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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 217 records · Page 12

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NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

NASA NTRS1983

Space shuttle launch vehicle aerodynamic uncertainties: Lessons learned

The chronological development and evolution of an uncertainties model which defines the complex interdependency and interaction of the individual Space Shuttle element and component uncertainties for the launch vehicle are presented. Emphasis is placed on user requirements which dictated certain concessions, simplifications, and assumptions in the analytical model. The use of the uncertainty model in the vehicle design process and flight planning support is discussed. The terminology and justification associated with tolerances as opposed to variations are also presented. Comparisons of and conclusions drawn from flight minus predicted data and uncertainties are given. Lessons learned from the Space Shuttle program concerning aerodynamic uncertainties are examined.

Hamilton, J. T.↗

NASA NTRSDate not supplied

The Nasa Multiscale Analysis Tool: an Enabling Platform for Achieving Vision 2040

Vision 2040 is a community-driven consensus document, written in 2018, aimed at defining the potential 25-year future state required for performing integrated multiscale modeling of materials and systems for future aerospace and aeronautical applications. Nine Vision Key Elements (KEs) were defined along with associated technical gaps. This paper will address current NASA GRC research efforts utilizing the NASA Multiscale Analysis Tool (NASMAT). This paper will specifically focus on NASMAT’s ability to address gaps in three of the nine Vision 2040 KEs: 1) Models and Methods, 2) Multiscale Measurements and Characterization Tools and Methods, and 6) Data, Informatics, and Visualization. NASMAT is a versatile platform for performing computationally efficient multiscale analyses of heterogeneous materials. NASMAT offers the user flexibility to define an arbitrary number of length scales (levels) where a variety of micromechanics theories can be implemented at each level. Micromechanics theories can be selected to balance accuracy and computational efficiency and range from analytical (Mori-Tanaka) to several semi-analytical (method of cells) formulations. NASMAT can also be coupled with external software and used to perform multiscale analyses of more complex structures. The paper will include a recent application of NASMAT to model a complex, three-dimensional woven composite, with a particular emphasis placed on multiscale measurements utilized to enhance the quality of the multiscale analysis. Since typical NASMAT analyses can be completed in on the order of seconds to minutes, a second example will demonstrate NASMAT’s ability to generate large quantities of data useful for sensitivity analysis, uncertainty quantification, or machine learning applications. Current progress on developing multiscale data visualization tools will also be addressed along with the challenges associated with and proposed solutions for sifting through large amounts of data. These examples will demonstrate that NASMAT is an enabling platform for achieving the goals in Vision 2040.

Vision 2040↗

NASA NTRS1992

What we learn about cooling flows through the study of the 10,000 K gas in clusters

The physical properties of the emission-line nebulae associated with central dominant galaxies in clusters are explored within the context of standard cooling-flow models of the hot ICM. It is pointed out that the properties of the 10,000 K gas are inconsistent with simple theories in which this gas has accreted out of the hot ICM in thermal instabilities within a standard cooling flow atmosphere. Several possible ramifications for our understanding of the properties of clusters and the hot intracluster medium are discussed.

Baum, Stefi A.↗

NASA NTRSDate not supplied

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

machine learning↗

NASA NTRS1982

Near surface observations

Learning objectives include: (1) developing an understanding of the implications of the term "near surface observations"; (2) associating the appearance of large ground features as seen in satellite imagery with their appearance as seen from the ground; (3) grasping criteria and procedures for selecting training sites on the ground for use in supervised classification; (4) running through an example of training site selection; (5) becoming familiar with several methods of accuracy assessment; (6) becoming aware of the approach and value of making supporting measurements of the spectral and physical properties of materials on the ground and from aircraft; (7) taking note of the different types of instruments used in making specific ground measurements; and (8) appreciating the rationale underlying laboratory and field studies on or near the Earth's surface for the purpose of developing new sensor systems.

Source record↗

NASA NTRS1992

The NASA/USRA ADP at the University of Central Florida

An approach to learning engineering design is discussed with particular attention given to the impact of the NASA/Universities Space Research Association (USRA) Advanced Design Program (ADP) on that process. Attention is also given to a teaching method stressing science discipline and creativity and various selected space related designs.

Anderson, L. A.↗

NASA NTRS2008

Spaceflight-induced Bone Loss: Countermeasures and Their Evaluations

The learning objectives of this viewgraph presentation are to: (1) Understand the biomedical effects of spaceflight and their associated health risks. (2) Understand how the "Factor of Risk" for fracture can be calculated in the adult skeleton for space travel. and (3) Understand how various countermeasures [CMs] influence the Factor of Risk.

Sibonga, Jean D.↗

NASA NTRS2011

Content Documents Management

The Content Documents are created and managed under the System Software group with. Launch Control System (LCS) project. The System Software product group is lead by NASA Engineering Control and Data Systems branch (NE~C3) at Kennedy Space Center. The team is working on creating Operating System Images (OSI) for different platforms (i.e. AIX, Linux, Solaris and Windows). Before the OSI can be created, the team must create a Content Document which provides the information of a workstation or server, with the list of all the software that is to be installed on it and also the set where the hardware belongs. This can be for example in the LDS, the ADS or the FR-l. The objective of this project is to create a User Interface Web application that can manage the information of the Content Documents, with all the correct validations and filters for administrator purposes. For this project we used one of the most excellent tools in agile development applications called Ruby on Rails. This tool helps pragmatic programmers develop Web applications with Rails framework and Ruby programming language. It is very amazing to see how a student can learn about OOP features with the Ruby language, manage the user interface with HTML and CSS, create associations and queries with gems, manage databases and run a server with MYSQL, run shell commands with command prompt and create Web frameworks with Rails. All of this in a real world project and in just fifteen weeks!

Muniz, R.↗

NASA NTRS2011

Managing Toxicological Risks: The Legacy of Shuttle Operations

Space toxicology greatly matured as a result of research and operations associated with the Shuttle. Materials offgassing had been a manageable concern since the Apollo days, but we learned to pay careful attention to compounds that could escape containment, to combustion events, to toxic propellants, to overuse of utility compounds, and to microbial and human metabolites. We also learned that flying real-time hardware to monitor air pollutants was a pathway with unanticipated speed bumps. Each new orbiter was tested for any excess offgassing products that could pollute the air during flight. In the late 1990s toxicologists and safety experts developed a 5-level toxicity rating system to guide containment of toxic compounds. This system is now in use aboard the International Space Station (ISS). Several combustion events during Shuttle Mir and also during Shuttle free-flight impelled toxicologists to identify hardware capable of monitoring toxic products; however, rapid adaptation of the hardware for the unique conditions of spaceflight caused unexpected missteps. Current and planned combustion analyzers would be useful to commercial partners that wish to manage the risk of health effects from thermal events. Propellants received special attention during the Shuttle program because of the possibility of bringing them into the habitable volume on extravehicular activity suits. Monitors for the airlocks were developed to mitigate this risk. Utility materials, such as lubricants, posed limited toxicological problems because water was not recovered. One clearly documented case of microbial metabolites polluting the Shuttle atmosphere was noted, and this has implications for commercial flights and control of microbes. Finally, carbon dioxide, the major human metabolite, episodically presented air quality problems aboard Shuttle, especially when nominal air flows were obstructed. Commercial vehicles must maintain robust air circulation given the anticipated high density of human occupants.

James, John T.↗

NASA NTRS2016

Brief History of Agricultural Systems Modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the next generation models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

agricultural systems↗

NASA NTRS2002

Lessons Learned and Technical Standards: A Logical Marriage for Future Space Systems Design

A comprehensive database of engineering lessons learned that corresponds with relevant technical standards will be a valuable asset to those engaged in studies on future space vehicle developments, especially for structures, materials, propulsion, control, operations and associated elements. In addition, this will enable the capturing of technology developments applicable to the design, development, and operation of future space vehicles as planned in the Space Launch Initiative. Using the time-honored tradition of passing on lessons learned while utilizing the newest information technology, NASA has launched an intensive effort to link lessons learned acquired through various Internet databases with applicable technical standards. This paper will discuss the importance of lessons learned, the difficulty in finding relevant lessons learned while engaged in a space vehicle development, and the new NASA effort to relate them to technical standards that can help alleviate this difficulty.

Gill, Paul S.↗

NASA NTRS2006

Intelligence for Human-Assistant Planetary Surface Robots

The central premise in developing effective human-assistant planetary surface robots is that robotic intelligence is needed. The exact type, method, forms and/or quantity of intelligence is an open issue being explored on the ERA project, as well as others. In addition to field testing, theoretical research into this area can help provide answers on how to design future planetary robots. Many fundamental intelligence issues are discussed by Murphy [2], including (a) learning, (b) planning, (c) reasoning, (d) problem solving, (e) knowledge representation, and (f) computer vision (stereo tracking, gestures). The new "social interaction/emotional" form of intelligence that some consider critical to Human Robot Interaction (HRI) can also be addressed by human assistant planetary surface robots, as human operators feel more comfortable working with a robot when the robot is verbally (or even physically) interacting with them. Arkin [3] and Murphy are both proponents of the hybrid deliberative-reasoning/reactive-execution architecture as the best general architecture for fully realizing robot potential, and the robots discussed herein implement a design continuously progressing toward this hybrid philosophy. The remainder of this chapter will describe the challenges associated with robotic assistance to astronauts, our general research approach, the intelligence incorporated into our robots, and the results and lessons learned from over six years of testing human-assistant mobile robots in field settings relevant to planetary exploration. The chapter concludes with some key considerations for future work in this area.

Hirsh, Robert↗

NASA NTRS2011

Lessons Learned in Engineering

This Contractor Report (CR) is a compilation of Lessons Learned in approximately 55 years of engineering experience by each James C. Blair, Robert S. Ryan, and Luke A. Schutzenhofer. The lessons are the basis of a course on Lessons Learned that has been taught at Marshall Space Flight Center. The lessons are drawn from NASA space projects and are characterized in terms of generic lessons learned from the project experience, which are further distilled into overarching principles that can be applied to future projects. Included are discussions of the overarching principles followed by a listing of the lessons associated with that principle. The lesson with sub-lessons are stated along with a listing of the project problems the lesson is drawn from, then each problem is illustrated and discussed, with conclusions drawn in terms of Lessons Learned. The purpose of this CR is to provide principles learned from past aerospace experience to help achieve greater success in future programs, and identify application of these principles to space systems design. The problems experienced provide insight into the engineering process and are examples of the subtleties one experiences performing engineering design, manufacturing, and operations.

Blair, J. C.↗

NASA NTRS2011

Lessons Learned in Engineering

Blair, James C.↗

NASA NTRS1986

Thin-sectioning and microanalysis of individual extraterrestrial particles

A long standing constraint on the study of micrometeorites has centered on difficulties in preparing them for analysis. This is due largely to their small dimensions and consequent practical limitations on sample manipulation. Chondritic micrometeorites provide a good example; although much has been learned about their chemistry and mineralogy almost nothing was known about such basic properties as texture and petrographic associations. The only way to assess such properties is to examine microstructure indigenous to the particles. Unfortunately, almost all micrometeorites, out of necessity, have been crushed and dispersed onto appropriate substances prior to analysis, and most information about texture and petrography was lost. Recently, thin-sections of individual extraterrestrial particles have been prepared using an ultramicrotome equipped with a diamond knife. This procedure has been applied to stratospheric micrometeorites and Solar Max impact debris. In both cases the sections have enabled observation of a variety of internal particle features, including textures, porosity, and petrographic associations. The sectioning procedure is described and analysis results for chondritic micrometeoroids and select particles from Solar Max are presented.

Bradley, J. P.↗

NASA NTRS2016

Fast Track to the Cloud: Design Patterns for 12-Factor Earth Sciences Applications

As expanding service offerings and decreasing prices make the cloud increasingly attractive to Earth Science applications, there are nontrivial practical considerations which can hinder its meaningful use. In this talk, we will discuss architectural recommendations and lessons learned while working on EOSDIS' cloud efforts, particularly the NASA-compliant General Application Platform (NGAP) and its associated applications. Prominent in our findings is the importance of 12-factor design patterns and the powerful "wins" they enable in the cloud. We will share our strategies for "fast-tracking" applications to the cloud --whether they be legacy, planned for the future, or somewhere in between.

cloud↗