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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 109 records · Page 6

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NASA NTRS1993

Tensoral: A system for post-processing turbulence simulation data

Many computer simulations in engineering and science -- and especially in computational fluid dynamics (CFD) -- produce huge quantities of numerical data. These data are often so large as to make even relatively simple post-processing of this data unwieldy. The data, once computed and quality-assured, is most likely analyzed by only a few people. As a result, much useful numerical data is under-utilized. Since future state-of-the-art simulations will produce even larger datasets, will use more complex flow geometries, and will be performed on more complex supercomputers, data management issues will become increasingly cumbersome. My goal is to provide software which will automate the present and future task of managing and post-processing large turbulence datasets. My research has focused on the development of these software tools -- specifically, through the development of a very high-level language called 'Tensoral'. The ultimate goal of Tensoral is to convert high-level mathematical expressions (tensor algebra, calculus, and statistics) into efficient low-level programs which numerically calculate these expressions given simulation datasets. This approach to the database and post-processing problem has several advantages. Using Tensoral the numerical and data management details of a simulation are shielded from the concerns of the end user. This shielding is carried out without sacrificing post-processor efficiency and robustness. Another advantage of Tensoral is that its very high-level nature lends itself to portability across a wide variety of computing (and supercomputing) platforms. This is especially important considering the rapidity of changes in supercomputing hardware.

Dresselhaus, Eliot↗

NASA NTRS1987

Simplifying the construction of domain-specific automatic programming systems: The NASA automated software development workstation project

An overview is presented of the Automated Software Development Workstation Project, an effort to explore knowledge-based approaches to increasing software productivity. The project focuses on applying the concept of domain specific automatic programming systems (D-SAPSs) to application domains at NASA's Johnson Space Center. A version of a D-SAPS developed in Phase 1 of the project for the domain of space station momentum management is described. How problems encountered during its implementation led researchers to concentrate on simplifying the process of building and extending such systems is discussed. Researchers propose to do this by attacking three observed bottlenecks in the D-SAPS development process through the increased automation of the acquisition of programming knowledge and the use of an object oriented development methodology at all stages of the program design. How these ideas are being implemented in the Bauhaus, a prototype workstation for D-SAPS development is discussed.

Allen, Bradley P.↗

NASA NTRS1988

Simplifying the construction of domain-specific automatic programming systems: The NASA automated software development workstation project

An overview is presented of the Automated Software Development Workstation Project, an effort to explore knowledge-based approaches to increasing software productivity. The project focuses on applying the concept of domain specific automatic programming systems (D-SAPSs) to application domains at NASA's Johnson Space Flight Center. A version of a D-SAPS developed in Phase 1 of the project for the domain of space station momentum management is described. How problems encountered during its implementation led researchers to concentrate on simplifying the process of building and extending such systems is discussed. Researchers propose to do this by attacking three observed bottlenecks in the D-SAPS development process through the increased automation of the acquisition of programming knowledge and the use of an object oriented development methodology at all stages of the program design. How these ideas are being implemented in the Bauhaus, a prototype workstation for D-SAPS development is discussed.

Allen, Bradley 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 NaÃ 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

Automated daily processing of more than 1000 ground-based GPS receivers for studying intense ionospheric storms

To take advantage of the vast amount of GPS data, researchers use a number of techniques to estimate satellite and receiver interfrequency biases and the total electron content (TEC) of the ionosphere. Most techniques estimate vertical ionospheric structure and, simultaneously, hardware-related biases treated as nuisance parameters. These methods often are limited to 200 GPS receivers and use a sequential least squares or Kalman filter approach. The biases are later removed from the measurements to obtain unbiased TEC. In our approach to calibrating GPS receiver and transmitter interfrequency biases we take advantage of all available GPS receivers using a new processing algorithm based on the Global Ionospheric Mapping (GIM) software developed at the Jet Propulsion Laboratory. This new capability is designed to estimate receiver biases for all stations. We solve for the instrumental biases by modeling the ionospheric delay and removing it from the observation equation using precomputed GIM maps. The precomputed GIM maps rely on 200 globally distributed GPS receivers to establish the ''background'' used to model the ionosphere at the remaining 800 GPS sites.

total electron content (TEC)↗

NASA NTRS2016

Autonomous Operations System: Development and Application

Autonomous control systems provides the ability of self-governance beyond the conventional control system. As the complexity of mechanical and electrical systems increases, there develops a natural drive for developing robust control systems to manage complicated operations. By closing the bridge between conventional automated systems to knowledge based self-awareness systems, nominal control of operations can evolve into relying on safe critical mitigation processes to support any off-nominal behavior. Current research and development efforts lead by the Autonomous Propellant Loading (APL) group at NASA Kennedy Space Center aims to improve cryogenic propellant transfer operations by developing an automated control and health monitoring system. As an integrated systems, the center aims to produce an Autonomous Operations System (AOS) capable of integrating health management operations with automated control to produce a fully autonomous system.

Autonomous Control Systems↗

NASA NTRSDate not supplied

Linear Regression Model for Predictive Service Provider Selection

The increasing number of satellites in orbit has led to a growing reliance on third-party service providers for data transfer between Earth and space. Traditional approaches to managing satellite communications require human intervention, which becomes more burdensome with the escalating number of satellites. This research addresses the need for an efficient and automated system to optimize service provider selection for NASA space communication. Previous research has utilized human-operated approaches for service provider management. Our study fills a gap by developing a cognitive algorithm that automates and optimizes the selection process based on various parameters, such as data volume, priority, quality of service and cost. This novel solution reduces user burden, facilitates service management, and contributes to the development of cognitive spaceflight missions, ultimately supporting NASA’s research into Cognitive Communications technology. The algorithm design consists of three major steps: modeling data, developing a Link Selection Algorithm (LSA) based on a grading system, and applying machine learning using linear regression. The LSA evaluates providers based on user-defined constraints, considering factors such as delivery time, cost, and quality of service. We define a suitability metric which allows our algorithm to make a recommendation to a user regarding which commercial service providers to select. The addition of Linear Regression predicts the future suitability value. Our main findings demonstrate that the resulting algorithm can autonomously manage connections between satellites and providers, maximizing communication channel efficiency. This research has significant implications, as it not only addresses a pressing issue in satellite communication management but also advances the field of cognitive spaceflight missions.

Linear regression↗

NASA NTRS1989

Centers for the commercial development of space

In 1985, NASA initiated an innovative effort called Centers for the Commercial Development of Space (CCDS). The CCDS program was designed to increase private-sector interest and investment in space-related activities, while encouraging U.S. economic leadership and stimulating advances in promising areas of research and development. Research conducted in the Centers handling the following areas is summarized: materials processing; life sciences; remote sensing; automation and robotics; space propulsion; space structures and materials; and space power.

Walker, Susan E.↗

NASA NTRS1991

Industry-university cooperation/research

The paper concentrates on the commercial development of space programs through cooperative research with the U.S. universities and industry. The origins of the programs are discussed, beginning with the Communication Satellite Act of 1963. The National Space Policy is outlined, and the creation of NASA's Office of Commercial Programs is emphasized, along with its Centers for the Commercial Development of Space. It is noted that the centers are consortia of university, industry, and government involved in commercial-space-technology database development and research and testing of potentially valuable products and services. The center titles, locations, and brief descriptions for such area of research as remote sensing, life sciences, materials processing, space power, space propulsion, materials and space structures, and automation and robotics centers are listed, along with some results of the programs.

Whitten, Raymond P.↗

NASA NTRS2005

The Optimization of Trained and Untrained Image Classification Algorithms for Use on Large Spatial Datasets

The HARVIST project seeks to automatically provide an accurate, interactive interface to predict crop yield over the entire United States. In order to accomplish this goal, large images must be quickly and automatically classified by crop type. Current trained and untrained classification algorithms, while accurate, are highly inefficient when operating on large datasets. This project sought to develop new variants of two standard trained and untrained classification algorithms that are optimized to take advantage of the spatial nature of image data. The first algorithm, harvist-cluster, utilizes divide-and-conquer techniques to precluster an image in the hopes of increasing overall clustering speed. The second algorithm, harvistSVM, utilizes support vector machines (SVMs), a type of trained classifier. It seeks to increase classification speed by applying a "meta-SVM" to a quick (but inaccurate) SVM to approximate a slower, yet more accurate, SVM. Speedups were achieved by tuning the algorithm to quickly identify when the quick SVM was incorrect, and then reclassifying low-confidence pixels as necessary. Comparing the classification speeds of both algorithms to known baselines showed a slight speedup for large values of k (the number of clusters) for harvist-cluster, and a significant speedup for harvistSVM. Future work aims to automate the parameter tuning process required for harvistSVM, and further improve classification accuracy and speed. Additionally, this research will move documents created in Canvas into ArcGIS. The launch of the Mars Reconnaissance Orbiter (MRO) will provide a wealth of image data such as global maps of Martian weather and high resolution global images of Mars. The ability to store this new data in a georeferenced format will support future Mars missions by providing data for landing site selection and the search for water on Mars.

Kocurek, Michael J.↗

NASA NTRS1989

Automated Heat-Flux-Calibration Facility

Computer control speeds operation of equipment and processing of measurements. New heat-flux-calibration facility developed at Lewis Research Center. Used for fast-transient heat-transfer testing, durability testing, and calibration of heat-flux gauges. Calibrations performed at constant or transient heat fluxes ranging from 1 to 6 MW/m2 and at temperatures ranging from 80 K to melting temperatures of most materials. Facility developed because there is need to build and calibrate very-small heat-flux gauges for Space Shuttle main engine (SSME).Includes lamp head attached to side of service module, an argon-gas-recirculation module, reflector, heat exchanger, and high-speed positioning system. This type of automated heat-flux calibration facility installed in industrial plants for onsite calibration of heat-flux gauges measuring fluxes of heat in advanced gas-turbine and rocket engines.

Liebert, Curt H.↗

NASA NTRS1995

Automated Target Acquisition, Recognition and Tracking (ATTRACT)

The primary objective of phase 1 of this research project is to conduct multidisciplinary research that will contribute to fundamental scientific knowledge in several of the USAF critical technology areas. Specifically, neural networks, signal processing techniques, and electro-optic capabilities are utilized to solve problems associated with automated target acquisition, recognition, and tracking. To accomplish the stated objective, several tasks have been identified and were executed.

Abdallah, Mahmoud A.↗

NASA NTRS2008

The Wide-Field Imaging Interferometry Testbed (WIIT): Recent Progress and Results

Continued research with the Wide-Field Imaging Interferometry Testbed (WIIT) has achieved several important milestones. We have moved WIIT into the Advanced Interferometry and Metrology (AIM) Laboratory at Goddard, and have characterized the testbed in this well-controlled environment. The system is now completely automated and we are in the process of acquiring large data sets for analysis. In this paper, we discuss these new developments and outline our future research directions. The WIIT testbed, combined with new data analysis techniques and algorithms, provides a demonstration of the technique of wide-field interferometric imaging, a powerful tool for future space-borne interferometers.

Rinehart, Stephen A.↗

NASA NTRS1996

Mission simulation as an approach to develop requirements for automation in Advanced Life Support Systems

This paper examines mission simulation as an approach to develop requirements for automation and robotics for Advanced Life Support Systems (ALSS). The focus is on requirements and applications for command and control, control and monitoring, situation assessment and response, diagnosis and recovery, adaptive planning and scheduling, and other automation applications in addition to mechanized equipment and robotics applications to reduce the excessive human labor requirements to operate and maintain an ALSS. Based on principles of systems engineering, an approach is proposed to assess requirements for automation and robotics using mission simulation tools. First, the story of a simulated mission is defined in terms of processes with attendant types of resources needed, including options for use of automation and robotic systems. Next, systems dynamics models are used in simulation to reveal the implications for selected resource allocation schemes in terms of resources required to complete operational tasks. The simulations not only help establish ALSS design criteria, but also may offer guidance to ALSS research efforts by identifying gaps in knowledge about procedures and/or biophysical processes. Simulations of a planned one-year mission with 4 crewmembers in a Human Rated Test Facility are presented as an approach to evaluation of mission feasibility and definition of automation and robotics requirements.

NASA Discipline Number 61-10↗

NASA NTRS1990

Research into language concepts for the mission control center

A final report is given on research into language concepts for the Mission Control Center (MCC). The Specification Driven Language research is described. The state of the image processing field and how image processing techniques could be applied toward automating the generation of the language known as COmputation Development Environment (CODE or Comp Builder) are discussed. Also described is the development of a flight certified compiler for Comps.

Dellenback, Steven W.↗

NASA NTRS2012

Integrated Design Tools Reduce Risk, Cost

Thanks in part to a SBIR award with Langley Research Center, Phoenix Integration Inc., based in Wayne, Pennsylvania, modified and advanced software for process integration and design automation. For NASA, the tool has resulted in lower project costs and reductions in design time; clients of Phoenix Integration are experiencing the same rewards.

Source record↗

NASA NTRS2013

Data Quality Screening Service

A report describes the Data Quality Screening Service (DQSS), which is designed to help automate the filtering of remote sensing data on behalf of science users. Whereas this process often involves much research through quality documents followed by laborious coding, the DQSS is a Web Service that provides data users with data pre-filtered to their particular criteria, while at the same time guiding the user with filtering recommendations of the cognizant data experts. The DQSS design is based on a formal semantic Web ontology that describes data fields and the quality fields for applying quality control within a data product. The accompanying code base handles several remote sensing datasets and quality control schemes for data products stored in Hierarchical Data Format (HDF), a common format for NASA remote sensing data. Together, the ontology and code support a variety of quality control schemes through the implementation of the Boolean expression with simple, reusable conditional expressions as operands. Additional datasets are added to the DQSS simply by registering instances in the ontology if they follow a quality scheme that is already modeled in the ontology. New quality schemes are added by extending the ontology and adding code for each new scheme.

Strub, Richard↗

NASA NTRSDate not supplied

Digital Assistance for System Requirement Discovery and Analysis using Machine Learning Natural Language Processing Algorithm

NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future (see https://www.nasa.gov/uam-overview/ for more information). The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and machine learning natural language processing (ML NLP, or just NLP) capabilities. This presentation gives an overview of the NLP application within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

ATM↗