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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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Development of space suits for long duration space missions to include wardrobe definition, fabric usage, laundry system concepts, wardrobe packaging, and crewman sizing
Technical data collected for the food management and personal hygiene appliances considered for the shuttle orbiter are presented as well as plotted and tabulated trade study results for each appliance. Food storage, food operation, galley cleanup, waste collection/transfer, body cleansing, and personal grooming were analyzed.
Technical data collected for housekeeping, off-duty activities, and medical appliances considered for the shuttle orbiter are presented. Equipment cleaning, refuse management, garment/linen maintenance, entertainment, physical conditioning, sterilization, and physical monitoring were analyzed.
Data collected for the appliances considered for the space station are presented along with plotted and tabulated trade study results for each appliance. The food management, and personal hygiene data are applicable to a six-man mission of 180-days.
Housekeeping, off-duty, and medical data concerning the appliances considered for the space station are presented. Appliance functions analyzed include: cleanup, collection, processing and storage of refuse; crew entertainment and physical exercise, and the autoclaves and ergometers.
General capabilities of data base management technology are described. Information requirements posed by the space station life cycle are discussed, and it is asserted that data base management technology supporting engineering/manufacturing in a heterogeneous hardware/data base management system environment should be applied to meeting these requirements. Today's commercial systems do not satisfy all of these requirements. The features of an R&D data base management system being developed to investigate data base management in the engineering/manufacturing environment are discussed. Features of this system represent only a partial solution to space station requirements. Areas where this system should be extended to meet full space station information management requirements are discussed.
An effective data collection method for evaluating software development methodologies and for studying the software development process is described. The method uses goal-directed data collection to evaluate methodologies with respect to the claims made for them. Such claims are used as a basis for defining the goals of the data collection, establishing a list of questions of interest to be answered by data analysis, defining a set of data categorization schemes, and designing a data collection form. Feasibility of the data collection methodology was demonstrated by applying it to five different projects in two different environments (other NRL Reports). The application showed that the methodology was both feasible and useful.
An effective data collection method for evaluating software development methodologies and for studying the software development process is described. The method uses goal-directed data collection to evaluate methodologies with respect to the claims made for them. Such claims are used as a basis for defining the goals of the data collection, establishing a list of questions of interest to be answered by data analysis, defining a set of data categorization schemes, and designing a data collection form. The data to be collected are based on the changes made to the software during development, and are obtained when the changes are made. To insure accuracy of the data, validation is performed concurrently with software development and data collection. Validation is based on interviews with those people supplying the data. Results from using the methodology show that data validation is a necessary part of change data collection. Without it, as much as 50% of the data may be erroneous. Feasibility of the data collection methodology was demonstrated by applying it to five different projects in two different environments. The application showed that the methodology was both feasible and useful.
A collection of scaling equations, weight statements, scaling factors, etc., useful for doing conceptual designs of spacecraft are given. Rules of thumb and methods of calculating quantities of interest are provided. Basic relationships for conventional, and several non-conventional, propulsion systems (nuclear, solar electric and solar thermal) are included. The equations and other data were taken from a number of sources and are not at all consistent with each other in level of detail or method, but provide useful references for early estimation purposes.
This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.
The various types of materials related data that exist at the NASA Marshall Space Flight Center and compiled into databases which could be accessed by all the NASA centers and by other contractors, are presented.
Abstract not provided.
Data were obtained in an altitude test chamber for a range of altitudes from 20,000 to 58,000 feet at a flight Mach number of 0.9, and for several flight Mach numbers at an altitude of 45,000 feet. Data approximating sea-level operation are also included. Engine component performance data are presented in addition to windmilling, exhaust-nozzle, and ejector performance.
An engineering evaluation of dynamic data from SSME hot firing tests and SSV flights is summarized. The basic objective of the study is to provide analyses of vibration, strain and dynamic pressure measurements in support of MSFC performance and reliability improvement programs. A brief description of the SSME test program is given and a typical test evaluation cycle reviewed. Data banks generated to characterize SSME component dynamic characteristics are described and statistical analyses performed on these data base measurements are discussed. Analytical models applied to define the dynamic behavior of SSME components (such as turbopump bearing elements and the flight accelerometer safety cut-off system) are also summarized. Appendices are included to illustrate some typical tasks performed under this study.
Software Engineering Laboratory Data Base Maintenance System (SEL/DBAM) collects and maintains data base for evaluating software-development methodologies. SEL/DBAM provides interactive facilities for management of data collected by Software Engineering Laboratory at Goddard Space Flight Center.
Aircraft gas-turbine engine data is available from a variety of sources, including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data fusion is the integration of data or information from multiple sources for the achievement of improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/ information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This report describes a basic PHM data fusion architecture being developed in alignment with the NASA C-17 PHM Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center, NASA Dryden Flight Research Center, and Pratt & Whitney have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion, as it applies to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This report will provide a chronology and summary of the work accomplished under this research contract.