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At least 739 records · Page 41

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NASA NTRSDate not supplied

Status and Operations at the Granite Island, Michigan (GIM) BSRN Station

In June 2018, a new surface radiation site was established on Granite Island, Michigan (GIM), located in Lake Superior. GIM is a 0.1 square km (2.5 acre) rock island. GIM moved from candidate to active and became a fully-fledged member of the Baseline Surface Radiation Network (BSRN) in July 2020 (BSRN Label: GIM). The installation is solar powered, and autonomous instrument functions are accessed remotely from Hampton, Virginia, USA. The original motivation to establishing surface radiation measurements at GIM was to combine high quality downwelling shortwave and longwave measurements with existing evaporation measurements that could lead to improved understanding of the Earth’s energy budget. Other scientific benefits are the addition of a new "water" site to the BSRN network (Water sites are rare in the BSRN network) and a surface validation site for satellite measurements such as the Clouds and the Earth’s Radiant Energy System (CERES). In this poster, we present the status of GIM. GIM is equipped with standard downwelling flux at visible and infrared wavelengths as required by the BSRN network. Basic meteorological parameters are being monitored, as well as other data collections for aerosol and total column water vapor, PAR, and lake skin temperature. Lastly, the capabilities to clean shortwave optics remotely will be discussed and a current list of instruments and pictures will be displayed.

Bryan Fabbri↗

NASA NTRS2022

A Terrestrial Gamma-ray Flash from the 2022 Hunga Tonga–Hunga Ha’apai Volcanic Eruption

The Hunga Tonga–Hunga Ha’apai submarine volcano recently resumed activity. Violent eruptions on 2022 January 14th and 15th launched a tall ash plume that produced extremely high lightning rates. Here we report a terrestrial gamma-ray flash (TGF) that was produced by the volcanic lightning and observed from space by the Fermi Gamma-ray Burst Monitor (GBM). Observations by radio lightning networks and especially by the Geostationary Lightning Mapper (GLM) show that the only lightning close enough to produce a TGF detectable by Fermi GBM was from the volcano’s plume. With the observing duration of Fermi, observing a single TGF is consistent with the hypothesis that the volcanic lightning of this eruption produced TGFs at the average rate of thunderstorm lightning. The observation of a strong TGF from space also indicates that the electric field was oriented so as to accelerate electrons upward.

Terrestrial gamma-ray flashes↗

DOE OSTI2020

Mechanical Ventilator Milano (MVM): A Novel Mechanical Ventilator Designed for Mass Scale Production in Response to the COVID-19 Pandemics

We present here the design of the Mechanical Ventilator Milano (MVM), a novel mechanical ventilator designed for mass scale production in response to the COVID-19 pandemics, to compensate for the dramatic shortage of such ventilators in many countries. This ventilator is an electro-mechanical equivalent of the old, reliable Manley Ventilator. Our design is optimized to permit large sale production in short time and at a limited cost, relying on off-the-shelf components, readily available worldwide from hardware suppliers. Operation of the MVM requires only a source of compressed oxygen (or compressed medical air) and electrical power. The MVM control and monitoring unit can be connected and networked via WiFi so that no additional electrical connections are necessary other than the connection to the electrical power. At this stage the MVM is not a certified medical device. Construction of the first prototypes is starting with a team of engineers, scientists and computing experts. The purpose of this paper is to disseminate the conceptual design of the MVM broadly and to solicit feedback from the scientific and medical community to speed the process of review, improvement and possible implementation.

Galbiati, C.↗

DOE OSTI2021

The EGS Collab Project: Status and Accomplishments

The EGS Collab project, supported by the US Department of Energy, is addressing challenges in implementing enhanced geothermal systems (EGS). This includes improving understanding of the stimulation of crystalline rock to create appropriate flow pathways, and the ability to effectively simulate both the stimulation and the flow and transport processes in the resulting fracture network. The project is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory. Data and observations from the field test are compared to simulations to understand processes and to build confidence in numerical modeling of the processes. In Experiment 1, we examined hydraulic fracturing an underground test bed at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, at a depth of approximately 1.5 km. We drilled eight sub-horizontal boreholes in a well-characterized phyllite. Six of the boreholes were instrumented with many sensor types to allow careful monitoring of stimulation events and flow tests, and the other two boreholes were used for water injection and production. We performed a number of stimulations and flow tests in the testbed. Our monitoring systems allowed detailed observations and collection of numerous data sets of processes occurring during stimulation and during dynamic flow tests. Long-term ambient temperature and chilled water flow tests were performed in addition to many tracer tests to examine system behavior. Data were rapidly analyzed, allowing adaptive control of the tests. Numerical simulation was used to answer key experimental design questions, to forecast fracture propagation trajectories and extents, and to analyze and evaluate results. Many simulations were performed in near-real-time in conjunction with the field experiments, with more detailed process study simulations performed on a longer timeframe. Experiment 2 will examine hydraulic shearing in a test bed being built at the SURF at a depth of about 1.25 km in amphibolite under a different set of stress and fracture conditions than Experiment 1. Five sets of fracture orientations were considered in design, and three orientations seem to be consistently observed.

EGS Collab↗

DOE OSTI2024

Internet of things and operational technology detection and visualization platform

A computer-implemented method of monitoring activity of devices in a network is provided. The method comprises passively collecting data regarding how the devices access the network, and for each device on the network, identifying all other devices on the network with which the device communicates. All communication traffic from the devices to outside the network is identified. A determination is made if there are any required updates and if patches for the devices execute in a fashion defined as safe. A number of risk indicators for privacy risks are determined according to device communication within the network, device communication to outside the network, and update and patch execution. A visualization of any identified risk factors is displayed to a user through a user interface.

Urias, Vincent↗

NASA NTRSDate not supplied

Open-source Wireless Sensor Network (Wi-Se Net) for Flexible Deployment

Wireless sensors, especially if battery powered, have a number of advantages over wired sensors for flexible or temporary diagnostic deployment in a field or lab setting. Recent advances in wireless technology and microprocessor boards have produced a variety of inexpensive off-the shelf chips which can communicate wirelessly with simple protocols. This paper describes the design and implementation of a highly customizable wireless sensor network (called WiSe Net) using inexpensive open-source hardware components as wireless nodes. These wireless sensor nodes can transmit data at a rate <250 Hz, can be battery powered, and have a small footprint (2x5 cm). In addition, a preliminary over-the-air programming system was developed to allow for simple wireless configuration when active. The network performance was demonstrated by taking distributed and electrically isolated temperature measurements on a high-voltage lab apparatus. Although this test case is in a laboratory setting, this network architecture could be easily repurposed for various other forms of monitoring

Wireless Sensor Network↗

NASA NTRSDate not supplied

Open-source Wireless Sensor Network (Wi-Se Net) for Flexible Deployment

Wireless sensors, especially if battery powered, have a number of advantages over wired sensors for flexible or temporary diagnostic deployment in a field or lab setting. Recent advances in wireless technology and microprocessor boards have produced a variety of inexpensive off-the shelf chips which can communicate wirelessly with simple protocols. This paper describes the design and implementation of a highly customizable wireless sensor network (called Wi-Se Net) using inexpensive open-source hardware components as wireless nodes. These wireless sensor nodes can transmit data at a rate <250 Hz, can be battery powered, and have a small footprint (2x5 cm). In addition, a preliminary over-the-air programming system was developed to allow for simple wireless configuration when active. The network performance was demonstrated by taking distributed and electrically isolated temperature measurements on a high-voltage lab apparatus. Although this test case is in a laboratory setting, this network architecture could be easily repurposed for various other forms of monitoring.

Wireless Sensor Network↗

NASA NTRSDate not supplied

Open-source Wireless Sensor Network (Wi-Se Net) for Flexible Deployment

Wireless Sensor Network↗

NASA NTRSDate not supplied

Wireless Sensor Instrumentation for Distributed Sensing in Ground-Test Facilities

Wireless sensors have a number of advantages over wired sensors for flexible or temporary diagnostic deployment in a field or lab setting. Relative to wireless alternatives, traditional wired systems encounter limitations including spatial and weight constraints, maintenance costs, and signal integrity in high-noise environments. Ground-test facilities often face these challenges and cause increase down-time costs and failure of data integrity. This paper describes the development and implementation of flexible wireless sensor boards for applications within ground-test facilities using different network architectures. These boards implement state-of-the- art STM32 microprocessor chips for high-speed data acquisition and computational power. Additionally, the design integrates long-range radio transceivers with power output of +20dBm, easily reaching 2 km range, while maintaining high interference immunity and minimizing current consumption. The wireless sensor boards were implemented to demonstrate performance through benchmark testing and by taking distributed electrically-isolated measurements on a high-voltage lab apparatus while using different network architectures. Although this test case is in a laboratory setting, this network architecture could be easily repurposed for various other forms of monitoring.

Wireless Sensor Network↗

NASA NTRS2004

Comparison of Satellite Observations of Aerosol Optical Depth to Surface Monitor Fine Particle Concentration

Under NASA's Earth Science Applications Program, the Infusing satellite Data into Environmental Applications (IDEA) project examined the relationship between satellite observations and surface monitors of air pollutants to facilitate a more capable and integrated observing network. This report provides a comparison of satellite aerosol optical depth to surface monitor fine particle concentration observations for the month of September 2003 at more than 300 individual locations in the continental US. During September 2003, IDEA provided prototype, near real-time data-fusion products to the Environmental Protection Agency (EPA) directed toward improving the accuracy of EPA s next-day Air Quality Index (AQI) forecasts. Researchers from NASA Langley Research Center and EPA used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument combined with EPA ground network data to create a NASA-data-enhanced Forecast Tool. Air quality forecasters used this tool to prepare their forecasts of particle pollution, or particulate matter less than 2.5 microns in diameter (PM2.5), for the next-day AQI. The archived data provide a rich resource for further studies and analysis. The IDEA project uses data sets and models developed for tropospheric chemistry research to assist federal, state, and local agencies in making decisions concerning air quality management to protect public health.

Kleb, Mary M.↗

DOE OSTI2024

Cybersecurity Assessment in DER-rich Distribution Operations: Criticality Levels and Impact Analysis

The integration of distributed energy resources (DERs) in distribution networks has become a pivotal strategy for achieving decarbonization, enhancing grid resilience, and optimizing grid efficiency. Remote monitoring and control op- erations of such resources rely on a network of sensors and communication infrastructure, exposing the system to potential cyber threats. Therefore, as the deployment of DERs increases, ensuring secure monitoring and control becomes an imperative challenge. This paper utilizes real-time feeder models, which are instrumental in developing cybersecurity testbeds tailored for hardware-in-loop (HIL) systems. These models enable users to simulate cyber attacks in a real-world environment and analyze the power distribution operations during vulnerabilities. Furthermore, we discuss several practical sets of grid parameters to identify critical levels of DERs and evaluate various scenarios that simulate cyber threats on sensitive DERs. The modified IEEE 123-bus model is used as the test case for demonstrating the proposed scenarios. The findings from this study provide valuable insights into the vulnerabilities and potential consequences of cyber attacks on DERs, allowing for better mitigation strategies and improved cyber resilience in future distribution networks.

Maharjan, Manisha↗

DOE OSTI2025

Pyridine Catalysis of Anhydride Hydrolysis within Carbodiimide‐Driven Reaction Networks

Carbodiimide-fueled reaction networks offer a versatile platform for nonequilibrium chemical systems. Typically, the carbodiimide converts a carboxylic acid to its anhydride, called “activation,” which subsequently undergoes hydrolysis, called “deactivation.” Here, we investigate pyridines with variable nucleophilicity as catalysts to control deactivation (pyridine, 4-methylpyridine, 4-methoxypyridine, and 4-dimethylaminopyridine). Reactions have been monitored by NMR spectroscopy. Although this reaction network is simple, determination of well-defined rate constants from kinetic modeling is challenging because of correlation between the parameters. This issue can be addressed by analyzing the anhydride hydrolysis independently. The rate of attack of the pyridines on the anhydride follows expected nucleophilicity trends, although this is offset by increased protonation of more-nucleophilic pyridines at typical pH's. The optimized parameters can be used to model the full carbodiimide-driven process, although the presence of the common carbodiimide EDC has unanticipated effects on the anhydride hydrolysis rate. The results offer context for controlling carbodiimide-fueled reaction networks through the choice of suitable catalysts and pH.

Anhydrides↗

NASA NTRS2008

Rig Diagnostic Tools

Rig Diagnostic Tools is a suite of applications designed to allow an operator to monitor the status and health of complex networked systems using a unique interface between Java applications and UNIX scripts. The suite consists of Java applications, C scripts, Vx- Works applications, UNIX utilities, C programs, and configuration files. The UNIX scripts retrieve data from the system and write them to a certain set of files. The Java side monitors these files and presents the data in user-friendly formats for operators to use in making troubleshooting decisions. This design allows for rapid prototyping and expansion of higher-level displays without affecting the basic data-gathering applications. The suite is designed to be extensible, with the ability to add new system components in building block fashion without affecting existing system applications. This allows for monitoring of complex systems for which unplanned shutdown time comes at a prohibitive cost.

Soileau, Kerry M.↗

NASA NTRS2022

Chapter 7: Sun Photometers

Sun photometry is an extended technique for monitoring the atmospheric composition and support satellite product validation. By measuring the direct solar irradiance, the amount and characteristics of the atmospheric aerosol particles, precipitable water vapor column, and ozone columns can be derived. Global operational networks such as the Aerosol Robotic Network (AERONET), Global Atmosphere Watch-Precision Filter Radiometer Network (GAW-PFR), Maritime Aerosol Network (MAN), or the Brewer spectrophotometer networks constitute sustained efforts for long-term monitoring of the atmosphere, with invaluable data production using cost-effective and relatively simple instrumentation. The data quality relies on the standardization of these networks, with special emphasis on the calibration as the key element in Sun photometry.

Sun photometry↗

DOE OSTI2024

Multi-level structural damage characterization using sparse acoustic sensor networks and knowledge transferred deep learning

Not Available

36 MATERIALS SCIENCE↗

DOE OSTI2024

Mid-IR UAV-based sensing platform with deep learning to Identify and Quantify Gaseous Emission in Gas Flares

This report details the development and evaluation of a Mid-Infrared (Mid-IR) Unmanned Aerial Vehicle (UAV)-based sensing platform integrated with deep learning algorithms for the identification and quantification of gaseous emissions in gas flares. The project, spearheaded by Omega Optics, Inc., aimed to address environmental monitoring challenges by leveraging advanced photonic technologies and autonomous UAV operations. The research focused on designing, optimizing, and fabricating photonic crystal waveguides and grating couplers to enhance the sensitivity and accuracy of gas detection. A comprehensive drone-based system was developed, featuring a miniaturized sensor, GPS module, and microcontroller communication network for real-time gas concentration monitoring. The system's adaptive sampling algorithm, implemented using the Robot Operating System (ROS), enables autonomous detection and localization of gas emission sources. Preliminary results demonstrate the platform's capability to detect and monitor gas emissions with high precision, cost-effectiveness, and scalability. Future work will expand upon this foundation by introducing 3D wind model-based learning for dynamic environmental conditions and further enhancing the user interface and data processing algorithms to support broader environmental monitoring applications. Overall, this project represents a significant step forward in UAV-based environmental sensing technologies, offering robust solutions for detecting and mitigating the impacts of gaseous emissions on public health and safety.

47 OTHER INSTRUMENTATION↗

NASA NTRS2021

Sub-city Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements

While multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA’s GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements.

C. Malings↗

DOE OSTI2024

Predicting seismic amplitudes with machine learning

The accurate estimation of seismic wave amplitude is vital to precisely determine the yield, magnitude, and event discrimination possible for a given network – a critical element in nuclear explosion monitoring. This task is complicated by several factors, including but not limited to radiation pattern, scattering effects, and crustal variations, which can lead to the attenuation or amplification of amplitude along a given raypath. In this report, we explore the novel application of machine learning to the task of seismic amplitude estimation by training a simple Artificial Neural Network (ANN) on an S-wave amplitude dataset from Lai et al. (2019). Attributes from this dataset used as input to the ANN included event-station distances, station locations (latitude, longitude), event locations (latitude, longitude), event depths, event magnitudes, radiation patterns, signal-to noise ratio (SNR) measurements (average-amplitude, peak-to-trough, maximum peak), and signal periods. We find that the trained ANN predicts S-wave amplitudes with a modest tendency toward underestimating the actual values, as indicated by a linear regression between predicted and actual data (slope: 0.892, intercept: -0.651). These results suggest that an ANN can perform this task, with potential for significant improvements through improved datasets, architectures, and parameter tuning.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗