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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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Detection of Environment Transitions in Time Series Data for Responsive Science
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Integration of Real-Time Building Data into Economic Dispatch of Dynamically Electrified Building Steam Systems
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Using Machine Learning to Predict Bilingual Language Proficiency from Reaction Time Priming Data.
Abstract not provided.
Extreme Weather Events and the Impact on PV Time Series Data
The impact of extreme weather events on PV performance was studied by comparing the National Oceanic and Atmospheric Administration database on severe weather with the PV Fleet database on continuous PV performance. We identified 170 systems that were immediately impacted by weather events. These severe weather events lead to a median loss of only 1% of annual production. However, flooding and high wind events were found to have an extremely long tail extending to 60 % loss showing that these discrete events can pose a substantial risk to PV systems. Besides the short-term impact of lost production due to outages, we also found a statistically significant increased performance loss rate (PLR) for high wind events comparing PLR before and after these weather events. In addition, hail events caused a higher PLR for 2 out of 3 systems. More data are required to better quantify the impact, but these first results illustrate the substantial risk these events pose short-and long-term.
A Two-Step Time-Series Data Clustering Method for Building-Level Load Profile: Preprint
Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation. In our two-step clustering approach, we first calculate the total load consumption for each building, clustering the load profiles based on energy consumption level. Then, we further cluster the load profiles in each energy cluster based on the load shape. The parameter selection for each clustering step is discussed. The proposed method is applied on actual building-level load profiles, and the results have proved the effectiveness of this method.
Development of a timing and data link for EIC Common Hardware Platform
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TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
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A Three-Tier Incremental Approach for Development of Smart Corridor Digital Twins
Development of an arterial smart corridor digital twin requires the integration of real time data streams, data storage, and simulation model execution. This process may be complex, time consuming, and susceptible to errors. To aid in smart corridor digital twin development this paper seeks to provide a framework, best practices, and development guidance. As such, a three-tier incremental approach to smart corridor digital twin development is presented. The paper highlights practical issues in digital twin construction along with key data challenges based on experiences from the development of digital twins for two large Smart Corridor deployments, one in Chattanooga, Tennessee, and the other in Atlanta, Georgia. The presented three-tier incremental approach includes: 1) development of a prepopulated offline simulation, 2) development of a pseudo digital twin that is driven by archived data, and 3) integration of real time data streams to create the online digital twin model. The three-tiered approach facilitates conducting multiple trials and scenarios with increasing complexity, allowing for incremental error processing and updating of the digital twin.
Future timing accuracy requirements and procedures for data processing
Timing accuracy requirements for satellite experiment data are discussed. Although time accuracies of 100 milliseconds were sufficient when OSO-3, 4, 5 were launched in the mid-60s, time accuracies of 100 microseconds are necessary for OSO-8, HEAO-A, and Seasat, and it is thought likely that accuracies of 10 microseconds will become routine in the 1980s. An approach to the problem of identifying the sources of spacecraft timing errors - from the onboard counters to the final tagging of data - is considered.
Real-time flight test data distribution and display
Enhancements to the real-time processing and display systems of the NASA Western Aeronautical Test Range are described. Display processing has been moved out of the telemetry and radar acquisition processing systems super-minicomputers into user/client interactive graphic workstations. Real-time data is provided to the workstations by way of Ethernet. Future enhancement plans include use of fiber optic cable to replace the Ethernet.
Acquisition of Dynamic Stress/Strain Data
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.
(abstract) A Comparison Between Measurements of the F-layer Critical Frequency and Values Derived from the PRISM Adjustment Algorithm Applied to Total Electron Content Data in the Equatorial Region
The Parametrized Real-Time Ionosphere Specification Model (PRISM) is a global ionospheric specification model that can incorporate real-time data to compute accurate electron density profiles. Time series of computed and measured data are compared in this paper. This comparison can be used to suggest methods of optimizing the PRISM adjustment algorithm for TEC data obtained at low altitudes.
Thermal balance testing of MSAT 2 spacecraft
The present work reports on the recently completed infrared thermal balance/thermal vacuum testing of a MSAT satellite, the first satellite to provide mobile communications service for all of continental North America. MSAT is a two spacecraft program, using a three-axis stabilized Hughes HS-601 series Bus as the vehicle for the Canadian designed Payload. The thermal tests which were performed at the Canadian Space Agency's David Florida Laboratory in Ottawa, Canada, lasted approximately 35 days. The infrared (IR) heating rig was designed to provide radiant heat inputs into seven spacecraft zones during Thermal Vacuum (TV) testing. The TV test was divided into multiple phases. It began with a thermal balance cold phase, followed by a thermal cold cycle and a hot balance phase, complemented by a thermal hot cycle to finish with a thermal cycle with continuous monitoring of the Bus and Payload. The spacecraft's external heat fluxes were provided by IR lamp sources. To ensure flux uniformity, highly reflective baffles and IR East and West faces; the Earth facing (Nadir); and the inside of the thrust cylinder. The aft-end panel heat fluxes were provided by a heated LN2 shroud. The radiation flux intensity on the spacecraft zones from the various rig elements was measured using Monitored Background Radiometers (MBR's) and compared with direct calculations and with pretest predictions. The temperature measurement system was based on Uniform Temperature References (UTR's) located inside the chamber such that all feedthroughs were copper-copper. This system was devised to achieve a temperature measurement accuracy of plus/minus 0.5 C for over 850 thermocouples used in the test. A PC-(QNX-based) based real-time data acquisition system was utilized to provide continuous monitoring of all channels based on a 30-second time scan. In addition, the data acquisition system was able to retrieve telemetry stream from the Satellite Test Equipments (STE) station for real-time data manipulation. Preliminary results showed the test to be successful from both the thermal balance side and the electrical testing side.
Advanced space information systems
Onboard computers for pre-transmission data processing, automation of data acquisition centers, and real time data relay to control centers for advanced space information systems
Land and Atmosphere Near-Real-Time Capability for Earth Observing System
The past decade has seen a rapid increase in availability and usage of near-real-time data from satellite sensors. The EOSDIS (Earth Observing System Data and Information System) was not originally designed to provide data with sufficiently low latency to satisfy the requirements for near-real-time users. The EOS (Earth Observing System) instruments aboard the Terra, Aqua and Aura satellites make global measurements daily, which are processed into higher-level 'standard' products within 8-40 hours of observation and then made available to users, primarily earth science researchers. However, applications users, operational agencies, and even researchers desire EOS products in near-real-time to support research and applications, including numerical weather and climate prediction and forecasting, monitoring of natural hazards, ecological/invasive species, agriculture, air quality, disaster relief and homeland security. These users often need data much sooner than routine science processing allows, usually within 3 hours, and are willing to trade science product quality for timely access. While Direct Broadcast provides more timely access to data, it does not provide global coverage. In 2002, a joint initiative between NASA (National Aeronautics and Space Administration), NOAA (National Oceanic and Atmospheric Administration), and the DOD (Department of Defense) was undertaken to provide data from EOS instruments in near-real-time. The NRTPE (Near Real Time Processing Effort) provided products within 3 hours of observation on a best-effort basis. As the popularity of these near-real-time products and applications grew, multiple near-real-time systems began to spring up such as the Rapid Response System. In recognizing the dependence of customers on this data and the need for highly reliable and timely data access, NASA's Earth Science Division sponsored the Earth Science Data and Information System Project (ESDIS)-led development of a new near-real-time system called LANCE (Land, Atmosphere Near-Real-Time Capability for EOS) in 2009. LANCE consists of special processing elements, co-located with selected EOSDIS data centers and processing facilities. A primary goal of LANCE is to bring multiple near-real-time systems under one umbrella, offering commonality in data access, quality control, and latency. LANCE now processes and distributes data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric Infrared Sounder (AIRS), Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) instruments within 3 hours of satellite observation. The Rapid Response System and the Fire Information for Resource Management System (FIRMS) capabilities will be incorporated into LANCE in 2011. LANCE maintains a central website to facilitate easy access to data and user services. LANCE products are extensively tested and compared with science products before being made available to users. Each element also plans to implement redundant network, power and server infrastructure to ensure high availability of data and services. Through the user registration system, users are informed of any data outages and when new products or services will be available for access. Building on a significant investment by NASA in developing science algorithms and products, LANCE creates products that have a demonstrated utility for applications requiring near-real-time data. From lower level data products such as calibrated geolocated radiances to higher-level products such as sea ice extent, snow cover, and cloud cover, users have integrated LANCE data into forecast models and decision support systems. The table above shows the current near-real-time product categories by instrument. The ESDIS Project continues to improve the LANCE system and use the experience gained through practice to seek adjustments to improve the quality and performance of the system. For example, anGC-compliant Web Map Service (WMS) will be added shortly that will allow users to download geo-referenced MODIS images for arbitrary bounding boxes. Further, an OGC-compliant Web Coverage Service (WCS) will be added later this year that will expedite user access to arbitrary data subsets or re-formatted products. AIRS images are now served through WMS and available in multiple formats (PNG, GeoTIFF, KMZ). NASA has established a LANCE User Working Group to steer the development of the system and create a forum for sharing ideas and experiences that are expected to further improve the LANCE capabilities. The LANCE system has proved a success by satisfying the growing needs of the applications and operational communities for land and atmosphere data in near-real-time. NASA's Earth Sciences Division was able to leverage existing science research capabilities to provide the near-real-time community with products and imagery that support monitoring of disasters in a timely manner.
Labeled Clearsky Period Data Set
Time series data set containing labeled clearsky periods.
Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data
Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.