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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.
A memory system of a data processing system includes one or more storage devices and a data rearrangement engine for moving data between memory regions of the plurality of memory regions. The data rearrangement engine is configured to rearrange data stored at non-contiguous addresses in a source memory region into contiguous address in a destination region responsive to a rearrangement specified by a host processing unit of the data processing system. A description of the rearranged data is maintained in a metadata memory region. Rearranged data may be accessed by one or more host processing units. Write-back of data from the destination to the source region may be reduced by use of Bloom filter or the like.
Yarrowia lipolytica , an oleaginous yeast, shows promise for industrial fermentation due to its robust acetyl-CoA flux and well-developed genetic engineering tools. However, its lack of an active xylose metabolism restricts the conversion of cellulosic sugars to valuable products. To address this, metabolic engineering, and adaptive laboratory evolution (ALE) were applied to the Y. lipolytica PO1f strain, resulting in an efficient xylose-assimilating strain (XEV). Whole-genome sequencing (WGS) of the XEV followed by reverse engineering revealed that the amplification of the heterologous oxidoreductase pathway and a mutation in the GTPase-activating protein gene (YALI0B12100g) might be the primary reasons for improved xylose assimilation in the XEV strain. When a sorghum hydrolysate was used, the XEV strain showed superior xylose consumption and lipid production compared to its parental strain (X123). This study advances our understanding of xylose metabolism in Y. lipolytica and proposes effective metabolic engineering strategies for optimizing lignocellulosic hydrolysates.
The entry of graphical engineering data into the Space Station Data Base was examined. Discussed were: representation of graphics objects; representation of connectivity data; graphics capture hardware; graphics display hardware; site-wide distribution of graphics, and consolidation of tools and hardware. A fundamental assumption was that existing equipment such as IBM based graphics capture software and VAX networked facilities would be exploited. Defensible conclusions reached after study and simulations of use of these systems at the engineering level are: (1) existing IBM based graphics capture software is an adequate and economical means of entry of schematic and block diagram data for present and anticipated electronic systems for Space Station; (2) connectivity data from the aforementioned system may be incorporated into the envisioned Space Station Data Base with modest effort; (3) graphics and connectivity data captured on the IBM based system may be exported to the VAX network in a simple and direct fashion; (4) graphics data may be displayed site-wide on VT-125 terminals and lookalikes; (5) graphics hard-copy may be produced site-wide on various dot-matrix printers; and (6) the system may provide integrated engineering services at both the engineering and engineering management level.
The Readiness Engine for Data Integration (REDI) is an open-source framework for automating, standardizing, and assessing the process of preparing scientific data for AI training. REDI implements a five-stage pipeline (ingest, preprocess, transform, structure, output) with per-stage provenance instrumentation via Flowcept, domain-aware transformation logic (PII anonymization, regridding, graph encoding, and more), and built-in readiness assessment and validation modes. REDI has been evaluated across climate, proteomics, materials science, and nuclear fusion datasets, demonstrating near-ideal parallel scaling to 100 nodes on OLCF's Frontier system. REDI is deployable as an agent-callable skill in coding environments such as Claude Code and OpenAI Codex, and is complemented by SetGo for FAIR compliance and catalog publication.
This paper presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. If the standard deviation of the data falls below defined constraints, the engine is assumed to be at a steady-state operating point, and the mean measurement data at that point are archived for subsequent condition monitoring purposes. The fundamental design of the steady-state data filter is completely generic and applicable for any dynamic system. Additional domain-specific logic constraints are applied to reduce data outliers and variance within the collected steady-state data. The filter is designed for on-line real-time processing of streaming data as opposed to post-processing of the data in batch mode. Results of applying the steady-state data filter to recorded helicopter engine flight data are shown, demonstrating its utility for engine condition monitoring applications.
This report provides an analysis of engineering and geologic data collected from the Hydraulic Fracturing Test Site #1 (HTSF-1) project in the southern Midland Basin, Reagan County, Texas. The site is being studied as part of the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative at the National Energy Technology Laboratory (NETL). The data collected is intended to provide a basis of understanding of the site and to construct simulation models. This report provides a summary analysis of the engineering and geologic data collected from the project to provide a basis of understanding of the site and to construct simulation models. The report examines various possible correlations in engineering properties based on natural fracture data from the program, which consisted of 11 horizontal wells, one vertical well, and one slant well. The horizontal wells are in two horizons: 1) Upper and 2) Middle Wolfcamp formations. Program data include: fracture frequency and fracture orientation data from four core runs in a slant well; fracture spacing and orientation from a vertical pilot well; laboratory triaxial testing and mineralogical determinations; and porosity results from magnetic resonance analyses from various wells, together with observations based on the data collection. In addition, available references were reviewed on the site for additional insights. As the focus of the report is on the natural system, hydraulic fracture data from the site were not examined in detail in this report. Data variability is the chief observation in examination of the database. Fracture frequency in the slant well can range from sections with values as high as five fractures per ft to sections up to 100+ ft in length with no natural observed fractures. Fracture spacing across is typically less than 10 ft, but can range up to hundreds of feet. Apparent fracturing shows the trends in two predominate orientations, E-SW and WNW-ESE, but minor variations exist. The rock units vary across the site from siliceous mudstones to calcareous mudstones, showing a general layering with depth. The laboratory properties such as strength and modulus show no apparent trend with depth, but appear to correlate with rock mineralogy with high strength and modulus values where calcium content is high. In addition, an attempt to examine variability and mineralogy on a larger scale was made using a color-coded system based on gamma ray measurements. A staged colored approach was adopted, presuming that lower gamma ray values indicate higher value of calcium content (blue scale) and that higher gamma ray values indicate higher clay mineral content (orange scale). As provided in report appendices, the system correlated well with visual examination of the slant core and the petrofabric analyses of the vertical pilot well. The results showed large variability in mineral content along the horizontal plane across the site. The change in mineral content was also rapid, on a scale less than that of the average hydraulic fracture stage length of about 180 ft.
Acoustic tests of a subscale fan and a subscale mixer nozzle were conducted in anechoic chambers over a variety of operating conditions. The subscale fan test was an investigation into the effects of vane/blade ratio and spacing on fan generated noise. A turbulence control structure (TCS) was used to simulate the 'turbulence-free' condition in flight. The subscale mixer nozzle test investigated the acoustic properties of several different forced mixer designs. A tertiary flow was utilized on the mixer model to simulate the forward velocity effects on the jet. The results were scaled up to full size conditions and compared with measured engine data. The comparisons showed good agreement between the component scaled model results and the full scale engine data.
Data for a liquid-cooled engine with a displacement volume of 1710 cubic inches were analyzed to determine the effect of exhaust pressure on the engine cooling characteristics. The data covered a range of exhaust pressures from 7 to 62 inches of mercury absolute, inlet-manifold pressures from 30 to 50 inches of mercury absolute, engine speeds from 1600 to 3000 rpm, and fuel-air ratios from 0.063 to 0.100. The effect of exhaust pressure on engine cooling was satisfactorily incorporated in the NACA cooling-correlation method as a variation in effective gas temperature with exhaust pressure. Large variations of cylinder-head temperature with exhaust pressure were obtained for operation at constant charge flow. At a constant charge flow of 2 pounds per second (approximately 1000 bhp) and a fuel-air ratio of 0.085, an increase in exhaust pressure from 10 to 60 inches of mercury absolute resulted in an increase of 40 F in average cylinder-head temperature. For operation at constant engine speed and inlet-manifold pressure and variable exhaust pressure (variable charge flow), however, the effect of exhaust pressure on cylinder-head temperature is small. For example, at an inlet-manifold pressure of 40 inches of mercury absolute, an engine speed of 2400 rpm.- and a fuel-air ratio of 0.085, the average cylinder-head temperature was about the same at exhaust pressures of 10 and 60 inches of,mercury absolute; a rise and a subsequent decrease of about 70 occurred between these extremes.
Engine icing researchers at the NASA Glenn Research Center use the Escort data acquisition system in the Propulsion Systems Laboratory (PSL) to generate and collect a tremendous amount of data every day. Currently these researchers spend countless hours processing and formatting their data, selecting important variables, and plotting relationships between variables, all by hand, generally analyzing data in a spreadsheet-style program (such as Microsoft Excel). Though spreadsheet-style analysis is familiar and intuitive to many, processing data in spreadsheets is often unreproducible and small mistakes are easily overlooked. Spreadsheet-style analysis is also time inefficient. The same formatting, processing, and plotting procedure has to be repeated for every dataset, which leads to researchers performing the same tedious data munging process over and over instead of making discoveries within their data. This paper documents a data analysis tool written in Python hosted in a Jupyter notebook that vastly simplifies the analysis process. From the file path of any folder containing time series datasets, this tool batch loads every dataset in the folder, processes the datasets in parallel, and ingests them into a widget where users can search for and interactively plot subsets of columns in a number of ways with a click of a button, easily and intuitively comparing their data and discovering interesting dynamics. Furthermore, comparing variables across data sets and integrating video data (while extremely difficult with spreadsheet-style programs) is quite simplified in this tool. This tool has also gathered interest outside the engine icing branch, and will be used by researchers across NASA Glenn Research Center. This project exemplifies the enormous benefit of automating data processing, analysis, and visualization, and will help researchers move from raw data to insight in a much smaller time frame.
A memory subsystem package is provided that has processing logic for data reorganization within the memory subsystem package. The processing logic is adapted to reorganize data stored within the memory subsystem package. In some embodiments, the memory subsystem package includes memory units, a memory interconnect, and a data reorganization engine ("DRE"). The data reorganization engine includes a stream interconnect and DRE units including a control processor and a load-store unit. The control processor is adapted to execute instructions to control a data reorganization. The load-store unit is adapted to process data move commands received from the control processor via the stream interconnect for loading data from a load memory address of a memory unit and storing data to a store memory address of a memory unit.
Varying reference pressure used together with a balanced pressure pickup /a diaphragm switch/ to switch the electric output of the pressure transducer in a reference pressure line obtains precise engine cylinder pressure data from a high speed internal combustion engine.
The Space Shuttle Main Engine Vibration Data Base is described. Included is a detailed description of the data base components, the data acquisition process, the more sophisticated software routines, and the future data acquisition methods. Several figures and plots are provided to illustrate the various output formats accessible to the user. The numerous vibration data recall and analysis capabilities available through automated data base techniques are revealed.
Data were obtained with five exhaust-nozzle areas and with the variable-area exhaust nozzle interlinked with the control system at conditions simulating flight at a Mach number of 0.8 and altitudes of 35,00 and 45,000 feet. Data simulating operation at zero flight Mach number at an altitude of 15,000 feet are also included. Engine component performance data are presented in addition to the overall engine performance.