Search NASA⌕ Search

SEARCH · Search NASA

Results for “streaming data”

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.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Generating Accurate and Consistent Top-Of-Atmosphere Reflectance Products from the New Generation Geostationary Satellite Sensors

GeoNEX is a collaborative project by scientists from NASA, NOAA, JAXA, and other organizations around the world with the purpose of generating a suite of Earth-monitoring products using data streams from the latest geostationary (GEO) sensors including the GOES-16/17 ABI and the Himawari-8/9 AHI. An accurate and consistent top-of-atmosphere (TOA) reflectance product, in particular the bidirectional reflectance factor (BRF), is the starting point in the scientific processing chain. We describe the main considerations and corresponding algorithms in generating the GeoNEX TOA BRF product. First, a special advantage of geostationary data streams is their high temporal resolution (~10 minutes per full-disk scan), providing a key source of information for many downstream products. To fully utilize this high temporal frequency demands a high georegistration accuracy for every acquired image. Our analysis shows that there can be substantial georegistration uncertainties in both GOES and Himawari L1b data which we addressed by implementing a phase-based correction algorithm to remove residual errors. Second, geostationary sensors have distinct illumination-view geometry features in that the solar angle changes for every pixel. Therefore, to accurately derive a BRF requires a solar position algorithm and the estimation of the pixel-wise acquisition time within an uncertainty of 10 seconds. Third, we discuss the measures we adopted to check and correct residual radiometric calibration issues of individual sensors to enable time-series analysis as well as the cross calibration between different satellite sensors (including those from low-Earth orbit). Finally, we also explain the rationale for the choice of the global grid/tile system of the GeoNEX TOA BRF product.

Wang, Weile↗
DOE OSTI2012

Real Time Simulation of Power Grid Disruptions

DOE-OE and DOE-SC workshops (Reference 1-3) identified the key power grid problem that requires insight addressable by the next generation of exascale computing is coupling of real-time data streams (1-2 TB per hour) as the streams are ingested to dynamic models. These models would then identify predicted disruptions in time (2-4 seconds) to trigger the smart grid s self healing functions. This project attempted to establish the feasibility of this approach and defined the scientific issues, and demonstrated example solutions to important smart grid simulation problems. These objectives were accomplished by 1) using the existing frequency recorders on the national grid to establish a representative and scalable real-time data stream; 2) invoking ORNL signature identification algorithms; 3) modeling dynamically a representative region of the Eastern interconnect using an institutional cluster, measuring the scalability and computational benchmarks for a national capability; and 4) constructing a prototype simulation for the system s concept of smart grid deployment. The delivered ORNL enduring capability included: 1) data processing and simulation metrics to design a national capability justifying exascale applications; 2) Software and intellectual property built around the example solutions; 3) demonstrated dynamic models to design few second self-healing.

Chinthavali, Supriya↗
DOE OSTI2018

Fast Reconstruction and Data Scouting

Data scouting, introduced by CMS in 2011, is the use of specialized data streams based on reduced event content, enabling LHC experiments to record unprecedented numbers of proton-proton collision events that would otherwise be rejected by the usual filters. These streams were created to maintain sensitivity to new light resonances decaying to jets or muons, while requiring minimal online and offline resources, and taking advantage of the fast and accurate online reconstruction algorithms of the high-level trigger. The viability of this technique was demonstrated by CMS in 2012, when 18.8 fb$^{-1}$ of collision data at $\sqrt{s} = 8$ TeV were collected and analyzed. For LHC Run 2, CMS, ATLAS, and LHCb implemented or expanded similar reduced-content data streams, promoting the concept to an essential and flexible discovery tool for the LHC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking

The intermittency of carbon-free renewables and the demand changes associated with the widespread push for electrifying the transportation and building sectors provides an opportunity for buildings to go beyond energy efficiency and push towards providing demand flexibility to the electricity grid. The duality of energy efficiency and demand flexibility is necessary for success in a sustainable and reliable energy transition. Current building energy benchmarking models are limited in their ability to integrate concepts of demand flexibility and/or utilize granular smart meter data. Thus, current benchmarking methods are focused annual energy usage and fail to incorporate how the time of use of energy consumption impacts emissions in a quickly changing energy grid. Without a more comprehensive view of energy usage and associated real-time emissions, current benchmarking methods are unlikely to realize the full decarbonization potential of buildings. New emerging data streams provide an opportunity to develop a new generation of benchmarking energy models that embed dimensions of energy efficiency, grid interactivity, and demand flexibility into their analysis. In this paper, we propose a four-step method for embedding grid interactivity and demand flexibility into building benchmarking models that utilizes emerging building and time-series electricity data streams. We first engineer features to produce a mix-type dataset that encompasses many attributes of grid-interactive and efficient buildings, and then we apply K-medoids using Gower's Distance to produce peer-group clusters. We apply the method to a case study of 306 primary and secondary schools in southern California, USA. The results show that the method effectively clusters buildings by attributes of demand flexibility and energy efficiency. The clustering results reveal patterns in inefficient building operations and demand inflexibility at the building peer group level. In conclusion, the interpretation of clusters can serve as an integrated energy efficiency and demand flexibility benchmarking model and inform performance-specific policy targeting for buildings that go beyond traditional efficiency measures.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

BAYESIAN NOISE ESTIMATION FOR NON-IDEAL COSMIC MICROWAVE BACKGROUND EXPERIMENTS

We describe a Bayesian framework for estimating the time-domain noise covariance of cosmic microwave background (CMB) observations, typically parameterized in terms of a 1/f frequency profile. This framework is based on the Gibbs sampling algorithm, which allows for exact marginalization over nuisance parameters through conditional probability distributions. In this paper, we implement support for gaps in the data streams and marginalization over fixed time-domain templates, and also outline how to marginalize over confusion from CMB fluctuations, which may be important for high signal-to-noise experiments. As a by-product of the method, we obtain proper constrained realizations, which themselves can be useful for map making. To validate the algorithm, we demonstrate that the reconstructed noise parameters and corresponding uncertainties are unbiased using simulated data. The CPU time required to process a single data stream of 100,000 samples with 1000 samples removed by gaps is 3 s if only the maximum posterior parameters are required, and 21 s if one also wants to obtain the corresponding uncertainties by Gibbs sampling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Directional gamma sensing from covariance processing of inter-detector Compton crosstalk energy asymmetries

Energy asymmetry of inter-detector crosstalk from Compton scattering can be exploited to infer the direction to a gamma source. A covariance approach extracts the correlated crosstalk from data streams to estimate matched signals from Compton gammas split over two detectors. On a covariance map the signal appears as an asymmetric cross diagonal band with axes intercepts at the full photo-peak energy of the original gamma. The asymmetry of the crosstalk band can be processed to determine the direction to the radiation source. The technique does not require detector shadowing, masking, or coded apertures, thus sensitivity is not sacrificed to obtain the directional information. An angular precision of better than 1° of arc is possible, and processing of data streams can be done in real time with very modest computing hardware.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Spatial temporal cluster analysis to enhance awareness of disease re-emergence on a global scale

ObjectiveThe application of spatial analysis to improve the awareness and use of surveillance data.IntroductionThe re-emergence of an infectious disease is dependent on social, political, behavioral, and disease-specific factors. Global disease surveillance is a requisite of early detection that facilitates coordinated interventions to these events. Novel informatics tools developed from publicly available data are constantly evolving with the incorporation of new data streams. Re-emerging Infectious Disease (RED) Alert is an open-source tool designed to help analysts develop a contextual framework when planning for future events, given what has occurred in the past. Geospatial methods assist researchers in making informed decisions by incorporating the power of place to better explain the relationships between variables.MethodsDisease incidence and indicator data derived for the RED Alert project were analyzed for spatial associations. Using aggregate country-level data, spatial and spatiotemporal clusters were identified in ArcMap 10.5.1. The identified clusters were then used as the outcome for a series of binary logistic regression models to determine significant covariates that help explain global hotspots. These methods will continue to evolve and be incorporated into the RED Alert decision support ecosystem to provide analysts with a global perspective on potential re-emergence.ResultsHotspots of high disease incidence in relation to neighboring countries were identified for measles, cholera, dengue, and yellow fever between 2000 and 2014. Disease-specific predictors were identified using aggregate estimates from World Bank indicator dataset. Data was imputed where possible to enhance the validity of the Gi * statistic for clustering. In the future, as data streams become more readily available, hotspot modeling at a finer resolution will help to improve the precision of spatial epidemiology.ConclusionsSpatial methods enhance the capability of understanding complex population and disease relationships, which in turn improves surveillance and the ability to predict re-emergence. With tools like RED Alert, public health analysts can better prepare to respond rapidly to future re-emerging disease threats.

Altherr, Forest M.↗
DOE OSTI2026

WHONDRS Surface Water and Sediment Geochemistry and Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon (v2)

This dataset supports a broader study developing conceptual models for river corridor critical zone processes across spatial scales and was generated in collaboration with the HJ Andrews River Corridor Critical Zone Workshop in 2025. The dataset provides surface water geochemistry (dissolved organic carbon, total dissolved nitrogen) from 48 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Some of the sites have been impacted by the Holiday Farm Fire and the Lookout Fire in 2020 and 2023, respectively. Related data were collected as part of the workshop and will be published separately in collaboration with other workshop attendees and available at http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. Related genomic data can be found on the National Center for Biotechnology Information (NCBI) under BioProject PRJNA1503030 (see critical details section below for more information). Additional related data collected in 2016 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3377027 and http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1-2019 (Ward et al., 2019). This data package was originally published in March 2026. It was updated in August 2026 (v2; new and modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos, (2) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data, (3) a data checks report, (4) a folder of sample data, (5) file-level metadata, (6) data dictionary, (7) field metadata, (8) readme, (9) international generic sample number (IGSN) mapping file; and (10) field protocol. The sample data subfolder contains surface water and sediment (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages, (2) total dissolved nitrogen data and averages, (3) methods codes, (4) FTICR-MS methods; and (5) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the CoreMS processed data and seven subfolders, thee containing .xml files for each sample type (sediment, surface water and blank samples), three containing the sediment CoreMS output files for each sample type (sediment, surface water and blank samples), and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, .json, .jpg, or .jpeg.

Biogeochemistry↗
DOE OSTI2016

Dragonfly : an implementation of the expand–maximize–compress algorithm for single-particle imaging

Single-particle imaging (SPI) with X-ray free-electron lasers has the potential to change fundamentally how biomacromolecules are imaged. The structure would be derived from millions of diffraction patterns, each from a different copy of the macromolecule before it is torn apart by radiation damage. The challenges posed by the resultant data stream are staggering: millions of incomplete, noisy and un-oriented patterns have to be computationally assembled into a three-dimensional intensity map and then phase reconstructed. In this paper, the Dragonfly software package is described, based on a parallel implementation of the expand–maximize–compress reconstruction algorithm that is well suited for this task. Auxiliary modules to simulate SPI data streams are also included to assess the feasibility of proposed SPI experiments at the Linac Coherent Light Source, Stanford, California, USA.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control

Abstract Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework for out of distribution (OOD) detection and data drift monitoring that combines ML and geometric methods with statistical process control (SPC). We investigated different design choices, including methods for extracting feature representations and drift quantification for OOD detection in individual images and as an approach for input data monitoring. We evaluated the framework for both identifying OOD images and demonstrating the ability to detect shifts in data streams over time. We demonstrated a proof-of-concept via the following tasks: 1) differentiating axial vs. non-axial CT images, 2) differentiating CXR vs. other radiographic imaging modalities, and 3) differentiating adult CXR vs. pediatric CXR. For the identification of individual OOD images, our framework achieved high sensitivity in detecting OOD inputs: 0.980 in CT, 0.984 in CXR, and 0.854 in pediatric CXR. Our framework is also adept at monitoring data streams and identifying the time a drift occurred. In our simulations tracking drift over time, it effectively detected a shift from CXR to non-CXR instantly, a transition from axial to non-axial CT within few days, and a drift from adult to pediatric CXRs within a day—all while maintaining a low false positive rate. Through additional experiments, we demonstrate the framework is modality-agnostic and independent from the underlying model structure, making it highly customizable for specific applications and broadly applicable across different imaging modalities and deployed ML models.

Zamzmi, Ghada↗
DOE OSTI2021

Investigating Vegetation Responses to Underground Nuclear Explosions Through Integrated Analyses

Vegetation has the potential to respond to underground nuclear explosions, yet these links have not been fully explored. Given the lack of previously described signatures, the changes in vegetation are possibly subtle. The integration of multiple different data streams is potentially a useful approach to improve signal detection. Here, we investigate whether semi-arid vegetation growth patterns responded to eight legacy underground nuclear tests at the Nevada National Security Site in southern Nevada, USA. We tested for spatial and temporal changes in vegetation cover, tree growth patterns, and tree leaf spectral properties using ground-based measurements, including those from tree-rings and hyperspectral surface vegetation reflectance, as well as space-based measurements of Normalized Difference Vegetation Index (NDVI) from Landsat. Multiple data streams suggest a localized (<1.2 km) spatial pattern whereby tree growth is enhanced closer to the source of the underground test relative to sites further away. We also observed a more regional (>1.2–9 km) pattern whereby tree growth is suppressed coincident with a drought beginning 1 year before the 1989 tests, but continuing in the 5 years following the tests, which is anomalous relative to what is expected based on the response of tree growth to previous droughts. furthermore, quantification of the relative effects of the tests on vegetation remains a challenge due to the coincident drought and the potential for other disturbances to have impacted tree growth at this time, but the integration of these data reveals a more nuanced growth response than any other one data set indicates alone.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

BeyondPlanck: V. Minimal ADC Corrections for Planck LFI

We describe the correction procedure for Analog-to-Digital Converter (ADC) differential non-linearities (DNL) adopted in the Bayesian end-to-end BEYONDPLANCK analysis framework. This method is nearly identical to that developed for the official Planck Low Frequency Instrument (LFI) Data Processing Center (DPC) analysis, and relies on the binned rms noise profile of each detector data stream. However, rather than building the correction profile directly from the raw rms profile, we first fit a Gaussian to each significant ADC-induced rms decrement, and then derive the corresponding correction model from this smooth model. The main advantage of this approach is that only samples which are significantly affected by ADC DNLs are corrected, as opposed to the DPC approach in which the correction is applied to all samples, filtering out signals not associated with ADC DNLs. The new corrections are only applied to data for which there is a clear detection of the non-linearities, and for which they perform at least comparably with the DPC corrections. Out of a total of 88 LFI data streams (sky and reference load for each of the 44 detectors) we apply the new minimal ADC corrections in 25 cases, and maintain the DPC corrections in 8 cases. All these corrections are applied to 44 or 70 GHz channels, while, as in previous analyses, none of the 30 GHz ADCs show significant evidence of non-linearity. By comparing the BEYONDPLANCK and DPC ADC correction methods, we estimate that the residual ADC uncertainty is about two orders of magnitude below the total noise of both the 44 and 70 GHz channels, and their impact on current cosmological parameter estimation is small. However, we also show that non-idealities in the ADC corrections can generate sharp stripes in the final frequency maps, and these could be important for future joint analyses with the Planck High Frequency Instrument (HFI), Wilkinson Microwave Anisotropy Probe (WMAP), or other datasets. We therefore conclude that, although the existing corrections are adequate for LFI-based cosmological parameter analysis, further work on LFI ADC corrections is still warranted.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Exploring Physics of Ferroelectric Domain Walls in Real Time: Deep Learning Enabled Scanning Probe Microscopy

The functionality of ferroelastic domain walls in ferroelectric materials is explored in real-time via the in situ implementation of computer vision algorithms in scanning probe microscopy (SPM) experiment. The robust deep convolutional neural network (DCNN) is implemented based on a deep residual learning framework (Res) and holistically nested edge detection (Hed), and ensembled to minimize the out-of-distribution drift effects. The DCNN is implemented for real-time operations on SPM, converting the data stream into the semantically segmented image of domain walls and the corresponding uncertainty. Further the pre-defined experimental workflows perform piezoresponse spectroscopy measurement on thus discovered domain walls, and alternating high- and low-polarization dynamic (out-of-plane) ferroelastic domain walls in a PbTiO 3 (PTO) thin film and high polarization dynamic (out-of-plane) at short ferroelastic walls (compared with long ferroelastic walls) in a lead zirconate titanate (PZT) thin film is reported. This work establishes the framework for real-time DCNN analysis of data streams in scanning probe and other microscopies and highlights the role of out-of-distribution effects and strategies to ameliorate them in real time analytics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Timeseries Unlabeled and Labeled Photos, Modeled Stream Elevation, and (Meta)Data of Variably Inundated Streams Across The Yakima River Basin, Washington, United States (v2)

This dataset is associated with the “River Monitoring Photos” (RMP) study and subsequent manuscript (Bao et al. 2025. Monitoring river flow status using low-cost wildlife camera and image segmentation artificial intelligence doi: 10.1016/j.envsoft.2025.106715). Game camera timeseries photos were collected to evaluate stream variable inundation via changes in width. A subset of photos was labeled for training the YOLOv8 and Mask2Former models and used to segment water surface fractions from all the game camera photos.This data package was originally published in March 2024. It was updated in October 2025 (v2) to add additional photos and files associated with the manuscript (i.e., processed data, labeled photos, and trained models). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About.In addition to a readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; and (5) folders containing game camera photos and manuscript-associated files. Each Yakima River Basin site has a folder that contains subfolders for each month photos were collected. There is also a folder for files associated with the manuscript which has subfolders for labeled data, trained models, Yakima River Basin site water surface fractions, and USGS site water surface fractions. All files are .csv, .json, .txt, .yaml, .pth, .pt, or .pdf. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Downlink Data Multiplexer

A multiplexer/demultiplexer system has been developed to enable the transmission, over a single channel, of four data streams generated by a variety of sources at different (including variable) bit rates. In the original intended application, replicas of this multiplexer/demultiplexer system would be incorporated into the spacecraft-to-ground communication systems of the space shuttles. The multiplexer of each system would be installed in the spacecraft, where it would acquire and process data from such sources as commercial digital camcorders, video tape recorders, and the spacecraft telemetry system. The demultiplexer of each system would be installed in a ground station. Purely terrestrial systems of similar design could be attractive for use in situations in which there are requirements to transmit multiple streams of high-quality video data and possibly other data over single channels. The figure is a block diagram of the multiplexer as configured to process data received via three fiber-optic channels like those of the International Space Station and one electrical-cable channel that conforms to the Institute of Electrical and Electronic Engineers (IEEE) 1394 standard. (This standard consists of specifications of a high-speed serial data interface, the physical layer of which includes a cable known in the art as "FireWire." An IEEE 1394 interface can also transfer power between the components to which it is connected.) The fiber-optic channels carry packet and/or bit-stream signals that conform to the standards of the Consultative Committee for Space Data Systems (CCSDS). The IEEE 1394 interface accepts an isochronous signal like that from a digital camcorder or a video tape recorder. The processing of the four input data streams to combine them into one output stream is governed by a statistical multiplexing algorithm that features a flow-control capability and makes it possible to utilize the transmission channel with nearly 100-percent efficiency. This algorithm allocates the available bandwidth of the transmission channel to the data streams according to a combination of data rates and preassigned priorities. Incoming data streams that demand too much bandwidth are blocked. Bandwidth not needed for a transmission of a given data stream is allocated to other streams as available. Priority is given to the IEEE 1394 stream. In addition to the four incoming data streams, the multiplexer transmits data on the status of the system. An operator can monitor and control the multiplexer via displays and controls on the multiplexer housing. The output of the multiplexer is connected via a coaxial cable with an impedance of 50 Ohms to an interface circuit compatible with the space-shuttle high-speed digital downlink, which operates at a rate of 48 Mb/s.

Holland, Douglas↗
DOE OSTI2020

Evolution of the Data Quality Monitoring and Prompt Processing System in the proto DUNE-SP experiment

The DUNE Collaboration currently operates an experimental program based at CERN which includes a beam test and an extended cosmic ray run of two large-scale prototypes of the massive Liquid Argon Time Projection Chamber (LArTPC) for the DUNE Far Detector. The volume of data collected by the single-phase prototype (protoDUNE-SP) amounts to 3PB and the sustained rate of data sent to mass storage is O(100) MB/s. Data Quality Monitoring was implemented by directing a fraction of the data stream to the protoDUNE prompt processing system (p3s) which is optimized for continuous low-latency calculation of the vital detector metrics and various graphics including event displays. It served a crucial role throughout the life cycle of the experiment. We present our experience in leveraging the CERN computing environment and operating the system over an extended period of time, while adapting to evolving requirements and computing platform.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Automatic, simultaneous control of polymer composition and molecular weight during free radical copolymer synthesis

Fully automatic, simultaneous control of polymer composition and molecular weight trajectories during free radical copolymer synthesis was achieved by coupling the continuous ACOMP data stream into a feedback controller which operated pumps for reactor feed of comonomers. The controller operates without recourse to a detailed kinetic model or reactivity ratios. Rather, it uses the fact that instantaneous copolymer chain composition is related to the instantaneous rate of consumption of comonomers, and the rate for each of these is described by rates α 1 and α 2 for comonomers 1 and 2, respectively. The instantaneous weight average molecular weight M w,inst is proportional to total comonomer concentration via a constant p. Hence, target trajectories for composition and M w can be independently prescribed and followed. Finally, since α 1 , α 2 and p are directly measurable from the ACOMP data stream, their values can be constantly updated by the automatic controller during the reaction process to insure the target trajectories are followed.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A Synchronized Sensor Array for Remote Monitoring of Avian and Bat Interactions with Offshore Renewable Energy Facilities

Wind energy production in the U.S. is projected to increase to 35% of our nation’s energy by 2050. This substantial increase in the U.S. is only a portion of the global wind industry growth, as many countries strive to reduce greenhouse gas emissions. A major environmental concern and potential market barrier for expansion of wind energy is bird and bat mortality from impacts with turbine blades, towers, and nacelles. Carcass surveys are the standard protocol for quantifying mortality at onshore sites. This method is imperfect, however, due to survey frequency at remote sites, removal of carcasses by scavengers between surveys, searcher efficiency, and other biases as well as delays of days to weeks or more in obtaining information on collision events. Furthermore, carcass surveys are not feasible at offshore wind energy sites. Near-real-time detection and quantification of interaction rates is possible at both onshore and offshore wind facilities using an onboard, integrated sensor package with data transmitted to central processing centers. We developed and experimentally tested an array of sensors that continuously monitors for interactions (including impacts) of birds and bats with wind turbines. The synchronized array includes three sensor nodes: (1) vibration (accelerometers and contact microphones), (2) optical (visual and infrared spectrum cameras), and (3) bioacoustics (acoustic and ultrasonic microphones). Accelerometers and contact acoustic microphones are placed at the root of each blade to detect impact vibrations and sound waves propagating through the structure. On-board data processing algorithms using wavelet analysis detect impact signals exceeding background vibration. Stereo-visual and infrared cameras were placed on the nacelle to allow target tracking, distance, and size calculations. On-board image processing and target detection algorithms identify moving targets within the camera field of view. Bioacoustic recorders monitor vocalizations and echolocations to aid in identifying organisms involved in interactions. Data from all sensors are temporarily stored in ring (i.e., circular) buffers with a duration varying by sensor type. Detection of target presence or impact by any of the sensors can trigger the archiving of data from all buffers for transmission to a central data processing center for evaluation and post-processing. This mitigates the risk of “data mortgages” posed by continual recording and minimizes personnel time required to manually review event data. We first conducted individual component tests at laboratories and field sites in Corvallis and Newport, Oregon, and Seattle and Sequim, Washington. We conducted additional component tests on research wind turbines at the North American Wind Research and Training Center, Mesalands Community College (MCC; General Electric 1.5 MW turbine), New Mexico, and the National Wind Technology Center, National Renewable Energy Laboratory (NREL; Controls Advanced Research Turbines 3 [CART 3] 600 kW Westinghouse turbine), Colorado. We conducted fully integrated system tests at NREL in October 2014 and April 2015. We used only research wind turbines so that we could conduct controlled, experimentally generated impacts using empty and water-filled tennis balls shot from a compressed air cannon on the ground. The approx. 57-140 g tennis balls (depending on water content) were at the upper mass range for bats, but lower mass range for marine birds. Therefore, the ability to detect collisions of most seabirds is likely greater than our experiments demonstrate, but possibly lower for some bats depending on the background signal of a given turbine. Vibration data demonstrated that background signals of operating turbines varied markedly among the CART 3 under normal operation (greatest), GE (moderate), and CART 3 during idle rotation (generator not engaged; least). In total, we measured 63 experimental blade impacts on the two research turbines. Impaction detection was dependent on background signals, position of impact on the blade (a tip strike resulted in the strongest impact signal), and impact kinetics (velocity of ball and whether the ball struck the surface of the blade or the leading edge of the blade struck the ball). Overall detection percentage ranged from 100% for the “quietest” conditions (CART 3 idle rotation), down to 35% for the noisiest (CART 3 normal operation). Impact signals were detected from sensors on more than one blade (i.e., blades other than the blade struck) 50% - 75% of the time. Stereo imaging provided valuable metrics, but increased data processing and equipment cost. Given the cost of cameras with sufficient resolution for target identification, we suggest mounting cameras directly on the blades to continuously view the entire rotor swept area with the fewest number of cameras. Bioacoustic microphones provide taxonomic identification, as well as information on ambient noise levels. They also assist in identifying environmental conditions such as hail storms, high winds, thunder, lightning, etc., that may contribute to a collision or a false positive detection. We demonstrated a proof of concept for an integrated sensor array to detect and identify bird and bat collisions with wind turbines. The next phase of research and development for this system will miniaturize and integrate sensors from all three nodes into a single wireless package that can be attached directly to the blade. This next generation system would use all “smart” sensors capable of onboard data processing to drastically reduce data streams and processing time on a central computer. A provisional patent for the blade mounted system was submitted by Oregon State University and recorded by the U.S. Patent and Trademark Office (application no. 62313028). Eventually, technology and industry advances will allow this low cost monitoring system to be designed into materials during manufacturing so that all turbines could be monitored with either a subset or full suite of sensors. As standard equipment on all commercial turbines, the sensor suite would allow the industry to effectively monitor whether individual turbines were causing mortalities or not and under what circumstances. It would also provide real-time evaluation of mechanical and structural integrity of a turbine via vibration, image, and acoustic data streams, thereby permitting modifications in operation to limit environmental or mechanical damage.

17 WIND ENERGY↗