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NASA NTRS2006

Progress in Development of an Airborne Turbulence Detection System

Aircraft encounters with turbulence are the leading cause of in-flight injuries (Tyrvanas 2003) and have occasionally resulted in passenger and crew fatalities. Most of these injuries are caused by sudden and unexpected encounters with severe turbulence in and around convective activity (Kaplan et al 2005). To alleviate this problem, the Turbulence Prediction and Warning Systems (TPAWS) element of NASA s Aviation Safety program has investigated technologies to detect and warn of hazardous in-flight turbulence. This effort has required the numerical modeling of atmospheric convection: 1) for characterizing convectively induced turbulence (CIT) environments, 2) for defining turbulence hazard metrics, and 3) as a means of providing realistic three-dimensional data sets that can be used to test and evaluate turbulence detection sensors. The data sets are being made available to industry and the FAA for certification of future airborne turbulence-detection systems (ATDS) with warning capability. Early in the TPAWS project, a radar-based ATDS was installed and flight tested on NASA s research aircraft, a B-757. This ATDS utilized new algorithms and hazard metrics that were developed for use with existing airborne predictive windshear radars, thus avoiding the installation of new hardware. This system was designed to detect and warn of hazardous CIT even in regions with weak radar reflectivity (i.e. 5-15 dBz). Results from an initial flight test of the ATDS were discussed in Hamilton and Proctor (2002a; 2002b). In companion papers (Proctor et al 2002a; 2002b), a numerical simulation of the most significant encounter from that flight test was presented. Since the presentation of these papers a second flight test has been conducted providing additional cases for examination. In this paper, we will present results from NASA s flight test and a numerical model simulation of a turbulence environment encountered on 30 April 2002. Progress leading towards FAA certification of industry built ATDS will also be discussed.

Hamilton, David W.↗

NASA NTRS2018

Monitoring Intense Thunderstorms in the Hindu-Kush Himalayan Region

SERVIR Goal: Use NASA EOS (Earth Observing System) assets to build early warning capabilities and facilitate timely disaster response for high impact weather events in the HKH (Hindu-Kush Himalayan) region. Objectives: 1. High Impact Weather Assessment Toolkit(HIWAT) for the HKH region; 2. Jointly develop HIWAT capabilities/training with ICIMOD (International Centre for Integrated Mountain Development); 3. Demonstrate capability in end-user environment; 4. Transition HIWAT to ICIMOD.

thunderstorms↗

NASA NTRS2015

ACCEPT: Introduction of the Adverse Condition and Critical Event Prediction Toolbox

The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we introduce a generic framework developed in MATLAB (sup registered mark) called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox). ACCEPT is an architectural framework designed to compare and contrast the performance of a variety of machine learning and early warning algorithms, and tests the capability of these algorithms to robustly predict the onset of adverse events in any time-series data generating systems or processes.

machine learning↗

NASA NTRS2001

Incorporation of EGPWS in the NASA Ames Research Center 747-400 Flight Simulator

The NASA Ames Research Center CAE Boeing 747300 flight simulator is used primarily for the study of human factors in aviation safety. The simulator is constantly upgraded to maintain a configuration match to a specific United Airlines aircraft and maintains the highest level of FAA certification to ensure credibility to the results of research programs. United's 747-400 fleet and hence the simulator are transitioning from the older Ground Proximity Warning System (GPWS) to the state-of-the-art Enhanced Ground Proximity Warning System (EGPWS). GPWS was an early attempt to reduce or eliminate Controlled Flight Into Terrain (CFIT). Basic GPWS alerting modes include: excessive descent rate, excessive terrain closure rate, altitude loss after takeoff, unsafe terrain clearance, excessive deviation below glideslope, advisory callouts and windshear alerting. However, since GPWS uses the radar altimeter which looks straight down, ample warning is not always provided. EGPWS retains all of the basic functions of GPWS but adds the ability to look ahead by comparing the aircraft position to an internal database and provide additional alerting and display capabilities. This paper evaluates three methods of incorporating EGPWS in the simulator and describes the implementation and architecture of the preferred option.

Sallant, Ghislain↗

NASA NTRS2020

MSL Telecom Automated Anomaly Detection

The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.

Mukai, Ryan↗

NASA NTRS2020

Kenya Food Security & Agriculture II: Utilizing NASA Earth Observations to Enhance Drought Warning Systems and Develop Capacity to Use the RHEAS Model in Kenya

Twenty-three counties in Kenya experience frequent drought, which damages agricultural productivity and threatens the health and well being of millions. NASA DEVELOP partnered with NASA SERVIR, the Regional Centre for Mapping of Resources for Development (RCMRD) and Kenya’s National Drought Management Authority (NDMA) to enhance drought-detection capacity using NASA Earth observations. Currently, the NDMA publishes monthly Early Warning Bulletins with drought conditions for each arid or semi-arid county in Kenya. These bulletins utilize the Vegetation Condition Index (VCI) from the Moderate Resolution ImagingSpectroradiometer (MODIS) as well as a variety of biophysical, social and economic drought indicators, but do not allow for advanced forecasting. To improve drought-monitoring capabilities, the team created a Combined Drought Indicator (CDI) using the Regional Hydrologic Extremes Assessment System (RHEAS) model and data from Aqua and Terra MODIS and the National Centers for Environmental Prediction. The CDI combined precipitation anomalies, soil moisture anomalies, evaporative stress, and VCI according to weights determined by principal component analysis. To evaluate the performance of the CDI across Kenya, the team compiled a dataset of drought events from historical reports and the NDMA’s records. Results suggested that the CDI detected drought earlier thanVCI alone. However, more validation is needed to ensure that the CDI accurately and consistently detects drought earlier than current warning systems. In order to better understand the behavior of individual indices and the potential for earlier drought detection, the team also conducted a time-lag analysis. The team found that VCI responds to drought, on average, one month later than most other indices included in the CDI, suggesting that incorporating additional indices could improve early drought warning systems in Kenya.

Food Security & Agriculture↗

NASA NTRS2020

Kenya Food Security & Agriculture II: Utilizing NASA Earth Observations to Enhance Drought Warning Systems and Develop Capacity to Use the RHEAS Model in Kenya

Twenty-three counties in Kenya experience frequent drought, which damages agricultural productivity and threatens the health and well being of millions. NASA DEVELOP partnered with NASA SERVIR, the Regional Centre for Mapping of Resources for Development (RCMRD) and Kenya’s National Drought Management Authority (NDMA) to enhance drought-detection capacity using NASA Earth observations. Currently, the NDMA publishes monthly Early Warning Bulletins with drought conditions for each arid or semi-arid county in Kenya. These bulletins utilize the Vegetation Condition Index (VCI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) as well as a variety of biophysical, social and economic drought indicators, but do not allow for advanced forecasting. To improve drought-monitoring capabilities, the team created a Combined Drought Indicator (CDI) using the Regional Hydrologic Extremes Assessment System (RHEAS) model and data from Aqua and Terra MODIS and the National Centers for Environmental Prediction.The CDI combined precipitation anomalies, soil moisture anomalies, evaporative stress, and VCI according to weights determined by principal component analysis. To evaluate the performance of the CDI across Kenya, the team compiled a dataset of drought events from historical reports and the NDMA’s records. Results suggested that the CDI detected drought earlier than VCI alone. However, more validation is needed to ensure that the CDI accurately and consistently detects drought earlier than current warning systems. In order to better understand the behavior of individual indices and the potential for earlier drought detection, the team also conducted a time-lag analysis. The team found that VCI responds to drought, on average, one month later than most other indices included in the CDI, suggesting that incorporating additional indices could improve early drought warning systems in Kenya.

Food Security & Agriculture↗

NASA NTRS2001

Satellites as Sentinels for Health

Remotely-sensed data and observations are providing powerful new tools for addressing the human health aspects of sustainability by enabling improved understanding of the relationships and linkages between health-related environmental parameters and society as well as techniques for early warning of potential health problems. Remote sensing, geographic information systems, improved computational capabilities, and interdisciplinary research between the Earth and health science communities are being combined in rich collaborative efforts resulting in more rapid problem-solving, early warning, and prevention in global health issues. This paper provides a number of recent examples of applications of these technologies to health issues related to the following: infectious and vector-borne diseases; urban, regional and global air pollution; heat stress; UV radiation; water-borne disease; extreme weather; contaminant pathways (ocean, atmosphere, ice).

Maynard, Nancy G.↗

NASA NTRS2002

Combining Sense and Intelligence for Smart Structures

IFOS developed the I*Sense technology with assistance from a NASA Langley Research Center SBIR contract. NASA and IFOS collaborated to create sensing network designs that have high sensitivity, low power consumption, and significant potential for mass production. The joint- research effort led to the development of a module that is rugged, compact and light-weight, and immune to electromagnetic interference. These features make the I*Sense multisensor arrays favorable for smart structure applications, including smart buildings, bridges, highways, dams, power plants, ships, and oil tankers, as well as space vehicles, space stations, and other space structures. For instance, the system can be used as an early warning and detection device, with alarms being set to monitor the maximum allowable strain and stress values at various points of a given structure.

Source record↗

NASA NTRS1995

Analyzing rocket plume spectral data with neural networks

The Optical Plume Anomaly Detection (OPAD) system is under development to provide early-warning failure detection in support of ground-level testing of the Space Shuttle Main Engine (SSME). Failure detection is to be achieved through the acquisition of spectrally resolved plume emissions and subsequent identification of abnormal levels indicative of engine corrosion or component failure. Two computer codes (one linear and the other non-linear) are used by the OPAD system to iteratively determine specific element concentrations in the SSME plume, given emission intensity and wavelength information. Since this analysis is extremely labor intensive, a study was initiated to develop neural networks that would model the 'inverse' of these computer codes. Optimally connected feed-forward networks with imperceptible prediction error have been developed for each element modeled by the linear code, SPECTRA4. Radial basis function networks were developed for the non-linear code, SPECTRA5, and predict combustion temperature in addition to element concentrations.

Whitaker, Kevin W.↗

NASA NTRS2015

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013.

Funk, Chris↗

NASA NTRS2015

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

Funk, Chris↗

NASA NTRS1987

EUREX D: An expert system for failure diagnosis and recovery in the TCS of the European retrievable carrier EURECA

An expert system for diagnosis and recovery of failures in the Freon cooling loop of the European retrievable experiment carrier EURECA is described. The system demonstrates the feasibility of a functional scope of expert diagnostic systems which appears to be essential for practical applications of such systems in space technology. The scope includes early warning and treatment of incomplete information, fault tolerance, identification of failure superpositions, intelligent reaction to unforeseen events, and detailed status display for optimal recovery action.

Kellner, A.↗

NASA NTRS1984

Microburst wind structure and evaluation of Doppler radar for airport wind shear detection

The horizontal and vertical structure of airflow within microbursts has been determined using Doppler weather radar data from the Joint Airport Weather Studies (JAWS) Project. It is shown that the downdraft typically associated with microbursts is about 1 km wide and begins to spread horizontally at a height below 1 km. The median time from initial divergence at the surface to maximum differential wind velocity across the microburst is five minutes. The height of maximum differential velocity is about 75 m, and the median velocity differential is 22 m/s over an average distance of 3.1 km. The outflow of the air is asymmetric, averaging twice as strong along the maximum axis compared to the mininum axis. Some technical requirements for a radar system to detect microbursts and to provide aircraft with early warnings of the onset of windshear are identified.

Wilson, J. W.↗

NASA NTRS2003

Air Monitoring for Hazardous Gas Detection

The Hazardous Gas Detection Lab is involved in the design and development of instrumentation that can detect and quantify various hazardous gases. Traditionally these systems are designed for leak detection of the cryogenic gases used for the propulsion of the Shuttle and other vehicles. Mass spectrometers are the basis of these systems, which provide excellent quantitation, sensitivity, selectivity, response and limits of detection. Unfortunately, these systems are large, heavy and expensive. This feature limits the ability to perform gas analysis in certain applications. Smaller and lighter mass spectrometer systems could be used in many more applications primarily due to the portability of the system. Such applications would include air analysis in confined spaces, in-situ environmental analysis and emergency response. In general, system cost is lowered as size is reduced. With a low cost air analysis system, several systems could be utilized for monitoring large areas. These networked systems could be deployed at job-sites for worker safety, throughout a community for pollution warnings, or dispersed in a battlefield for early warning of chemical or biological threats. Presented will be information on the first prototype of this type of system. Included will be field trial data, with this prototype performing air analysis autonomously from an aircraft.

Arkin, C. Richard↗

NASA NTRS1991

Status of NASA's IR wind shear detection research

The status of NASA's wind shear detection research is reported in viewgraph form. Information is given on early experience, FLIR detectors, quantities measured by Airborne Warning and Avoidance System 1 (AWAS 1), the time series model for Flight 551, conclusions from NASA 737 flights, conclusions on Orlando 7-7-90, and AWAS 3 mnemonics.

Mckissick, Burnell↗

NASA NTRS1988

Design of components for growing higher plants in space

The overall goal of this project is to design unique systems and components for growing higher plants in microgravity during long-term space missions (Mars and beyond). Specific design tasks were chosen to contribute to and supplement NASA's Controlled Ecological Life Support System (CELSS) project. Selected tasks were automated seeding of plants, plant health sensing, and food processing. Prototype systems for planting both germinated and nongerminated seeds were fabricated and tested. Water and air pressure differences and electrostatic fields were used to trap seeds for separation and transport for planting. An absorption spectrometer was developed to measure chlorophyll levels in plants as an early warning of plant health problems. In the area of food processing, a milling system was created using high-speed rotating blades which were aerodynamically configured to produce circulation and retractable to prevent leakage. The project produced significant results having substantial benefit to NASA. It also provided an outstanding learning experience for the students involved.

Source record↗

NASA NTRS2009

Spinoff 2009

Topics covered include: Image-Capture Devices Extend Medicine's Reach; Medical Devices Assess, Treat Balance Disorders; NASA Bioreactors Advance Disease Treatments; Robotics Algorithms Provide Nutritional Guidelines; "Anti-Gravity" Treadmills Speed Rehabilitation; Crew Management Processes Revitalize Patient Care; Hubble Systems Optimize Hospital Schedules; Web-based Programs Assess Cognitive Fitness; Electrolyte Concentrates Treat Dehydration; Tools Lighten Designs, Maintain Structural Integrity; Insulating Foams Save Money, Increase Safety; Polyimide Resins Resist Extreme Temperatures; Sensors Locate Radio Interference; Surface Operations Systems Improve Airport Efficiency; Nontoxic Resins Advance Aerospace Manufacturing; Sensors Provide Early Warning of Biological Threats; Robot Saves Soldier's Lives Overseas (MarcBot); Apollo-Era Life Raft Saves Hundreds of Sailors; Circuits Enhance Scientific Instruments and Safety Devices; Tough Textiles Protect Payloads and Public Safety Officers; Forecasting Tools Point to Fishing Hotspots; Air Purifiers Eliminate Pathogens, Preserve Food; Fabrics Protect Sensitive Skin from UV Rays; Phase Change Fabrics Control Temperature; Tiny Devices Project Sharp, Colorful Images; Star-Mapping Tools Enable Tracking of Endangered Animals; Nanofiber Filters Eliminate Contaminants; Modeling Innovations Advance Wind Energy Industry; Thermal Insulation Strips Conserve Energy; Satellite Respondent Buoys Identify Ocean Debris; Mobile Instruments Measure Atmospheric Pollutants; Cloud Imagers Offer New Details on Earth's Health; Antennas Lower Cost of Satellite Access; Feature Detection Systems Enhance Satellite Imagery; Chlorophyll Meters Aid Plant Nutrient Management; Telemetry Boards Interpret Rocket, Airplane Engine Data; Programs Automate Complex Operations Monitoring; Software Tools Streamline Project Management; Modeling Languages Refine Vehicle Design; Radio Relays Improve Wireless Products; Advanced Sensors Boost Optical Communication, Imaging; Tensile Fabrics Enhance Architecture Around the World; Robust Light Filters Support Powerful Imaging Devices; Thermoelectric Devices Cool, Power Electronics; Innovative Tools Advance Revolutionary Weld Technique; Methods Reduce Cost, Enhance Quality of Nanotubes; Gauging Systems Monitor Cryogenic Liquids; Voltage Sensors Monitor Harmful Static; and Compact Instruments Measure Heat Potential.

Source record↗