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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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A two-terminator RF adapter for background noise measurement in a test environment comprises a system test port comprising a system test port termination and a system test port connector to connect to a system under test; and a data acquisition port, comprising a data acquisition port termination and a data acquisition port connector to connect to a data acquisition system.
Systems and computer-implemented methods are used for analyzing battery information. The battery information may be acquired from both passive data acquisition and active data acquisition. Active data may be used for feature extraction and parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric-based parameters and optimization-based parameters. These parameters can be combined with a decision fusion algorithm to develop internal battery parameters. Analysis processes including particle filter analysis, neural network analysis, and auto regressive moving average analysis can be used to analyze the internal battery parameters and develop battery health metrics. Additional decision fusion algorithms can be used to combine the internal battery parameters and the battery health metrics to develop state-of-health estimations, state-of-charge estimations, remaining-useful-life predictions, and end-of-life predictions for the battery.
A storage control system includes an apparatus and method for user control of a storage interface to operate a storage medium to store data obtained by a real-time data acquisition system. Digital data received in serial format from the data acquisition system is first converted to a parallel format and then provided to the storage interface. The operation of the storage interface is controlled in accordance with instructions based on user control input from a user. Also, a user status output is displayed in accordance with storage data obtained from the storage interface. By allowing the user to control and monitor the operation of the storage interface, a stand-alone, user-controllable data storage system is provided for storing the digital data obtained by a real-time data acquisition system.
The Combined Loads Test System (COLTS) is a new structures test complex that is being developed at NASA Langley Research Center (LaRC) to test large curved panels and cylindrical shell structures. These structural components are representative of aircraft fuselage sections of subsonic and supersonic transport aircraft and cryogenic tank structures of reusable launch vehicles. Test structures are subjected to combined loading conditions that simulate realistic flight load conditions. The facility consists of two pressure-box test machines and one combined loads test machine. Each test machine possesses a unique set of requirements or research data acquisition and real-time data display. Given the complex nature of the mechanical and thermal loads to be applied to the various research test articles, each data system has been designed with connectivity attributes that support both data acquisition and data management functions. This paper addresses the research driven data acquisition requirements for each test machine and demonstrates how an open architecture data system design not only meets those needs but provides robust data sharing between data systems including the various control systems which apply spectra of mechanical and thermal loading profiles.
A modular system for acquiring biometric data includes a plurality of data acquisition modules configured to sample biometric data from at least one respective input channel at a data acquisition rate. A representation of the sampled biometric data is stored in memory of each of the plurality of data acquisition modules. A central control system is in communication with each of the plurality of data acquisition modules through a bus. The central control system is configured to control communication of data, via the bus, with each of the plurality of data acquisition modules.
Four Surface Energy and Radiation Balance Systems (SERBS) were installed and operated for two weeks in Kansas during July of 1986. Surface energy and radiation balances were investigated on six sites on the Konza Prairie about 3 km south of Manhattan, Kansas. Measurements were made to allow the computation of these radiation components: total solar and diffuse radiation, reflected solar radiation, net radiation, and longwave radiation upward and downward. Measurements were made to allow the computation of the sensible and latent heat fluxes by the Bowen ratio method using differential psychrometers on automatic exchange mechanisms. The report includes a description of the experimental sites, data acquisition systems and sensors, data acquisitions system operating instructions, and software used for data acquisition and analysis. In addition, data listings and plots of the energy balance components for all days and systems are given.
A temporary bonding system for accurately predetermining the individual apparent strain curve characteristics of the gages is used, and subsequently employs a computer to watch the apparent strain curves of the individual gages to determine which gages should be used together on transducers. The temporary bonding system requires a test block on which the gages are temporarily bonded, several thermocouples for monitoring temperature, and a data acquisition system for recording apparent strain data. Initially, a group of strain gages are attached to the test block using a bonding agent that disintegrates at high temperatures. The gages are then wired to an appropriate data acquisition and data, collected throughout a predetermined temperature excursion. Once the data is obtained, the test block is heated until the bonding agent disintegrates freeing the gages from the test block. The gages are then disconnected from the data acquisition system and cleaned, thereby ready for use on transducers.
Currently, demand for rare earth elements (REEs) increased significantly. Coal is actively evaluated as potential economic sources for extraction of REEs. Here, in this work, laser-induced breakdown spectroscopy (LIBS) was evaluated for rapid estimation of REEs content and their distribution in the natural coal samples. The results were compared with similar laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) measurements. Thirteen coal samples (nine standard samples and five natural samples) were used in this study. Powder samples were pressed into pellets while coal chunks were directly ablated for data recording. Pellets of the powder standard samples were used to optimize the data acquisition system and then data recorded with this optimized system was used to identify the proper data acquisition and analysis models. After establishing the proper data acquisition system and analysis model using the standard samples, natural coal samples in powder form and their chunks were utilized to record LIBS and LA-ICP-MS spectra. Multivariate calibration models were developed using four of the natural samples, which were evaluated by predicting the REE content in the fifth sample. Principal component analysis was performed on the LIBS data obtained from the natural samples and it classified all the samples with high accuracy. Two-dimensional (2D) elemental mapping on coal chunk samples was also performed using both LIBS and LA-ICP-MS to study the distribution of REEs in the samples. The resulting elemental images and their correlations can be used to infer mineral distributions.
Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving these cooperative wind farms is to develop algorithms that provide necessary information to ensure reliable and efficient operation of turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This presentation demonstrates a framework for implementing a cooperative wind. Farm that incorporates information from local sensors in real time to better align turbines in a wind farm. Oftentimes, measurements made at an individual turbine are noisy and unreliable. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction and could decrease dynamic yaw misalignment, decrease the amount of time a turbines spends yawing due to a more reliable input to the yaw controller, and increase resiliency to faulty wind-vane measurements. The concept was evaluated on data provided by WindESCo and was evaluated across several other averaging techniques to demonstrate the resiliency of the proposed consensus method.
Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. In this work consensus control is applied in a hybrid analysis to data from an existing wind farm to demonstrate the benefit of consensus control.
Multifunctional biomedical data-acquisition system monitors cardiovascular and pulmonary performance of patient in real time.
A multichannel, acoustic AM carrier system was developed for a wide variety of applications, particularly for aircraft noise and sonic boom measurements. Each data acquisition channel consists of a condenser microphone, an acoustic signal converter, and a Zero Drive amplifier, along with peripheral supporting equipment. A control network insures continuous optimal tuning of the converter and permits remote calibration of the condenser microphone. With a 12.70-mm (1/2-in.) condenser microphone, the converter/Zero Drive amplifier combination has a frequency response from 0 Hz to 20 kHz (-3 db), a dynamic range exceeding 70 db, and a minimum noise floor of 50 db ref. 20 micro Pa) in the band 22.4 Hz to 22.4 kHz. The system requires no external impedance matching networks and is insensitive to cable length, at least up to 900 m (3,000 ft). System gain varies only + or - 1 db over the temperature range 4 to 54 C (40 to 130 F). Adapters are available to accommodate 23.77-mm (1-in.) and 6.35-mm (1/4-in.) microphones and to provide 30-db attenuation. A field test to obtain the acoustical time history of a helicopter flyover proved successful.
Computer program enables real-time data acquisition and plotting of stress strain by small, desk top computer. Program monitors load cell and multiple-strain gages during tension or compression loading. Scan routine triggered by specified change of applied load or longitudinal strain.
The PANDAWN sensor network in Chicago, IL, is a state-of-the-art testbed for networked, multi-modal sensing. It integrates AI/data science methods into its operation, from data acquisition to automated data labeling and curation workflows. The curation and dissemination of diverse multi-modal datasets will enable the development of new radiological/nuclear (R/N) detection, localization, and tracking algorithms and methods relevant across the nonproliferation mission space. This article first introduces the PANDAWN sensor network and the features that make it stand out from previous multi-modal data acquisition efforts. We then review the various data streams acquired on the PANDAWN nodes and present the implementation of an automated data curation pipeline that includes the labeling of radiation and contextual data streams. Here, we finally provide a short overview of different studies that leveraged the curated datasets.
The earth resources data acquisition systems on ERTS are providing data from the earth's surface that have great potential for resources management. The Data Collection System is providing water resources data several times a day from widely scattered locations in the Delaware River Basin. Within the constraints of an experimental test, the data are being processed and released to water resources agencies in near-real time. The results of ERTS investigations have shown that there is a potential application for satellite-borne systems for earth resources data acquisition. It is becoming clear that the solutions to many natural resource problems can be found faster and more efficiently with the help of data acquisition systems such as those on the ERTS observatory. Under operational conditions, low cost battery-operated DCP's could provide the Geological Survey with data from a large number of field instruments. These data could be used by the Geological Survey to monitor the operational status of field instrumentation and could be used by cooperating agencies to monitor a wide range of earth resources conditions. Under operational conditions, the data flow could be in real time. The delay from time of data acquisition by ERTS to the time of data availability to data users could be reduced to seconds, rather than the present lag time of a few hours.
Investment in brighter sources and larger and faster detectors has accelerated the speed of data acquisition at national user facilities. The accelerated data acquisition offers many opportunities for the discovery of new materials, but it also presents a daunting challenge. The rate of data acquisition far exceeds the current speed of data quality assessment, resulting in less than optimal data and data coverage, which in extreme cases forces recollection of data. Herein, we show how this challenge can be addressed through the development of an approach that makes routine data assessment automatic and instantaneous. By extracting and visualizing customized attributes in real time, data quality and coverage, as well as other scientifically relevant information contained in large data sets, is highlighted. Deployment of such an approach not only improves the quality of data but also helps optimize the usage of expensive characterization resources by prioritizing measurements of the highest scientific impact. We anticipate our approach will become a starting point for a sophisticated decision-tree that optimizes data quality and maximizes scientific content in real time through automation. Finally, with these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed of data acquisition.
We present a case for low batch-size inference with the potential for adaptive training of a lean encoder model. We do so in the context of a paradigmatic example of machine learning as applied in data acquisition at high data velocity scientific user facilities such as the Linac Coherent Light Source-II x-ray Free-Electron Laser. We discuss how a low-latency inference model operating at the data acquisition edge can capitalize on the naturally stochastic nature of such sources. We simulate the method of attosecond angular streaking to produce representative results whereby simulated input data reproduce high-resolution ground truth probability distributions. By minimizing the mean-squared error between the decoded output of the latent representation and the ground truth distributions, we ensure that the encoding layers and resulting latent representation maintains full fidelity for any downstream task, be it classification or regression. We present throughput results for data-parallel inference of various batch sizes, some with throughput exceeding 100 k images per second. We also show in situ training below 10 s per epoch for the full encoder–decoder model as would be relevant for streaming and adaptive real-time data production at our nation’s scientific light sources.
The complete set of Tropical Ocean and Global Atmosphere (TOGA)/Coupled Ocean Atmosphere Response Experiment (COARE) flight data for the 91.65 GHz Airborne Meteorological Radiometer (AMMR92) contains data from nineteen flights: two test flights, four transit flights, and thirteen experimental flights. The data flight occurred between December 16, 1992 and February 28, 1993. Data collection from the AMMR92 during the first ten flights of TOGA/COARE was performed using the executable code TSK30041. These are IBM PC/XT programs used by the NASA Goddard Space Flight Center (GSFC). During one flight, inconsistencies were found during the operation of the AMMR92 using the GSFC data acquisition system. Consequently, the Georgia Tech (GT) data acquisition system was used during all successive TOGA/COARE flights. These inconsistencies were found during the data processing to affect the recorded data as well. Errors are caused by an insufficient pre- and post-calibration setting period for the splash-plate mechanism. The splash-plate operates asynchronusly with the data acquisition system (there is no position feedback to the GSFC or GT data system). This condition caused both the calibration and the post-calibration scene measurement to be corrupted on a randomly occurring basis when the GSFC system was used. This problem did not occur with the GT data acquisition system due to sufficient allowance for splash-plate settling. After TOGA/COARE it was determined that calibration of the instrument was a function of the scene brightness temperature. Therefore, the orientation error in the main antenna beam of the AMMR92 is hypothesized to be caused by misalignment of the internal 'splash-plate' responsible for directing the antenna beam toward the scene or toward the calibration loads. Misalignment of the splash-plate is responsible for 'scene feedthrough' during calibration. Laboratory investigation at Georgia Tech found that each polarization is affected differently by the splash-plate alignment error. This is likely to cause significant and unique errors in the absolute calibration of each channel.