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NASA NTRS2017

Cloud Surprises in Moving NASA EOSDIS Applications into Amazon Web Services

NASA ESDIS has been moving a variety of data ingest, distribution, and science data processing applications into a cloud environment over the last 2 years. As expected, there have been a number of challenges in migrating primarily on-premises applications into a cloud-based environment, related to architecture and taking advantage of cloud-based services. What was not expected is a number of issues that were beyond purely technical application re-architectures. We ran into surprising network policy limitations, billing challenges in a government-based cost model, and difficulty in obtaining certificates in an NASA security-compliant manner. On the other hand, this approach has allowed us to move a number of applications from local hosting to the cloud in a matter of hours (yes, hours!!), and our CMR application now services 95% of granule searches and an astonishing 99% of all collection searches in under a second. And most surprising of all, well, you'll just have to wait and see the realization that caught our entire team off guard!

Cloud↗

NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗

NASA NTRSDate not supplied

Additive Manufacturing: Disrupting Global Supply Chains and Enabling Sustainable Development

The integration of new materials and innovative manufacturing technologies into product supply chains are critically needed to address human and societal needs and to promote sustainable development and economic competitiveness. Recently, there has been tremendous growth in the additive manufacturing (AM) landscape with the introduction of high- end machines suitable for industrial applications. In addition, availability of desktop 3-D printers as well as open source printers and platforms have also facilitated the large scale growth of distributed manufacturing. The paradigm shift in thinking, where one can turn their design into product on demand, is leading to new business models and challenging traditional models of product development and distribution. In this presentation, an overview of different AM technologies will be provided along with technical challenges and opportunities. Various examples of materials (polymers, ceramics, metals, hybrids, and multi material systems) and structures achieved from utilizing a wide variety of additive manufacturing approaches will be provided. Technical challenges and opportunities for the utilization of additive manufacturing as a powerful enabler for sustainable development and disruptive technological threat to global supply chains for different materials and systems will be presented.

Additive Manufacturing↗

NASA NTRS2002

Systems Integration, Analysis and Modeling Support to the HEDS Technology/Commercialization Initiative (HTCI)

In response to a recommendation from OMB, NASA's Fiscal Year 2001 budget included a new program within the HEDS (Human Exploration and Development of Space) Enterprise called HEDS Technology/ Commercialization Initiative (HTCI). HTCI had three overarching goals: to support REDS analysis and planning for safe, affordable and effective future programs and projects that advance human exploration, scientific discovery, and the commercial development of space; to pursue research, development, and validation of breakthrough technologies and highly innovative systems concepts; and to advance die creation of strong partnerships within NASA, with U.S. industry and universities, and internationally. As part of its contracted effort, SAIC was to write a report contribution, describing die results of its task activities, to a final HTCI report prepared by MSFC. Unfortunately, government cancellation of the HTCI program in the summer of 2001 curtailed all efforts on the program including die Final HTCI report. In the absence of that report, SAIC has issued this final report in an attempt to document some of the technical material it produced. The report contains SAIC presentations for both HTCI workshops; a set of roadmap charts for the Systems Analysis, Integration and Modeling; and charts showing the evolution of the current TITAN modeling architecture.

Feingold, Harvey↗

NASA NTRS2010

Modeling Flight: The Role of Dynamically Scaled Free-Flight Models in Support of NASA's Aerospace Programs

The state of the art in aeronautical engineering has been continually accelerated by the development of advanced analysis and design tools. Used in the early design stages for aircraft and spacecraft, these methods have provided a fundamental understanding of physical phenomena and enabled designers to predict and analyze critical characteristics of new vehicles, including the capability to control or modify unsatisfactory behavior. For example, the relatively recent emergence and routine use of extremely powerful digital computer hardware and software has had a major impact on design capabilities and procedures. Sophisticated new airflow measurement and visualization systems permit the analyst to conduct micro- and macro-studies of properties within flow fields on and off the surfaces of models in advanced wind tunnels. Trade studies of the most efficient geometrical shapes for aircraft can be conducted with blazing speed within a broad scope of integrated technical disciplines, and the use of sophisticated piloted simulators in the vehicle development process permits the most important segment of operations the human pilot to make early assessments of the acceptability of the vehicle for its intended mission. Knowledgeable applications of these tools of the trade dramatically reduce risk and redesign, and increase the marketability and safety of new aerospace vehicles. Arguably, one of the more viable and valuable design tools since the advent of flight has been testing of subscale models. As used herein, the term "model" refers to a physical article used in experimental analyses of a larger full-scale vehicle. The reader is probably aware that many other forms of mathematical and computer-based models are also used in aerospace design; however, such topics are beyond the intended scope of this document. Model aircraft have always been a source of fascination, inspiration, and recreation for humans since the earliest days of flight. Within the scientific community, Leonardo da Vinci, George Cayley, and the Wright brothers are examples of early aviation pioneers who frequently used models during their scientific efforts to understand and develop flying machines. Progress in the technology associated with model testing in worldwide applications has firmly established model aircraft as a key element in new aerospace research and development programs. Models are now routinely used in many applications and roles, including aerodynamic data gathering in wind tunnel investigations for the analysis of full-scale aircraft designs, proof-of-concept demonstrators for radical aeronautical concepts, and problem-solving exercises for vehicles already in production. The most critical contributions of aerospace models are to provide confidence and risk reduction for new designs and to enhance the safety and efficiency of existing configurations.

Chambers, Joseph↗

NASA NTRS2008

Multi-Evaporator Miniature Loop Heat Pipe for Small Spacecraft Thermal Control

This paper presents the development of the Thermal Loop experiment under NASA's New Millennium Program Space Technology 8 (ST8) Project. The Thermal Loop experiment was originally planned for validating in space an advanced heat transport system consisting of a miniature loop heat pipe (MLHP) with multiple evaporators and multiple condensers. Details of the thermal loop concept, technical advances and benefits, Level 1 requirements and the technology validation approach are described. An MLHP breadboard has been built and tested in the laboratory and thermal vacuum environments, and has demonstrated excellent performance that met or exceeded the design requirements. The MLHP retains all features of state-of-the-art loop heat pipes and offers additional advantages to enhance the functionality, performance, versatility, and reliability of the system. In addition, an analytical model has been developed to simulate the steady state and transient operation of the MHLP, and the model predictions agreed very well with experimental results. A protoflight MLHP has been built and is being tested in a thermal vacuum chamber to validate its performance and technical readiness for a flight experiment.

Ku, Jentung↗

NASA NTRSDate not supplied

Implementation of a SysML Model-Based Concept of Operations for Level of Care IV: Long-Duration Lunar Orbital and Surface Operations

One goal of the Human Research Program (HRP) Exploration Medical Capability (ExMC) Element Systems Engineering (SE) team is to define the technical system needed to support crew medical system capabilities for future exploration missions, including orbital and surface operations for long duration lunar missions. This is accomplished through the development of a Medical System Foundation, which communicates medical system requirements, capabilities, conditions, and resources that define a starting point for a medical system that meets the specification for design reference missions and associated Levels of Care (as defined by NASA-STD-3001). The starting point for creating this Medical System Foundation Model is the development of a Concept of Operations (ConOps) that describes the operation of the system from the point of view of the users. It includes a comprehensive and thoroughly vetted list of the users, their specific needs, the goals of the system, key assumptions about the system, and definitions of the system’s operational environments. The use case scenarios included in the ConOps illustrate required medical system capabilities for Level of Care IV and enable the ExMC SE team to develop integrated medical system requirements and identify capabilities required to meet those requirements. For this “Long Duration” Medical System Foundation development effort, ExMC replaced the traditional document-based ConOps with a model-based ConOps using model-based systems engineering. This approach has several advantages, including the facilitation of more efficient understanding of the material through information-dense images with less opportunity for misinterpretation than text alone, the roll-out of changes to the ConOps to stakeholders in real-time as they are approved, and the consolidation of all salient information into one centralized location. This discussion will focus on both how the model represents the ConOps content and how the SE team utilized lessons learned in an agile environment to improve the way this information was created and presented to stakeholders.

M Kaetzer↗

NASA NTRSDate not supplied

Implementation of a SysML Model-Based Concept of Operations for Level of Care IV: Long-Duration Lunar Orbital and Surface Operations

Mary Susan Kaetzer↗

NASA NTRS2010

Ares I-X Flight Evaluation Tasks in Support of Ares I Development

NASA s Constellation Program successfully launched the Ares I-X Flight Test Vehicle on October 28, 2009. The Ares I-X flight was a development flight test that offered a unique opportunity for early engineering data to impact the design and development of the Ares I crew launch vehicle. As the primary customer for flight data from the Ares I-X mission, the Ares Projects Office established a set of 33 flight evaluation tasks to correlate fight results with prospective design assumptions and models. Included within these tasks were direct comparisons of flight data with pre-flight predictions and post-flight assessments utilizing models and modeling techniques being applied to design and develop Ares I. A discussion of the similarities and differences in those comparisons and the need for discipline-level model updates based upon those comparisons form the substance of this paper. The benefits of development flight testing were made evident by implementing these tasks that used Ares I-X data to partially validate tools and methodologies in technical disciplines that will ultimately influence the design and development of Ares I and future launch vehicles. The areas in which partial validation from the flight test was most significant included flight control system algorithms to predict liftoff clearance, ascent, and stage separation; structural models from rollout to separation; thermal models that have been updated based on these data; pyroshock attenuation; and the ability to predict complex flow fields during time-varying conditions including plume interactions.

Huebner, Lawrence D.↗

NASA NTRS2020

SISO Space Reference FOM - Tools and Testing

The Simulation Interoperability Standards Organization (SISO) Space Reference Federation Object Model (SpaceFOM) version 1.0 is nearing completion. Earlier papers have described the use of the High Level Architecture (HLA) in Space simulation as well as technical aspects of the SpaceFOM. This paper takes a look at different SpaceFOM tools and how they were used during the development and testing of the standard.The first organizations to develop SpaceFOM-compliant federates for SpaceFOM development and testing were NASA's Johnson Space Center (JSC), the University of Calabria (UNICAL), and Pitch Technologies.JSC is one of NASA's lead centers for human space flight. Much of the core distributed simulation technology development, specifically associated with the SpaceFOM, is done by the NASA Exploration Systems Simulations (NExSyS) team. One of NASA's principal simulation development tools is the Trick Simulation Environment. NASA's NExSyS team has been modifying and using Trick and TrickHLA to help develop and test the SpaceFOM.The System Modeling And Simulation Hub Laboratory (SMASH-Lab) at UNICAL has developed the Simulation Exploration Experience (SEE) HLA Starter kit, that has been used by most SEE teams involved in the distributed simulation of a Moon base. It is particularly useful for the development of federates that are compatible with the SpaceFOM. The HLA Starter Kit is a Java based tool that provides a well-structured framework to simplify the formulation, generation, and execution of SpaceFOM-compliant federates.Pitch Technologies, a company specializing in distributed simulation, is utilizing a number of their existing HLA tools to support development and testing of the SpaceFOM. In addition to the existing tools, Pitch has developed a few SpaceFOM specific federates: Space Master for managing the initialization, execution and pacing of any SpaceFOM federation; EarthEnvironment, a simple Root Reference Publisher; and Space Monitor, a graphical tool for monitoring reference frames and physical entities.Early testing of the SpaceFOM was carried out in the SEE university outreach program, initiated in SISO. Students were given a subset of the FOM, that was later extended. Sample federates were developed and frameworks were developed or adapted to the early FOM versions.As drafts of the standard matured, testing was performed using federates from government, industry, and academia. By mixing federates developed by different teams the standard could be tested with respect to functional correctness, robustness and clarity.These frameworks and federates have been useful when testing and verifying the design of the standard. In addition to this, they have since formed a starting point for developing SpaceFOM-compliant federations in several projects, for example for NASA, ESA as well as SEE.

Möller, Björn↗

NASA NTRS2006

Zero to Integration in Eight Months, the Dawn Ground Data System Engineering Challange

The Dawn Project has presented the Ground Data System (GDS) with technical challenges driven by cost and schedule constraints commonly associated with National Aeronautics and Space Administration (NASA) Discovery Projects. The Dawn mission consists of a new and exciting Deep Space partnership among: the Jet Propulsion Laboratory (JPL), responsible for project management and flight operations; Orbital Sciences Corporation (OSC), spacecraft builder and responsible for flight system test and integration; and the University of California, at Los Angeles (UCLA), responsible for science planning and operations. As a cost-capped mission, one of Dawn s implementation strategies is to leverage from both flight and ground heritage. OSC's ground data system is used for flight system test and integration as part of the flight heritage strategy. Mission operations, however, are to be conducted with JPL s ground system. The system engineering challenge of dealing with two heterogeneous ground systems emerged immediately. During the first technical interchange meeting between the JPL s GDS Team and OSC's Flight Software Team, August 2003, the need to integrate the ground system with the flight software was brought to the table. This need was driven by the project s commitment to enable instrument engineering model integration in a spacecraft simulator environment, for both demonstration and risk mitigation purposes, by April 2004. This paper will describe the system engineering approach that was undertaken by JPL's GDS Team in order to meet the technical challenge within a non-negotiable eight-month schedule. Key to the success was adherence to an overall systems engineering process and fundamental systems engineering practices: decomposition of the project request into manageable requirements; definition of a structured yet flexible development process; integration of multiple ground disciplines and experts into a focused team effort; in-process risk management; and aggregation of the intermediate products to an integrated final product. In addition, this paper will highlight the role of lessons learned from the integration experience. The lessons learned from an early GDS deployment have served as the foundation for the design and implementation of the Dawn Ground Data System.

systems engineering↗

NASA NTRS2006

Zero to Integration in Eight Months, the Dawn Ground Data System Engineering Challenge

The Dawn Project has presented the Ground Data System (GDS) with technical challenges driven by cost and schedule constraints commonly associated with National Aeronautics and Space Administration (NASA) Discovery Projects. The Dawn mission consists of a new and exciting Deep Space partnership among: the Jet Propulsion Laboratory (JPL), manages the project and is responsible for flight operation; Orbital Sciences Corporation (OSC), is the spacecraft builder and is responsible for flight system test and integration; and the University of California, at Los Angeles (UCLA), is responsible for science planning and operations. As a cost-capped mission, one of Dawn's implementation strategies is to leverage from both flight and ground heritage. OSC's ground data system is used for flight system test and integration as part of the flight heritage strategy. Mission operations, however, are to be conducted with JPL's ground system. The system engineering challenge of dealing with two heterogeneous ground systems emerged immediately. During the first technical interchange meeting between the JPL's GDS Team and OSC's Flight Software Team, August 2003, the need to integrate the ground system with the flight software was brought to the table. This need was driven by the project's commitment to enable instrument engineering model integration in a spacecraft simulator environment, for both demonstration and risk mitigation purposes, by April 2004. This paper will describe the system engineering approach that was undertaken by JPL's GDS Team in order to meet the technical challenge within a non-negotiable eight-month schedule. Key to the success was adherence to fundamental systems engineering practices: decomposition of the project request into manageable requirements; integration of multiple ground disciplines and experts into a focused team effort; definition of a structured yet flexible development process; definition of an in-process risk reduction plan; and aggregation of the intermediate products to an integrated final product. In addition, this paper will highlight the role of lessons learned from the integration experience. The lessons learned from an early GDS deployment have served as the foundation for the design and implementation of the Dawn Ground Data System.

Ground Data System (GDS)↗

NASA NTRS2014

Loads and Structural Dynamics Requirements for Spaceflight Hardware

The purpose of this document is to establish requirements relating to the loads and structural dynamics technical discipline for NASA and commercial spaceflight launch vehicle and spacecraft hardware. Requirements are defined for the development of structural design loads and recommendations regarding methodologies and practices for the conduct of load analyses are provided. As such, this document represents an implementation of NASA STD-5002. Requirements are also defined for structural mathematical model development and verification to ensure sufficient accuracy of predicted responses. Finally, requirements for model/data delivery and exchange are specified to facilitate interactions between Launch Vehicle Providers (LVPs), Spacecraft Providers (SCPs), and the NASA Technical Authority (TA) providing insight/oversight and serving in the Independent Verification and Validation role. In addition to the analysis-related requirements described above, a set of requirements are established concerning coupling phenomena or other interaction between structural dynamics and aerodynamic environments or control or propulsion system elements. Such requirements may reasonably be considered structure or control system design criteria, since good engineering practice dictates consideration of and/or elimination of the identified conditions in the development of those subsystems. The requirements are included here, however, to ensure that such considerations are captured in the design space for launch vehicles (LV), spacecraft (SC) and the Launch Abort Vehicle (LAV). The requirements in this document are focused on analyses to be performed to develop data needed to support structural verification. As described in JSC 65828, Structural Design Requirements and Factors of Safety for Spaceflight Hardware, implementation of the structural verification requirements is expected to be described in a Structural Verification Plan (SVP), which should describe the verification of each structural item for the applicable requirements. The requirement for and expected contents of the SVP are defined in JSC 65828. The SVP may also document unique verifications that meet or exceed these requirements with Technical Authority approval.

George James↗

NASA NTRS1971

Methodologies for optimal resource allocation to the national space program and new space utilizations. Volume 2: Resource allocation and smoothing model, programmer's manual

Appendixes are presented that provide model input requirements, a sample case, flow charts, and a program listing. At the beginning of each appendix, descriptive details and technical comments are provided to indicate any special instructions applicable to the use of that appendix. In addition, the program listing includes comment cards that state the purpose of each subroutine in the complete program and describe operations performed within that subroutine. The input requirements includes details on the many options that adapt the program to the specific needs of the analyst for a particular problem.

Source record↗

NASA NTRS1973

Phase 1 of the earth resources data analysis program

Research completed in the Earth Resources Data Analysis Program is discussed along with recommendations for future study. Projects discussed include use of the Cholesky decomposition in feature selection and classification algorithms; optimal feature selection and extraction, probability density estimation and nonparametric classifiers; use of spatial information in classification; and model for crop row reflectance. The installation of LARSYS on the ICSA's IBM 370/155 is discussed, and a list of technical reports is included.

Source record↗

NASA NTRS1979

Air pollution from aircraft

Forty-one annotated abstracts of reports generated at MIT and the University of Sheffield are presented along with summaries of the technical projects undertaken. Work completed includes: (1) an analysis of the soot formation and oxidation rates in gas turbine combustors, (2) modelling the nitric oxide formation process in gas turbine combustors, (3) a study of the mechanisms causing high carbon monoxide emissions from gas turbines at low power, (4) an analysis of the dispersion of pollutants from aircraft both around large airports and from the wakes of subsonic and supersonic aircraft, (5) a study of the combustion and flow characteristics of the swirl can modular combustor and the development and verification of NO sub x and CO emissions models, (6) an analysis of the influence of fuel atomizer characteristics on the fuel-air mixing process in liquid fuel spray flames, and (7) the development of models which predict the stability limits of fully and partially premixed fuel-air mixtures.

Heywood, J. B.↗

NASA NTRS1991

Center for Space Power, Texas A and M University

Johnson Controls is a 106 year old company employing 42,000 people worldwide with $4.7 billion annual sales. Though we are new to the aerospace industry we are a world leader in automobile battery manufacturing, automotive seating, plastic bottling, and facilities environment controls. The battery division produces over 24,000,000 batteries annually under private label for the new car manufacturers and the replacement market. We are entering the aerospace market with the nickel hydrogen battery with the help of NASA's Center for Space Power at Texas A&M. Unlike traditional nickel hydrogen battery manufacturers, we are reaching beyond the space applications to the higher volume markets of aircraft starting and utility load leveling. Though space applications alone will not provide sufficient volume to support the economies of scale and opportunities for statistical process control, these additional terrestrial applications will. For example, nickel hydrogen batteries do not have the environmental problems of nickel cadmium or lead acid and may someday start your car or power your electric vehicle. However you envision the future, keep in mind that no manufacturer moves into a large volume market without fine tuning their process. The Center for Space Power at Texas A&M is providing indepth technical analysis of all of the materials and fabricated parts of our battery as well as thermal and mechanical design computer modeling. Several examples of what we are doing with nickel hydrogen chemistry to lead to these production efficiencies are presented.

Jones, Ken↗

NASA NTRS1993

NASA/DoD Aerospace Knowledge Diffusion Research Project. Paper 31: The information-seeking behavior of engineers

Engineers are an extraordinarily diverse group of professionals, but an attribute common to all engineers is their use of information. Engineering can be conceptualized as an information processing system that must deal with work-related uncertainty through patterns of technical communications. Throughout the process, data, information, and tacit knowledge are being acquired, produced, transferred, and utilized. While acknowledging that other models exist, we have chosen to view the information-seeking behavior of engineers within a conceptual framework of the engineer as an information processor. This article uses the chosen framework to discuss information-seeking behavior of engineers, reviewing selected literature and empirical studies from library and information science, management, communications, and sociology. The article concludes by proposing a research agenda designed to extend our current, limited knowledge of the way engineers process information.

Pinelli, Thomas E.↗