with an ORACLE based heterogeneous system.

Mukkamala, Ravi↗
NASA NTRSDate not supplied

NASA’s Comprehensive Databases for Materials Selection (MAPTIS) and Low-Gravity Experiments (PSI)

In the realm of advancing technological change the convergence of materials science and scientific inquiry stands as a testament to humanity’s insatiable curiosity. To assist in this endeavor the National Aeronautics and Space Administration (NASA) provides curated access to two unique databases. Physical Sciences Informatics (PSI) is an online database that houses completed physical science reduced-gravity experiments. Whereas Materials and Processes Technical Information System (MAPTIS) contains several other databases that relate to aerospace materials and processes. Equipped with curated access to these databased provided by the NASA scientists and engineers are furnished with invaluable resources needed to propel technological change.

PSI↗