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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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NASA NTRS1988

Using data tagging to improve the performance of Kanerva's sparse distributed memory

The standard formulation of Kanerva's sparse distributed memory (SDM) involves the selection of a large number of data storage locations, followed by averaging the data contained in those locations to reconstruct the stored data. A variant of this model is discussed, in which the predominant pattern is the focus of reconstruction. First, one architecture is proposed which returns the predominant pattern rather than the average pattern. However, this model will require too much storage for most uses. Next, a hybrid model is proposed, called tagged SDM, which approximates the results of the predominant pattern machine, but is nearly as efficient as Kanerva's original formulation. Finally, some experimental results are shown which confirm that significant improvements in the recall capability of SDM can be achieved using the tagged architecture.

Rogers, David↗

NASA NTRS2011

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykhian, Gholam Ali↗

NASA NTRS2011

Improve Data Mining and Knowledge Discovery through the use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykahian, Gholan Ali↗

NASA NTRS2016

Helicopter Acoustic Flight Test with Altitude Variation and Maneuvers

A cooperative flight test campaign between NASA and the U.S. Army was performed from September 2014 to February 2015. The purposes of the testing were to: investigate the effects of altitude variation on noise generation, investigate the effects of gross weight variation on noise generation, establish the statistical variability in acoustic flight testing of helicopters, and characterize the effects of transient maneuvers on radiated noise for a medium-lift utility helicopter. This test was performed at three test sites (0, 4000, and 7000 feet above mean sea level) with two aircraft (AS350 SD1 and EH-60L) tested at each site. This report provides an overview of the test, documents the data acquired and describes the formats of the stored data.

Watts, Michael E.↗

NASA NTRS2022

Yuma 2022 Rotorcraft Acoustic Flight Test

A cooperative flight test campaign between U.S. Navy, Army, Marine Corps, and NASA was performed at Marine Corps Air Station (MCAS) Yuma from 19 January to 15 February 2022. The primary purpose of testing military vehicles was to characterize the acoustic signature of the MV-22 rotorcraft and Stalker unmanned aerial system for typical mission gross weight, configuration, and for a range of typical mission operating conditions. Additionally, a civilian MD530 vehicle was tested to characterize the acoustic emissions for both legacy blades and S411 main rotor blades. This report provides an overview of the test, documents the data acquired, and describes the formats of the stored data.

Acoustics↗

NASA NTRS2016

Semantic Representation and Scale-Up of Integrated Air Traffic Management Data

Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.

data management↗

NASA NTRS2016

Semantic Representation and Scale-Up of Integrated Air Traffic Management Data

air traffic management↗

NASA NTRS1972

An improved data transfer and storage technique for hybrid computation

Improved technique was developed for transferring and storing data at faster than real time speeds on hybrid computer. Predominant advantage is combined use of electronic relays, track and store units, and analog-to-digital and digital-to-analog conversion units of hybrid computer.

Hansing, A. M.↗

NASA NTRS1984

Processing of Synthetic-Aperture-Radar Data

Ambiguous data combined to permit fast-transform convolution. New data processor designed: two-dimensional ensemble of range-correlated SAR data stored in memory and extracted in format for which azimuth data are colinear.

Di Cenzo, A. E.↗

NASA NTRS1986

A low-cost optical data acquisition system for vibration measurement

A low cost optical data acquisition system was designed to measure deflection of vibrating rotor blade tips. The basic principle of the new design is to record raw data, which is a set of blade arrival times, in memory and to perform all processing by software following a run. This approach yields a simple and inexpensive system with the least possible hardware. Functional elements of the system were breadboarded and operated satisfactorily during rotor simulations on the bench, and during a data collection run with a two-bladed rotor in the Lewis Research Center Spin Rig. Software was written to demonstrate the sorting and processing of data stored in the system control computer, after retrieval from the data acquisition system. The demonstration produced an accurate graphical display of deflection versus time.

Posta, S. J.↗

NASA NTRS1988

Low-cost optical data acquisition system for blade vibration measurement

Posta, Stephen J.↗

NASA NTRS2012

Restoration and Reexamination of Data from the Apollo 11, 12, 14, and 15 Dust, Thermal and Radiation Engineering Measurements Experiments

As part of an effort by the Lunar Data Node (LDN) we are restoring data returned by the Apollo Dust, Thermal, and Radiation Engineering Measurements (DTREM) packages emplaced on the lunar surface by the crews of Apollo 11, 12, 14, and 15. Also commonly known as the Dust Detector experiments, the DTREM packages measured the outputs of exposed solar cells and thermistors over time. They operated on the surface for up to nearly 8 years, returning data every 54 seconds. The Apollo 11 DTREM was part of the Early Apollo Surface Experiments Package (EASEP), and operated for a few months as planned following emplacement in July 1969. The Apollo 12, 14, and 15 DTREMs were mounted on the central station as part of the Apollo Lunar Surface Experiments Package (ALSEP) and operated from deployment until ALSEP shutdown in September 1977. The objective of the DTREM experiments was to determine the effects of lunar and meteoric dust, thermal stresses, and radiation exposure on solar cells. The LDN, part of the Geosciences Node of the Planetary Data System (PDS), operates out of the National Space Science Data Center (NSSDC) at Goddard Space Flight Center. The goal of the LDN is to extract lunar data stored on older media and/or in obsolete formats, restore the data into a usable digital format, and archive the data with PDS and NSSDC. For the DTREM data we plan to recover the raw telemetry, translate the raw counts into appropriate output units, and then apply calibrations. The final archived data will include the raw, translated, and calibrated data and the associated conversion tables produced from the microfilm, as well as ancillary supporting data (metadata) packaged in PDS format.

McBride, Marie J.↗

NASA NTRS2006

Microdose Induced Data Loss on Floating Gate Memories

Heavy ion irradiation of flash memories shows loss of stored data. The fluence dependence is indicative of microdose effects. Other qualitative factors identifying the effect as microdose are discussed. The data is presented, and compared to statistical results of a microdose target-based model.

radiation effects↗

NASA NTRS1972

Application of satellite infrared measurements to mapping sea ice

The application of the ITOS-SR (scanning radiometer) infrared measurements for mapping sea ice was examined. The work included detailed mapping of ice features visible in the ITOS nighttime DRSR (direct readout scanning radiometer) pictorial data and in Nimbus summertime film strip data. Analyses of digital temperature values from computer printouts of ITOS stored data and from Nimbus data listings were also undertaken, and densitometric measurements of both ITOS and Nimbus data were initiated.

Barnes, J. C.↗

NASA NTRS1988

Efficient vibration mode analysis of aircraft with multiple external store configurations

A coupling method for efficient vibration mode analysis of aircraft with multiple external store configurations is presented. A set of low-frequency vibration modes, including rigid-body modes, represent the aircraft. Each external store is represented by its vibration modes with clamped boundary conditions, and by its rigid-body inertial properties. The aircraft modes are obtained from a finite-element model loaded by dummy rigid external stores with fictitious masses. The coupling procedure unloads the dummy stores and loads the actual stores instead. The analytical development is presented, the effects of the fictitious mass magnitudes are discussed, and a numerical example is given for a combat aircraft with external wing stores. Comparison with vibration modes obtained by a direct (full-size) eigensolution shows very accurate coupling results. Once the aircraft and stores data bases are constructed, the computer time for analyzing any external store configuration is two to three orders of magnitude less than that of a direct solution.

Karpel, M.↗

NASA NTRS2018

Benchmark Comparison of Cloud Analytics Methods Applied to Earth Observations

Earth Observation data are a vital resource for studying long term changes, but the large data volumes can be challenging to analyze. Time series analysis in particular is hampered by the typical thin-time-slice file organization. We examine several potential solutions inspired in large part by the data-parallel methods that have arisen with cloud computing. These solutions include various combinations of data re-organization, spatial indexing, distributed storage and pre-computation that we term "Analytics Optimized Data Stores" (AODS). We find that even simple solutions (such as a data cube) produce more than an order of magnitude improvement; the best provide two to three orders of magnitude improvement. The most performant solutions have tradeoffs in terms of generality or storage footprint, but may nonetheless be useful components in data analytics frameworks where performance is critical.

parallel processing (computers)↗

NASA NTRS1980

Active nutation controller

An apparatus is described for controlling nutation motion in a spinning body, comprised of an angular accelerometer with its input axis perpendicular to the spin axis of the body, a flywheel with an axis of rotation perpendicular to the axis of the accelerometer and to the spin axis of the body, and a motor for driving the flywheel to attenuate or build nutation. The motor is controlled by circuitry that monitors the output of the angular accelerometer and drives the motor clockwise or counterclockwise during predetermined nutation angles synchronized to the zero crossover points of the accelerometer signal centered about the nutation peaks. The motor drive is phased to damp nutation motion to zero for stabilization. To increase the noise immunity of the system, when the output of the accelerometer falls below a threshold level, the circuitry operates in an open loop, beat mode where data representing the last accelerometer signal that exceeded that threshold level is stored, and the motor drive is controlled by the stored data. In a second version, the motor is controlled to supply a predetermined amount of nutation motion to a body undergoing testing on a spin table for energy dissipation evaluation. In each version, the use of an angular accelerometer rather than a linear accelerometer or gyro to monitor nutation enables placement of the nutation control apparatus at any location relative to the spin axis of the body requiring only crude orientation and no calibration.

Hoffman, H. C.↗

NASA NTRS1990

Microspacecraft and Earth observation: Electrical field (ELF) measurement project

The Utah State University space system design project for 1989 to 1990 focuses on the design of a global electrical field sensing system to be deployed in a constellation of microspacecraft. The design includes the selection of the sensor and the design of the spacecraft, the sensor support subsystems, the launch vehicle interface structure, on board data storage and communications subsystems, and associated ground receiving stations. Optimization of satellite orbits and spacecraft attitude are critical to the overall mapping of the electrical field and, thus, are also included in the project. The spacecraft design incorporates a deployable sensor array (5 m booms) into a spinning oblate platform. Data is taken every 0.1 seconds by the electrical field sensors and stored on-board. An omni-directional antenna communicates with a ground station twice per day to down link the stored data. Wrap-around solar cells cover the exterior of the spacecraft to generate power. Nine Pegasus launches may be used to deploy fifty such satellites to orbits with inclinations greater than 45 deg. Piggyback deployment from other launch vehicles such as the DELTA 2 is also examined.

Olsen, Tanya↗