SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Model Checking conducts an exhaustive exploration of all possible behaviors of a software system design and as such can be used to detect defects in designs that are typically difficult to discover with conventional testing approaches.
The need for the application of software fault tolerance techniques in digital flight control systems is argued to follow from the requirements derivable from the safety constraints of such systems, requirements which can be stated in terms of minimum acceptable system reliability levels and, moreover, stated quantitatively. It is argued further that, while fault tolerance appears to be a viable mechanism in general, individual fault tolerance schemes need to be analyzed to ensure that they are adequate to the task and being properly utilized, that such analysis is essentially an exercise in software 'reliability' estimation involving software characteristics not currently included in software 'reliability' modeling (most especially, the degree of correlation of malfunctions among redundant, dissimilar software modules), and that, consequently, further research and studies in the characterization of software behavior and malfunctions is required.
HARPS is a telerobot control system that can perform some simple but useful tasks. This capability is demonstrated by performing the ORU exchange demonstration. HARPS is based on NASREM (NASA Standard Reference Model). All software is developed in Ada, and the project incorporates a number of different CASE (computer-aided software engineering) tools. NASREM was found to be a valid and useful model for building a telerobot control system. Its hierarchical and distributed structure creates a natural and logical flow for implementing large complex robust control systems. The ability of Ada to create and enforce abstraction enhanced the implementation of such control systems.
Enhanced engineering tools can be obtained through the integration of expert system methodologies and existing design software. The application of these methodologies to the spacecraft design and cost model (SDCM) software provides an improved technique for the selection of hardware for unmanned spacecraft subsystem design. The knowledge engineering system (KES) expert system development tool was used to implement a smarter equipment section algorithm than that which is currently achievable through the use of a standard data base system. The guidance, navigation, and control subsystems of the SDCM software was chosen as the initial subsystem for implementation. The portions of the SDCM code which compute the selection criteria and constraints remain intact, and the expert system equipment selection algorithm is embedded within this existing code. The architecture of this new methodology is described and its implementation is reported. The project background and a brief overview of the expert system is described, and once the details of the design are characterized, an example of its implementation is demonstrated.
The IAS-Galaxy (IASG) model is a software interface between theoretical models of the Galaxy and observed kinematic distributions. It has been developed for analysis of many kinematic catalogs to study global galactic structure. In addition, the IASG model can be used to estimate corrections needed to derive absolute parallax and absolute proper motion by evaluating, on a star-by-star basis, the expected mean motion of the reference stars. A theoretical Galaxy model is defined on an inertial coordinate frame. Proper motions are measured in a reference frame defined by a fundamental catalog. The observed distribution of proper motions in star catalogs can be directly compared with the expected distributions evaluated using IASG to check the accuracy of the adopted reference frame in realizing the inertial coordinate frame in the sky.
The model correlation process for the modal characteristics of a launch vehicle is well established. After a test, parameters within the nominal model are adjusted to reflect structural dynamics revealed during testing. However, a full model correlation process for a complex structure can take months of man-hours and many computational resources. If the analyst only has weeks, or even days, of time in which to correlate the nominal model to the experimental results, then the traditional correlation process is not suitable. This paper describes using model dispersions to assist the model correlation process and decrease the overall cost of the process. The process creates thousands of model dispersions from the nominal model prior to the test and then compares each of them to the test data. Using mode shape and frequency error metrics, one dispersion is selected as the best match to the test data. This dispersion is further improved by using a commercial model correlation software. In the three examples shown in this paper, this dispersion based model correlation process performs well when compared to models correlated using traditional techniques and saves time in the post-test analysis.
Cheatgrass (Bromus tectorum) is a species of concern across the western United States as it has the potential to outcompete native plant species, reduce biodiversity, and diminish nutrient availability for ungulates. Furthermore, because cheatgrass can quickly dominate disturbed landscapes it has the potential to exacerbate wildfire risk by increasing fuel loads. In 2020, the Cameron Peak fire burned more than 200,000 acres on the Arapaho and Roosevelt National Forests in Colorado. These issues are of imminent concern for our partners at the Forest Service (USFS), as they are tasked with wildfire risk and invasive species mitigation. Disturbances such as wildfires can substantially increase the rate and extent of cheatgrass spread. Current cheatgrass mitigation methods rely on field crews to physically locate cheatgrass on the landscape, which takes time, money, and extensive manpower. Here, we developed two Random Forest models within the Software for Assisted Habitat Modeling (SAHM) using remote sensing predictors derived from Sentinel-2 MultiSpectral Instrument (MSI) and Shuttle Radar Topography Mission (SRTM). The first model identified suitable cheatgrass habitat while the other detected cheatgrass presence during the 2021 growing season. Topographic variables were found to be the most important in driving the habitat suitability model. Cheatgrass detection was also found to be possible within a short timespan with limited imagery surrounding a phenological shift of the plant. Maps produced from these models provide natural resource managers the ability to implement early detection and rapid response to prevent the spread of cheatgrass to new locations.
Over vast oceans and remote continents, observations are often scarce and discontinuous. Satellite and model data play a critical role in research and applications. However, finding and accessing satellite and model data can be a daunting task for many, especially those outside the community. The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC), one of 12 NASA Science Mission Directorate Data Centers, provides Earth science data, information, and services to everyone such as researchers, application users, educators, and students. GES DISC archives and supports datasets applicable to several NASA Earth Science Focus Areas including Atmospheric Composition, Water & Energy Cycles, Carbon Cycle & Ecosystem, and Climate Variability. To facilitate data discovery, evaluation, and exploration, GES DISC has developed the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni), an online tool to analyze and visualize NASA remote sensing and model data without downloading data and software. As of this writing, over 2000 Earth satellite and model variables are available in Giovanni, including several wellknown NASA satellite missions (e.g., TRMM, GPM) and projects (e.g., MERRA-2, GPCP). Giovanni provides twenty-two plots that can be used to analyze, compare, and explore Earth data across different disciplines. Results can be shared with colleagues and downloaded for further analysis. Over the years, Giovanni has helped publish over 3000 referral papers. In this presentation, we will showcase key variables and plot types in Giovanni with examples. In particular, we will present several popular precipitation products from GPM and CPCP for evaluation and comparison.
A cryofreezer system is being evaluated as a new method of compressing and storing carbon dioxide (CO2) in an Advanced Life Support (ALS) Environmental Control and Life Support System (ECLSS). A cryocooler is used to provide cold temperatures and heat removal while CO2 freezes and accumulates around a coldtip. The CO2 can then be stored as a liquid or high-pressure gas after it has been accumulated. This system was originally conceived as an In-Situ Resource Utilization (ISRU) application for collecting CO2 from the Mars atmosphere to be converted to methane fuel with a Sabatier reaction. In the ALS application, this system could collect CO2 from the International Space Station (ISS) Carbon Dioxide Removal Assembly (CDRA) for delivery to the Sabatier reactor. The Sabatier reaction is an important part of proposed Air Revitalization System (ARS) for ALS, and technology sharing is often possible between ISRU and ARS applications in CO2 processing systems. A prototype system developed and initially tested by Lockheed Martin Astronautics is now being evaluated in the Air Revitalization Technology Evaluation Facility (ARTEF) at NASA Johnson Space Center (JSC). This paper will discuss testing conducted through December 2004 to examine the performance and capacity of the system under a variety of input conditions. A simulation of the system was developed simultaneously using the Aspen Custom Modeler (ACM) software package. Several approaches using varying levels of detail could be used when modeling the system, and this paper will discuss the assumptions and choices made in this simulation, as well as the validity of the simulation for predicting performance of the prototype unit.
A computational fluid dynamics (CFD) model is developed to simulate pressure control of an ellipsoidal-shaped liquid hydrogen tank under external heating in normal gravity. Pressure control is provided by an axial jet thermodynamic vent system (TVS) centered within the vessel that injects cooler liquid into the tank, mixing the contents and reducing tank pressure. The two-phase cryogenic tank model considers liquid hydrogen in its own vapor with liquid density varying with temperature only and a fully compressible ullage. The axisymmetric model is developed using a custom version of the commercially available FLOW-31) software. Quantitative model validation is ,provided by engineering checkout tests performed at Marshall Space Flight Center in 1999 in support of the Solar Thermal Upper Stage_ Technology Demonstrator (STUSTD) program. The engineering checkout tests provide cryogenic tank self-pressurization test data at various heat leaks and tank fill levels. The predicted self-pressurization rates, ullage and liquid temperatures at discrete locations within the STUSTD tank are in good agreement with test data. The work presented here advances current CFD modeling capabilities for cryogenic pressure control and helps develop a low cost CFD-based design process for space hardware.
This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance.
Explore the source record for details and available documents.
This report documents the work accomplished during the first two years of research to provide support to NASA in predicting operational and support parameters and costs of proposed space systems. The first year's research developed a methodology for deriving reliability and maintainability (R & M) parameters based upon the use of regression analysis to establish empirical relationships between performance and design specifications and corresponding mean times of failure and repair. The second year focused on enhancements to the methodology, increased scope of the model, and software improvements. This follow-on effort expands the prediction of R & M parameters and their effect on the operations and support of space transportation vehicles to include other system components such as booster rockets and external fuel tanks. It also increases the scope of the methodology and the capabilities of the model as implemented by the software. The focus is on the failure and repair of major subsystems and their impact on vehicle reliability, turn times, maintenance manpower, and repairable spares requirements. The report documents the data utilized in this study, outlines the general methodology for estimating and relating R&M parameters, presents the analyses and results of application to the initial data base, and describes the implementation of the methodology through the use of a computer model. The report concludes with a discussion on validation and a summary of the research findings and results.
In the traditional 'waterfall' model of the software project life cycle, the Requirements Phase ends and flows into the Design Phase, which ends and flows into the Development Phase. Unfortunately, the process rarely, if ever, works so smoothly in practice. Instead, software developers often receive new requirements, or modifications to the original requirements, well after the earlier project phases have been completed. In particular, projects with shorter than ideal schedules are highly susceptible to frequent requirements changes, as the software requirements analysis phase is often forced to begin before the overall system requirements and top-level design are complete. This results in later modifications to the software requirements, even though the software design and development phases may be complete. Requirements changes received in the later stages of a software project inevitably lead to modification of existing developed software. Presented here is a series of software design techniques that can greatly reduce the impact of last-minute requirements changes. These techniques were successfully used to add built-in flexibility to two complex software systems in which the requirements were expected to (and did) change frequently. These large, real-time systems were developed at NASA Langley Research Center (LaRC) to test and control the Lidar In-Space Technology Experiment (LITE) instrument which flew aboard the space shuttle Discovery as the primary payload on the STS-64 mission.
The Lunar Atmosphere and Dust Environment Explorer (LADEE) spacecraft was launched on September 6, 2013, and completed its mission on April 17, 2014 with a directed impact to the Lunar Surface. Its primary goals were to examine the lunar atmosphere, measure lunar dust, and to demonstrate high rate laser communications. The LADEE mission was a resounding success, achieving all mission objectives, much of which can be attributed to careful planning and preparation. This paper discusses some of the highlights from the mission, and then discusses the techniques used for developing the onboard Flight Software. A large emphasis for the Flight Software was to develop it within tight schedule and cost constraints. To accomplish this, the Flight Software team leveraged heritage software, used model based development techniques, and utilized an automated test infrastructure. This resulted in the software being delivered on time and within budget. The resulting software was able to meet all system requirements, and had very problems in flight.
The objective was to introduce the basic terminology and concepts of software engineering and Ada. The life cycle model is reviewed. The application of the goals and principles of software engineering is applied. An introductory understanding of the features of the Ada language is gained. Topics addressed include: the software crises; the mandate of the Space Station Program; software life cycle model; software engineering; and Ada under the software engineering umbrella.
One of the major concerns of any maintenance organization is to understand and estimate the cost of maintenance releases of software systems. Planning the next release so as to maximize the increase in functionality and the improvement in quality are vital to successful maintenance management. The objective of this paper is to present the results of a case study in which an incremental approach was used to better understand the effort distribution of releases and build a predictive effort model for software maintenance releases. This study was conducted in the Flight Dynamics Division (FDD) of NASA Goddard Space Flight Center(GSFC). This paper presents three main results: 1) a predictive effort model developed for the FDD's software maintenance release process; 2) measurement-based lessons learned about the maintenance process in the FDD; and 3) a set of lessons learned about the establishment of a measurement-based software maintenance improvement program. In addition, this study provides insights and guidelines for obtaining similar results in other maintenance organizations.
NASA's software engineering and Ada skill base are assessed and information that may result in new models for software engineering, Ada training plans, and curricula are provided. A quantitative assessment which reflects the requirements for software engineering and Ada training across NASA is provided. A recommended implementation plan including a suggested curriculum with associated duration per course and suggested means of delivery is also provided. The distinction between education and training is made. Although it was directed to focus on NASA's need for the latter, the key relationships to software engineering education are also identified. A rationale and strategy for implementing a life cycle education and training program are detailed in support of improved software engineering practices and the transition to Ada.