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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Continued Development of Compact Multi-gas Monitor for Life Support Systems Control in Space

Miniature optic gas sensors (MOGS) based on luminescent materials have shown great potential as alternatives to Near-Infrared-based gas sensor systems for the advanced space suit portable life support system (PLSS). The unique capability of MOGS for carbon dioxide and oxygen monitoring under wet conditions has been reported, as has the fast recovery of MOGS humidity sensors after long periods of being wet. Lower volume and power requirements are also potential advantages of MOGS over both traditional and advanced Non-Dispersive Infrared (NDIR) gas sensors, which have shown so far longer life than luminescent sensors. This paper presents the most recent results in the development and analytical validation of a compact multi-gas sensor unit based on luminescent sensors for the PLSS. Results of extensive testing are presented, including studies conducted at Intelligent Optical Systems laboratories, a United Technology Corporation Aerospace Systems (UTAS) laboratory, and a Johnson Space Center laboratory. The potential of this sensor technology for gas monitoring in PLSSs and other life support systems and the advantages and limitations found through detailed sensor validation are discussed.

Delgado-Alonso, Jesús↗
NASA NTRS2010

An Integrated Architecture for On-Board Aircraft Engine Performance Trend Monitoring and Gas Path Fault Diagnostics

Aircraft engine performance trend monitoring and gas path fault diagnostics are closely related technologies that assist operators in managing the health of their gas turbine engine assets. Trend monitoring is the process of monitoring the gradual performance change that an aircraft engine will naturally incur over time due to turbomachinery deterioration, while gas path diagnostics is the process of detecting and isolating the occurrence of any faults impacting engine flow-path performance. Today, performance trend monitoring and gas path fault diagnostic functions are performed by a combination of on-board and off-board strategies. On-board engine control computers contain logic that monitors for anomalous engine operation in real-time. Off-board ground stations are used to conduct fleet-wide engine trend monitoring and fault diagnostics based on data collected from each engine each flight. Continuing advances in avionics are enabling the migration of portions of the ground-based functionality on-board, giving rise to more sophisticated on-board engine health management capabilities. This paper reviews the conventional engine performance trend monitoring and gas path fault diagnostic architecture commonly applied today, and presents a proposed enhanced on-board architecture for future applications. The enhanced architecture gains real-time access to an expanded quantity of engine parameters, and provides advanced on-board model-based estimation capabilities. The benefits of the enhanced architecture include the real-time continuous monitoring of engine health, the early diagnosis of fault conditions, and the estimation of unmeasured engine performance parameters. A future vision to advance the enhanced architecture is also presented and discussed

Simon, Donald L.↗
NASA NTRS2011

Future Opportunities and Challenges in Remote Sensing of Drought

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and global efforts such as the Famine and Early Warning System (FEWS), National Integrated Drought Information System (NIDIS), and Group on Earth Observations (GEO), as well as the establishment of regional drought centers (e.g., European Drought Observatory) and geospatial visualization and monitoring systems (e.g, NASA SERVIR) have been undertaken to improve drought monitoring and early warning systems throughout the world. The suite of innovative remote sensing tools that have recently emerged will be looked upon to fill important data and knowledge gaps (NIDIS, 2007; NRC, 2007) to address a wide range of drought-related issues including food security, water scarcity, and human health.

Wardlow, Brian D.↗
NASA NTRS2021

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods toforecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

Machine Learning↗
NASA NTRS1984

Aeronautical propulsion: Present status and future directions

The advancement of aeropropulsion systems continues to provide technology to various portions of the gas turbine field. It is recognized that this area is undergoing considerale change, which will result in substantially improved gas turbine components and systems. These changes are occurring in a number of technical areas including advanced analytical and physical measurement methods, the application of large scientific computers, the dynamic modeling of components and systems, the application of integrated control systems that optimize and improve performance and system condition monitoring, and the development of new and unique materials and structures. As these areas evolve, the ways in which technology will advance, and factors affecting the design and development of new systems, will probably be considerably different than those of today. It is also anticipated that the necessary skilled work force will be different. Certainly there will be changes, but the nature, extent, and rate of those changes can only be surmised at this time.

Hartmann, M. J.↗
NASA NTRS1988

Knowledge-Acquisition Tool For Expert System

Digital flight-control systems monitored by computer program that evaluates and recommends. Flight-systems engineers for advanced, high-performance aircraft use knowlege-acquisition tool for expert-system flight-status monitor suppling interpretative data. Interpretative function especially important in time-critical, high-stress situations because it facilitates problem identification and corrective strategy. Conditions evaluated and recommendations made by ground-based engineers having essential knowledge for analysis and monitoring of performances of advanced aircraft systems.

Disbrow, James D.↗
NASA NTRS1986

Early warning and crop condition assessment research

The Early Warning Crop Condition Assessment Project of AgRISTARS was a multiagency and multidisciplinary effort. Its mission and objectives were centered around development and testing of remote-sensing techniques that enhance operational methodologies for global crop-condition assessments. The project developed crop stress indicators models that provide data filter and alert capabilities for monitoring global agricultural conditions. The project developed a technique for using NOAA-n satellite advanced very-high-resolution radiometer (AVHRR) data for operational crop-condition assessments. This technology was transferred to the Foreign Agricultural Service of the USDA. The project developed a U.S. Great Plains data base that contains various meteorological parameters and vegetative index numbers (VIN) derived from AVHRR satellite data. It developed cloud screening techniques and scan angle correction models for AVHRR data. It also developed technology for using remotely acquired thermal data for crop water stress indicator modeling. The project provided basic technology including spectral characteristics of soils, water, stressed and nonstressed crop and range vegetation, solar zenith angle, and atmospheric and canopy structure effects.

Boatwright, G. O.↗
NASA NTRSDate not supplied

Runtime Monitoring for Unmanned Aerospace Systems with Neural Network Components

AI components (e.g., Deep Neural Networks) are increasingly used in unmanned Aerospace systems for safety-relevant applications. Rigorous Verification and Validation methods for such components are still in their infancy and thus, monitoring of the AI's behavior during runtime is essential. In this paper, we will present a runtime-monitoring architecture, which combines the advanced statistical analysis framework SYSAI (System Analysis using Statistical AI) with temporal and probabilistic runtime monitoring carried out by R2U2 (Realizable, Responsive, and Unobtrusive Unit). Learned statistical models of complex systems with AI components are produced by the SYSAI framework and provide detailed information to enable the R2U2 runtime monitor to efficiently perform advanced safety and performance checks in nominal and off-nominal conditions. We will present initial results of our tool set and architecture on a case study, a DNN-based autonomous centerline tracking system (ACT).

Yuning He↗
NASA NTRS1984

Reusable rocket engine turbopump condition monitoring

Significant improvements in engine readiness with reductions in maintenance costs and turn-around times can be achieved with an engine condition monitoring systems (CMS). The CMS provides health status of critical engine components, without disassembly, through monitoring with advanced sensors. Engine failure reports over 35 years were categorized into 20 different modes of failure. Rotor bearings and turbine blades were determined to be the most critical in limiting turbopump life. Measurement technologies were matched to each of the failure modes identified. Three were selected to monitor the rotor bearings and turbine blades: the isotope wear detector and fiberoptic deflectometer (bearings), and the fiberoptic pyrometer (blades). Signal processing algorithms were evaluated for their ability to provide useful health data to maintenance personnel. Design modifications to the Space Shuttle Main Engine (SSME) high pressure turbopumps were developed to incorporate the sensors. Laboratory test fixtures have been designed for monitoring the rotor bearings and turbine blades in simulated turbopump operating conditions.

Hampson, M. E.↗
NASA NTRS2012

Prognostics for Microgrid Components

Prognostics is the science of predicting future performance and potential failures based on targeted condition monitoring. Moving away from the traditional reliability centric view, prognostics aims at detecting and quantifying the time to impending failures. This advance warning provides the opportunity to take actions that can preserve uptime, reduce cost of damage, or extend the life of the component. The talk will focus on the concepts and basics of prognostics from the viewpoint of condition-based systems health management. Differences with other techniques used in systems health management and philosophies of prognostics used in other domains will be shown. Examples relevant to micro grid systems and subsystems will be used to illustrate various types of prediction scenarios and the resources it take to set up a desired prognostic system. Specifically, the implementation results for power storage and power semiconductor components will demonstrate specific solution approaches of prognostics. The role of constituent elements of prognostics, such as model, prediction algorithms, failure threshold, run-to-failure data, requirements and specifications, and post-prognostic reasoning will be explained. A discussion on performance evaluation and performance metrics will conclude the technical discussion followed by general comments on open research problems and challenges in prognostics.

Saxena, Abhinav↗
NASA NTRS2012

Creating a Realistic Weather Environment for Motion-Based Piloted Flight Simulation

A flight simulation environment is being enhanced to facilitate experiments that evaluate research prototypes of advanced onboard weather radar, hazard/integrity monitoring (HIM), and integrated alerting and notification (IAN) concepts in adverse weather conditions. The simulation environment uses weather data based on real weather events to support operational scenarios in a terminal area. A simulated atmospheric environment was realized by using numerical weather data sets. These were produced from the High-Resolution Rapid Refresh (HRRR) model hosted and run by the National Oceanic and Atmospheric Administration (NOAA). To align with the planned flight simulation experiment requirements, several HRRR data sets were acquired courtesy of NOAA. These data sets coincided with severe weather events at the Memphis International Airport (MEM) in Memphis, TN. In addition, representative flight tracks for approaches and departures at MEM were generated and used to develop and test simulations of (1) what onboard sensors such as the weather radar would observe; (2) what datalinks of weather information would provide; and (3) what atmospheric conditions the aircraft would experience (e.g. turbulence, winds, and icing). The simulation includes a weather radar display that provides weather and turbulence modes, derived from the modeled weather along the flight track. The radar capabilities and the pilots controls simulate current-generation commercial weather radar systems. Appropriate data-linked weather advisories (e.g., SIGMET) were derived from the HRRR weather models and provided to the pilot consistent with NextGen concepts of use for Aeronautical Information Service (AIS) and Meteorological (MET) data link products. The net result of this simulation development was the creation of an environment that supports investigations of new flight deck information systems, methods for incorporation of better weather information, and pilot interface and operational improvements for better aviation safety. This research is part of a larger effort at NASA to study the impact of the growing complexity of operations, information, and systems on crew decision-making and response effectiveness; and then to recommend methods for improving future designs.

Daniels, Taumi S.↗
NASA NTRS1992

Intelligent sensor-model automated control of PMR-15 autoclave processing

An intelligent sensor model system has been built and used for automated control of the PMR-15 cure process in the autoclave. The system uses frequency-dependent FM sensing (FDEMS), the Loos processing model, and the Air Force QPAL intelligent software shell. The Loos model is used to predict and optimize the cure process including the time-temperature dependence of the extent of reaction, flow, and part consolidation. The FDEMS sensing system in turn monitors, in situ, the removal of solvent, changes in the viscosity, reaction advancement and cure completion in the mold continuously throughout the processing cycle. The sensor information is compared with the optimum processing conditions from the model. The QPAL composite cure control system allows comparison of the sensor monitoring with the model predictions to be broken down into a series of discrete steps and provides a language for making decisions on what to do next regarding time-temperature and pressure.

Hart, S.↗
NASA NTRS1999

Monitoring and Modeling Astronaut Occupational Radiation Exposures in Space: Recent Advances

In 1982 astronauts were declared to be radiation workers by OSHA, and as such were subject to the rules and regulations applied to that group. NASA was already aware that space radiation was a hazard to crewmembers and had been studying and monitoring astronaut doses since 1962 at the Johnson Space Center. It was quickly realized NASA would not be able to accomplish all of its goals if the astronauts were subject to the ground based radiation worker limits, and thus received a waiver from OSHA to establish independent limits. As part of the stipulation attached to setting new limits, OSHA included a requirement to perform preflight dose projections for each crew and inform them of the associated risks. Additional requirements included measuring doses from various sources during the flight, making every effort to prevent a crewmember from exceeding the new limits, and keeping all exposures As Low As Reasonably Achievable (a.k.a. ALARA - a common health physics principle). The assembly of the International Space Station (ISS) and its initial manned operations will coincide with the 4-5 year period of high space weather activity at the next maximum in the solar cycle. For the first time in NASA's manned program, US astronauts will be in orbit continuously throughout a solar maximum period. During this period, crews are at risk of significantly increased radiation exposures due to solar particle events and trapped electron belt enhancements following geomagnetic storms. The problem of protecting crews is compounded by the difficulty of providing continuous real-time monitoring over a period of a decade in an era of tightly constrained budgets. In order to prepare for ISS radiological support needs, the NASA Space Radiation Analysis Group and the NOAA Space Environment Center have undertaken a multiyear effort to improve and automate ground-based space weather monitoring systems and real-time radiation analysis tools. These improvements include a coupled, automated space weather monitoring and alarm system--SPE exposure analysis system, an advanced space weather data distribution and display system, and a high-fidelity space weather simulation system. In addition, significant new real-time space weather data sets, which will enhance the forecasting and now-casting of near-Earth space environment conditions, are being made available through unique NASA-NOAA-USAF collaborations. These new data sets include coronal mass ejection monitoring by the Solar and Heliospheric Observatory (SOHO) and in-situ plasma and particle monitoring at the L1 libration point by the Solar Wind Monitor (SWIM) and Advanced Composition Explorer (ACE) spacecraft. Advanced real-time radiation monitoring data from charged particle telescopes and tissue equivalent proportional counters will also be available to assist crew and flight controllers in monitoring the external and intravehicular radiation environment.

Weyland, Mark↗
NASA NTRS2018

Microbial Monitoring and the Risk of Infectious Disease Aboard the International Space Station

The International Space Station (ISS) is a semi-closed habitat in low Earth orbit with environmental conditions provided by an advanced life support system that controls temperature and recycles air and most of the potable water. The crew's activities, such as eating, sleeping, hygiene, and laboratory research, are performed in relatively close proximity. Research in the laboratory includes a myriad of experiments, including those with rodents, plants, and pathogenic microorganisms. Despite these conditions, in-flight monitoring of ISS indicates that the microbial diversity is similar to homes on earth. Accordingly, the crew is generally very healthy, however infectious disease does occur and potential routes of infection by obligate and opportunistic pathogens cannot be completely prevented. Determining the extent of this risk is further complicated, as microorganisms can alter their characteristics in response to spaceflight culture, as exemplified by the increase in virulence of the enteric pathogen Salmonella enterica Typhimurium during spaceflight compared to otherwise identical cultures grown on Earth. Taken together, these factors suggest a need for continued microbiological monitoring and research to understand and mitigate the risk of infectious disease during long duration missions.

Ott, C. Mark↗
NASA NTRS2012

Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable.

Bolten, John↗
NASA NTRS2012

Development of RGB Composite Imagery for Operational Weather Forecasting Applications

The NASA Short-term Prediction Research and Transition (SPoRT) Center, in collaboration with the Cooperative Institute for Research in the Atmosphere (CIRA), is providing red-green-blue (RGB) color composite imagery to several of NOAA s National Centers and National Weather Service forecast offices as a demonstration of future capabilities of the Advanced Baseline Imager (ABI) to be implemented aboard GOES-R. Forecasters rely upon geostationary satellite imagery to monitor conditions over their regions of responsibility. Since the ABI will provide nearly three times as many channels as the current GOES imager, the volume of data available for analysis will increase. RGB composite imagery can aid in the compression of large data volumes by combining information from multiple channels or paired channel differences into single products that communicate more information than provided by a single channel image. A standard suite of RGB imagery has been developed by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), based upon the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The SEVIRI instrument currently provides visible and infrared wavelengths comparable to the future GOES-R ABI. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the NASA Terra and Aqua satellites can be used to demonstrate future capabilities of GOES-R. This presentation will demonstrate an overview of the products currently disseminated to SPoRT partners within the GOES-R Proving Ground, and other National Weather Service forecast offices, along with examples of their application. For example, CIRA has used the channels of the current GOES sounder to produce an "air mass" RGB originally designed for SEVIRI. This provides hourly imagery over CONUS for looping applications while demonstrating capabilities similar to the future ABI instrument. SPoRT has developed similar "air mass" RGB imagery from MODIS, and through a case study example, synoptic-scale features evident in single-channel water vapor imagery are shown in the context of the air mass product. Other products, such as the "nighttime microphysics" RGB, are useful in the detection of low clouds and fog. Nighttime microphysics products from MODIS offer some advantages over single-channel or spectral difference techniques and will be discussed in the context of a case study. Finally, other RGB products from SEVIRI are being demonstrated as precursors to GOES-R within the GOES-R Proving Ground. Examples of "natural color" and "dust" imagery will be shown with relevant applications.

Molthan, Andrew L.↗
NASA NTRS2004

Development of Advanced Low Conductivity Thermal Barrier Coatings

Advanced multi-component, low conductivity oxide thermal barrier coatings have been developed using an approach that emphasizes real-time monitoring of thermal conductivity under conditions that are engine-like in terms of temperatures and heat fluxes. This is in contrast to the traditional approach where coatings are initially optimized in terms of furnace and burner rig durability with subsequent measurement in the as-processed or furnace-sintered condition. The present work establishes a laser high-heat-flux test as the basis for evaluating advanced plasma-sprayed and electron beam-physical vapor deposited (EB-PVD) thermal barrier coatings under the NASA Ultra-Efficient Engine Technology (UEET) Program. The candidate coating materials for this program are novel thermal barrier coatings that are found to have significantly reduced thermal conductivities and improved thermal stability due to an oxide-defect-cluster design. Critical issues for designing advanced low conductivity coatings with improved coating durability are also discussed.

Zhu, Dong-Ming↗