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At least 289 records · Page 16

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NASA NTRS2012

TRMM Precipitation Application Examples Using Data Services at NASA GES DISC

Data services to support precipitation applications are important for maximizing the NASA TRMM (Tropical Rainfall Measuring Mission) and the future GPM (Global Precipitation Mission) mission's societal benefits. TRMM Application examples using data services at the NASA GES DISC, including samples from users around the world will be presented in this poster. Precipitation applications often require near-real-time support. The GES DISC provides such support through: 1) Providing near-real-time precipitation products through TOVAS; 2) Maps of current conditions for monitoring precipitation and its anomaly around the world; 3) A user friendly tool (TOVAS) to analyze and visualize near-real-time and historical precipitation products; and 4) The GES DISC Hurricane Portal that provides near-real-time monitoring services for the Atlantic basin. Since the launch of TRMM, the GES DISC has developed data services to support precipitation applications around the world. In addition to the near-real-time services, other services include: 1) User friendly TRMM Online Visualization and Analysis System (TOVAS; URL: http://disc2.nascom.nasa.gov/Giovanni/tovas/); 2) Mirador (http://mirador.gsfc.nasa.gov/), a simplified interface for searching, browsing, and ordering Earth science data at GES DISC. Mirador is designed to be fast and easy to learn; 3) Data via OPeNDAP (http://disc.sci.gsfc.nasa.gov/services/opendap/). The OPeNDAP provides remote access to individual variables within datasets in a form usable by many tools, such as IDV, McIDAS-V, Panoply, Ferret and GrADS; and 4) The Open Geospatial Consortium (OGC) Web Map Service (WMS) (http://disc.sci.gsfc.nasa.gov/services/wxs_ogc.shtml). The WMS is an interface that allows the use of data and enables clients to build customized maps with data coming from a different network.

Liu, Zhong↗

NASA NTRS2012

Onboard Sensor Data Qualification in Human-Rated Launch Vehicles

The avionics system software for human-rated launch vehicles requires an implementation approach that is robust to failures, especially the failure of sensors used to monitor vehicle conditions that might result in an abort determination. Sensor measurements provide the basis for operational decisions on human-rated launch vehicles. This data is often used to assess the health of system or subsystem components, to identify failures, and to take corrective action. An incorrect conclusion and/or response may result if the sensor itself provides faulty data, or if the data provided by the sensor has been corrupted. Operational decisions based on faulty sensor data have the potential to be catastrophic, resulting in loss of mission or loss of crew. To prevent these later situations from occurring, a Modular Architecture and Generalized Methodology for Sensor Data Qualification in Human-rated Launch Vehicles has been developed. Sensor Data Qualification (SDQ) is a set of algorithms that can be implemented in onboard flight software, and can be used to qualify data obtained from flight-critical sensors prior to the data being used by other flight software algorithms. Qualified data has been analyzed by SDQ and is determined to be a true representation of the sensed system state; that is, the sensor data is determined not to be corrupted by sensor faults or signal transmission faults. Sensor data can become corrupted by faults at any point in the signal path between the sensor and the flight computer. Qualifying the sensor data has the benefit of ensuring that erroneous data is identified and flagged before otherwise being used for operational decisions, thus increasing confidence in the response of the other flight software processes using the qualified data, and decreasing the probability of false alarms or missed detections.

Wong, Edmond↗

NASA NTRS2015

Acoustic Emission Analysis of Damage Progression in Thermal Barrier Coatings Under Thermal Cyclic Conditions

Damage evolution of electron beam-physical vapor deposited (EBVD-PVD) ZrO2-7 wt.% Y2O3 thermal barrier coatings (TBCs) under thermal cyclic conditions was monitored using an acoustic emission (AE) technique. The coatings were heated using a laser heat flux technique that yields a high reproducibility in thermal loading. Along with AE, real-time thermal conductivity measurements were also taken using infrared thermography. Tests were performed on samples with induced stress concentrations, as well as calcium-magnesium-alumino-silicate (CMAS) exposure, for comparison of damage mechanisms and AE response to the baseline (as-produced) coating. Analysis of acoustic waveforms was used to investigate damage development by comparing when events occurred, AE event frequency, energy content and location. The test results have shown that AE accumulation correlates well with thermal conductivity changes and that AE waveform analysis could be a valuable tool for monitoring coating degradation and provide insight on specific damage mechanisms.

calcium-magnesium-alumino-silicate (CMAS)↗

NASA NTRS2017

Using C-Band Dual-Polarization Radar Signatures to Improve Convective Wind Forecasting at Cape Canaveral Air Force Station and NASA Kennedy Space Center

The United States Air Force's 45th Weather Squadron (45WS) is the organization responsible for monitoring atmospheric conditions at Cape Canaveral Air Force Station and NASA Kennedy Space Center (CCAFS/KSC) and issuing warnings for hazardous weather conditions when the need arises. One such warning is issued for convective wind events, for which lead times of 30 and 60 minutes are desired for events with peak wind gusts of 35 knots or greater (i.e., Threshold-1) and 50 knots or greater (i.e., Threshold-2), respectively (Roeder et al. 2014).

Convective Wind↗

NASA NTRS2019

OuroboroSat: A Modular, CubeSat-Scale Instrumentation Platform

OuroboroSat (also known as MRMSS: the Modular Rapidly Manufactured Spacecraft System) is a modular instrumentation platform consisting of multiple 3 inch (7.5 centimeter) square printed circuit boards that are mechanically and electrically connected to one another in order to produce a fully- functioning payload facility system. Each OuroboroSat module consists of a microcontroller, a battery, conditioning and monitoring circuitry for the battery, optional space for solar panels, and an expansion area where an experimental payload or specialized functionality (such as wireless communication submodules) can be attached.

Modular instrumentation platform↗

NASA NTRS2018

Piezoelectric Charging and Wireless Communication

Modern medicine is undergoing a revolution in the application of new sensor capabilities for aiding in diagnosis of specific conditions and monitoring a variety of informative vital signs. In the past, many of the measurements were limited by what could be accomplished externally. A shift toward in-vivo monitoring for both diagnostic and therapeutic sensing and actuation [1] has created a need for low power electronics, high energy density batteries and methods to successfully power devices embedded in the body. For a review of the field and sensing capabilities see [2]. Recent studies suggest that charging with ultrasound is more efficient at longer transmission distance (> 10cm) than inductive charging [3]. In this manuscript, we discuss the modeling and experimentation that we have accomplished and demonstrate in ultrasonic charging of sensors having the form and fit of in-vivo sensors. The task goal has been to use piezoelectric transducers for wireless communication and powering of sensors internal to the human body with a goal to transmit power levels of 100 W a receiver with receiving area of 3x3 mm2 over a distance of 16 cm equivalent to human body interior. The results suggest that we can transmit power levels that exceed this baseline requirement.

Jackson, Shannon↗

NASA NTRSDate not supplied

In-Time Safety Assessment & Risk Prediction for Unmanned Aerial Systems

One of the critical challenges in emerging autonomous systems is timely mitigation of hazards encountered during operation which may not be known or accounted for at the time of design. Efficient execution of unmanned systems therefore demands a paradigm shift from scheduled periodic maintenance to predictive risk analysis that includes condition-based-monitoring, real-time reliability assessment and hazard mitigation. Particularly, the state-of-health parameters needs to be computed at the component level, unit level as well as the integrated system level. While in the former two levels, the physics of health propagation may be based on underlying electro-mechanical properties, system level prognostics often relies on data-driven models. Further, uncertainty from model, measurements and input sources should be accurately quantified to generate meaningful prediction results that can be fed into reliable decision making processes. Finally, the expected risk and time to failure has to be computed based on the current state-of-health of the overall system. This talk presents a conceptual design of such an in-time safety assurance approach for unmanned aerial vehicles (UAV) operating at low altitudes near and over populated areas. Typical in-flight hazard incidents include unplanned detour, proximity to obstacles, mid-flight component faults, limited battery life and poor quality of GPS measurements. Safety assessment therefore comprises trajectory generation and re-plan, battery RUL computation, distributed fault diagnostics and uncertainty management of predicted trajectory based on GPS measurement noise. The entire monitoring framework will be demonstrated on simulated as well as real UAV flight experiments conducted at the NASA Langley Research Center. This tutorial will therefore guide the audience through a step-by-step tracking of an autonomous system with focus on in-time risk prediction in the presence of unforeseen hazards and uncertain environment.

diagnostics↗

NASA NTRSDate not supplied

Latest Status of the CCSDS Optical Communications Working Group

International civil space agencies around the world are working together in the Interagency Operation Advisory Group (IOAG) and the Consultative Committee for Space Data Systems (CCSDS) to develop interoperability architectures and standards for space communications. Within CCSDS, there is a working group dedicated on developing recommendations and standards for optical communications. These standards include recommendations for the physical layer, coding and synchronization layer, and best practices for measuring and monitoring atmospheric conditions and operating optical links. The working group has developed standards for both Near Earth and deep space robotic and hum exploration missions. The standards generally address both free space links between spacecraft and free space links between spacecraft and ground. This paper will provide an overview and update on the set of standards the CCSDS Optical Communications Working Group has developed

Bernard L Edwards↗

NASA NTRS2022

Idaho Wildfires II: Assessing the Relationship Between Drought Indicators and Wildfire Risk to Enhance Hazard Modeling and Inform Mitigation Planning

The western United States has experienced twenty years of increased and prolonged drought which have exacerbated wildfire hazards. These jeopardize population centers through increased risks to ecosystem services, local economies, and livelihoods. The Idaho Office of Emergency Management, Water Resources, and Department of Lands are seeking methods to dynamically monitor these conditions and update models that inform hazard mitigation planning and resource allocation. Towards this, these agencies partnered with NASA DEVELOP to produce drought-enhanced wildfire hazard models. Part of a two-term project, the two teams revised the state’s static wildfire hazard model with refined data layers and remotely-sensed data to reflect dynamic ecosystem responses to drought conditions and wildfire potential. Our team distinguished between rangeland and forestland ecosystems, and investigated relationships between drought metrics and vegetation condition using TerrSet Earth Trends Modeler. This analysis determined that total precipitation at a 5-month lag interval (r 2 = 0.72) along with the Evaporative Stress Index (r 2 = 0.69); and precipitation at a 5-month interval (r 2 = 0.42) were important drivers in rangeland and forestland, respectively. These driver variables were incorporated into a temporally dynamic wildfire hazard map. Our team used linear regression to correlate hazard ratings with wildfire frequency. For the year 2020, neither the enhanced hazard model (p < 0.10, r 2 = 0.01) nor the state’s static model (p < 0.05, r 2 = 0.03) were strongly correlated with actual wildfire frequency as they expressed an inverse relationship between wildfire hazard and frequency. This suggests wildfire occurrence is complex and not necessarily driven by the variables used.

Wildfire↗

DOE OSTI2024

From Data to Knowledge: A Graph-Based Reliability Approach to Assess System Health

With the goal of maximizing plant reliability and availability, complex systems such as nuclear power plants continuously monitor and record the performance and the health status of many components, assets, and systems. Such data may take the form of online monitoring data, condition reports, and maintenance reports and it carries the potential to provide system engineers with insights into anomalous behaviors or degradation trends as well as the possible causes behind them and to predict their direct consequences. The analysis of such data poses however few challenges. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly tackles these challenges, and it focuses on the integration of all these data elements in order to assist plant system engineers in analyzing component, assets, and systems performances and optimize maintenance activities. This is performed by 1) extracting knowledge from textual data via technical language processing methods, and 2) quantifying system, asset, and component health from numeric condition-based data. We rely on model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Numeric and textual data elements are then associated with an MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1993

Health monitoring of Japanese payload specialist: Autonomic nervous and cardiovascular responses under reduced gravity condition (L-0)

In addition to health monitoring of the Japanese Payload Specialists (PS) during the flight, this investigation also focuses on the changes of cardiovascular hemodynamics during flight which will be conducted under the science collaboration with the Lower Body Negative Pressure (LBNP) Experiment of NASA. For the Japanese, this is an opportunity to examine firsthand the effects of microgravity of human physiology. We are particularly interested in the adaption process and how it relates to space motion sickness and cardiovascular deconditioning. By comparing data from our own experiment to data collected by others, we hope to understand the processes involved and find ways to avoid these problems for future Japanese astronauts onboard Space Station Freedom and other Japanese space ventures. The primary objective of this experiment is to monitor the health condition of Japanese Payload Specialists to maintain a good health status during and after space flight. The second purpose is to investigate the autonomic nervous system's response to space motion sickness. To achieve this, the function of the autonomic nervous system will be monitored using non-invasive techniques. Data obtained will be employed to evaluate the role of autonomic nervous system in space motion sickness and to predict susceptibility to space motion sickness. The third objective is evaluation of the adaption process of the cardiovascular system to microgravity. By observation of the hemodynamics using an echocardiogram we will gain insight on cardiovascular deconditioning. The last objective is to create a data base for use in the health care of Japanese astronauts by obtaining control data in experiment L-O in the SL-J mission.

Sekiguchi, Chiharu↗

NASA NTRS1991

Monitoring Engine Vibrations And Spectrum Of Exhaust

Real-time computation of intensities of peaks in visible-light emission spectrum of exhaust combined with real-time spectrum analysis of vibrations into developmental monitoring technique providing up-to-the-second information on conditions of critical bearings in engine. Conceived to monitor conditions of bearings in turbopump suppling oxygen to Space Shuttle main engine, based on observations that both vibrations in bearings and intensities of visible light emitted at specific wavelengths by exhaust plume of engine indicate wear and incipient failure of bearings. Applicable to monitoring "health" of other machinery via spectra of vibrations and electromagnetic emissions from exhausts. Concept related to one described in "Monitoring Bearing Vibrations For Signs Of Damage", (MFS-29734).

Martinez, Carol L.↗

NASA NTRS2004

How Can AERONET Help with Monitoring Clouds?

When conditions are inappropriate to make AErosol RObotic NETwork (AERONET) measurements for aerosol studies, new measurements related to cloud physics can be made instead. As such, several AERONET CIMEL sunphotometers have been equipped with a new "cloud mode." This mode allows the CIMELs to make measurements of zenith radiance when the Sun in blocked by clouds. When in cloud mode, a CIMEL points straight up every 10-15 minutes and takes 10 measurements over a 9 second time interval at four wavelengths: 440,670,870, and 1020 nm. For cloudy conditions above green vegetation, the spectral contrast in surface albedo dominates over Rayleigh and aerosol effects. We have developed a new method for retrieving cloud optical depth, even for broken clouds, that uses data from the 670 (and/or 440) and 870 nm channels. In addition to cloud optical depth, the method also infers a "radiatively effective" cloud fraction. The results of the retrievals at the ARM Central Facility in Oklahoma are compared with the ones from Microwave Radiometer (MWR) and Multi-Filter Rotating Shadowband Radiometers (MFRSR).

Marshak, A.↗

NASA NTRS1991

Thermographic monitoring of materials under simulated reentry conditions

Thermography can be used to measure surface temperature gradients graphically, and can be used under conditions where the direct measurement of temperature at all desired points, using thermocouples or resistance temperature detectors (RTDs), is either difficult or impossible. The use of thermographic monitoring during a series of arc-jet tests is described. Described in this work are the issues that influence interpretation of the thermographic measurements under these conditions, including the calculation of effective emittance and window transmittance from the spectral properties of the materials in order to calculate the temperature distribution of a surface directly from the measured radiance. Comparison of the surface temperatures measured using thermocouples and the temperatures derived from thermographic measurements show good agreement. The data gathered will be used to evaluate important test parameters such as the heating distribution across the surface of a heat shield test model and at steps on the model surface.

White, Susan M.↗

DOE OSTI2026

Monitored Fluctuating Hydrodynamics

We introduce a hydrodynamic framework for describing monitored classical stochastic processes. We study the conditional ensembles for these monitored processes—i.e., we compute spacetime correlation functions conditioned on a fixed, typical measurement record. In the presence of global symmetries we show that these conditional ensembles can undergo measurement-induced “sharpening” phase transitions as a function of the monitoring rate; moreover, even weak monitoring can give rise to novel critical phases, derived entirely from a classical perspective. We give a simple hydrodynamic derivation of the known “charge-fuzzy phase” for weakly monitored diffusive many-body quantum systems. We show that although the unmonitored symmetric and asymmetric exclusion processes are in different universality classes of transport, the fluctuations in their conditional ensembles flow to the same fixed point with emergent relativistic invariance under monitoring. On the other hand, weakly monitored systems with non-Abelian symmetries enter a novel strongly coupled fixed point with nontrivial dynamical exponent, which we characterize. Our formalism naturally accounts for monitoring general observables, such as currents or density gradients, and allows for a direct calculation of information-theoretic diagnostics of sharpening transitions, including the Shannon entropy of the measurement record.

classical statistical mechanics↗

DOE OSTI2025

Tests of a High Temperature Sample Conditioner for the Waste Treatment Plant LV-S2, LV S3, HV S3A and HV-S3B Exhaust Systems

Tests were performed to evaluate a sample conditioning unit for stack monitoring at Hanford Tank Waste Treatment and Immobilization Plant (WTP) exhaust stacks with elevated air temperatures. The LV-S2, LV-S3, HV-S3A and HV-S3B exhaust stacks are expected to have elevated air temperature and dew point. At these emission points, exhaust temperatures are too high to deliver the air sample directly to the required stack monitoring equipment. As a result, a sample conditioning system is considered to cool and dry the air prior to its delivery to the stack monitoring system. The method proposed for the sample conditioning is a dilution system that will introduce cooler, dry air to the air sample stream. This method of sample conditioning is meant to reduce the sample temperature while avoiding condensation of moisture in the sample stream. An additional constraint is that the ANSI/HPS N13.1-1999 standard states that at least 50% of the 10 µm aerodynamic diameter (AD) particles present in the stack free stream must be delivered to the sample collector. In other words, depositional loss of particles should be limited to 50% in the sampling, transport, and conditioning systems. Based on estimates of particle penetration through the LV-S3 sampling system, the diluter should perform with about 80% penetration or better to ensure that the total sampling system passes the 50% or greater penetration criterion.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2022

Gatlinburg & Beatty Wildfires: Evaluating the Role of Soil Moisture in Determining Vegetation Health, Fuel Loads, and Wildfires in the Gatlinburg and Beatty Wildfires

Wildfire potential monitoring, which is increasingly vital under climate change-induced droughts, could be improved by incorporating remotely-sensed soil moisture data. To better understand the connections between soil moisture and vegetation health, stakeholders are interested in using soil moisture data in the development of fire-related indices. NASA DEVELOP partnered with the Desert Research Institute’s Western Regional Climate Center (WRCC), NOAA’s National Integrated Drought Information System (NIDIS), the North Carolina State Climate Office, and Oklahoma State University to evaluate how measures of remotely-sensed standardized soil moisture compare to vegetation health and fire fuel indices in a case study of two fire events: the 2016 Chimney Tops 2 Fire near Gatlinburg, Tennessee and the 2021 Bootleg Fire near Beatty, Oregon. The team visualized vegetation change six months prior to each event using spectral vegetation indices observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra satellite and the Keetch-Byram Drought Index (KBDI). These visualizations were compared to soil moisture data from European Space Agency's (ESA) Climate Change Initiative Soil Moisture (CCI SM) project, collected in part by the Soil Moisture Active Passive (SMAP) satellite. Overall, period of record percentiles and fraction of available water standardizations correlated more strongly with fuel load and vegetation indices, indicating their utility for fire potential monitoring. Soil moisture conditions remained exceptionally dry for several months before the Chimney Tops 2 Fire whereas drought conditions only intensified immediately prior to the Bootleg Fire. This indicates greater sensitivity to drought conditions under Western fire regimes. These findings will inform partners’ monitoring of wildfire potential in both regions and development of early warning systems.

soil moisture↗

NASA NTRS2004

Comparison of Satellite and Aircraft Measurements of Cloud Microphysical Properties in Icing Conditions During ATREC/AIRS-II

Satellites are ideal for continuous monitoring of aircraft icing conditions in many situations over extensive areas. The satellite imager data are used to diagnose a number of cloud properties that can be used to develop icing intensity indices. Developing and validating these indices requires comparison with objective "cloud truth" data in addition to conventional pilot reports (PIREPS) of icing conditions. Minnis et al. examined the relationships between PIREPS icing and satellite-derived cloud properties. The Atlantic-THORPEX Regional Campaign (ATReC) and the second Alliance Icing Research Study (AIRS-II) field programs were conducted over the northeastern USA and southeastern Canada during late 2003 and early 2004. The aircraft and surface measurements are concerned primarily with the icing characteristics of clouds and, thus, are ideal for providing some validation information for the satellite remote sensing product. This paper starts the process of comparing cloud properties and icing indices derived from the Geostationary Operational Environmental Satellite (GOES) with the aircraft in situ measurements of several cloud properties during campaigns and some of the The comparisons include cloud phase, particle size, icing intensity, base and top altitudes, temperatures, and liquid water path. The results of this study are crucial for developing a more reliable and objective icing product from satellite data. This icing product, currently being derived from GOES data over the USA, is an important complement to more conventional products based on forecasts, and PIREPS.

Nguyen, Louis↗