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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fUlly operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight models is expected to be underway in the latter part of 2007. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 ground processing algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area)

Goodman, Steven J.↗
NASA NTRS2008

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous,full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornado activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight units is expected to begin in latter part of the year. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2B algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data provided to selected National Weather Service forecast offices in Southern and Eastern Region are also improving our understanding of the application of these data in the severe storm warning process and help to accelerate the development of the pre-launch algorithms and Nowcasting applications.

Goodman, Steven↗
NASA NTRS2008

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational.The mission objectives for the GLM are to 1) provide continuous,full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight units is expected to begin in latter part of the year. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2B algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) sate]lite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data provided to selected National Weather Service forecast offices in Southern and Eastern Region are also improving our understanding of the application of these data in the severe storm warning process and help to accelerate the development of the pre-launch algorithms and Nowcasting applications. Abstract for the 3 rd Conference on Meteorological

Goodman, Steven↗
NASA NTRS2005

Assessing Malaria Risks in Greater Mekong Subregion based on Environmental Parameters

At 4,200 km, the Mekong River is the tenth longest river in the world. It directly and indirectly influences the lives of hundreds of millions of inhabitants in its basin. The riparian countries - Thailand, Myanmar, Cambodia, Laos, Vietnam, and a small part of China - form the Greater Mekong Subregion (GMS). This geographical region has the misfortune of being the world's epicenter of falciparum malaria, which is the most severe form of malaria caused by Plasmodium falciparum. Depending on the country, approximately 50 to 90% of all malaria cases are due to this species. In the Malaria Modeling and Surveillance Project, we have been developing techniques to enhance public health s decision capability for malaria risk assessments and controls. The main objectives are: 1) identifying the potential breeding sites for major vector species; 2) implementing a malaria transmission model to identify the key factors that sustain or intensify malaria transmission; and 3) implementing a risk algorithm to predict the occurrence of malaria and its transmission intensity. The potential benefits are: 1) increased warning time for public health organizations to respond to malaria outbreaks; 2) optimized utilization of pesticide and chemoprophylaxis; 3) reduced likelihood of pesticide and drug resistance; and 4) reduced damage to environment. Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. The NASA Earth science data sets that have been used for malaria surveillance and risk assessment include AVHRR Pathfinder, TRMM, MODIS, NSIPP, and SIESIP. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Socioeconomic factors that may influence malaria transmissions will also be incorporated into the predictive models.

Kiang, Richard↗
NASA NTRS2007

Surveillance and Control of Malaria Transmission in Thailand using Remotely Sensed Meteorological and Environmental Parameters

These slides address the use of remote sensing in a public health application. Specifically, this discussion focuses on the of remote sensing to detect larval habitats to predict current and future endemicity and identify key factors that sustain or promote transmission of malaria in a targeted geographic area (Thailand). In the Malaria Modeling and Surveillance Project, which is part of the NASA Applied Sciences Public Health Applications Program, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) identification of the potential breeding sites for major vector species; 2) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity; 3) implementation of a dynamic transmission model to identify the key factors that sustain or intensify malaria transmission. The potential benefits are: 1) increased warning time for public health organizations to respond to malaria outbreaks; 2) optimized utilization of pesticide and chemoprophylaxis; 3) reduced likelihood of pesticide and drug resistance; and 4) reduced damage to environment. !> Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. The NASA Earth science data sets that have been used for malaria surveillance and risk assessment include AVHRR Pathfinder, TRMM, MODIS, NSIPP, and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of the remotely sensed parameters. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Discrete event simulations are used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to the Greater Mekong Subregion. Our models have been applied to malaria in Indonesia, Korea, and other regions in the world with similar success.

Kiang, Richard K.↗
NASA NTRS2004

Improving the Transition of Earth Satellite Observations from Research to Operations

There are significant gaps between the observations, models, and decision support tools that make use of new data. These challenges include: 1) Decreasing the time to incorporate new satellite data into operational forecast assimilation systems, 2) Blending in-situ and satellite observing systems to produce the most accurate and comprehensive data products and assessments, 3) Accelerating the transition from research to applications through national test beds, field campaigns, and pilot demonstrations, and 4) Developing the partnerships and organizational structures to effectively transition new technology into operations. At the Short-term Prediction Research and Transition (SPORT) Center in Huntsville, Alabama, a NASA-NOAA-University collaboration has been developed to accelerate the infusion of NASA Earth science observations, data assimilation and modeling research into NWS forecast operations and decision-making. The SPoRT Center research focus is to improve forecasts through new observation capability and the regional prediction objectives of the US Weather Research Program dealing with 0-1 day forecast issues such as convective initiation and 24-hr quantitative precipitation forecasting. The near real-time availability of high-resolution experimental products of the atmosphere, land, and ocean from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Infrared Spectroradiometer (AIRS), and lightning mapping systems provide an opportunity for science and algorithm risk reduction, and for application assessment prior to planned observations from the next generation of operational low Earth orbiting and geostationary Earth orbiting satellites. This paper describes the process for the transition of experimental products into forecast operations, current products undergoing assessment by forecasters, and plans for the future. The SPoRT Web page is at (http://www.ghcc.msfc.nasa.gov/sport).

Goodman, Steven J.↗
NASA NTRS2005

Malaria Modeling and Surveillance for the Greater Mekong Subregion

At 4,200 km, the Mekong River is the tenth longest river in the world. It directly and indirectly influences the lives of hundreds of millions of inhabitants in its basin. The riparian countries - Thailand, Myanmar, Cambodia, Laos, Vietnam, and a small part of China - form the Greater Mekong Subregion (GMS). This geographical region has the misfortune of being the world's epicenter of falciparum malaria, which is the most severe form of malaria caused by Plasmodium falciparum. Depending on the country, approximately 50 to 90% of all malaria cases are due to this species. In the Malaria Modeling and Surveillance Project, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) Identifying the potential breeding sites for major vector species; 2) Implementing a malaria transmission model to identify the key factors that sustain or intensify malaria transmission; and 3) Implementing a risk algorithm to predict the occurrence of malaria and its transmission intensity. The potential benefits are: 1) Increased warning time for public health organizations to respond to malaria outbreaks; 2) Optimized utilization of pesticide and chemoprophylaxis; 3) Reduced likelihood of pesticide and drug resistance; and 4) Reduced damage to environment. Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. These parameters are extracted from NASA Earth science data sets. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records.

Kiang, Richard↗
NASA NTRS2007

Modeling Malaria Transmission in Thailand and Indonesia

Malaria Modeling and Surveillance is a project in the NASA Applied Sciences Public Health Applications Program. The main objectives of this project are: 1) identification of the potential breeding sites for major vector species: 2) implementation of a malaria transmission model to identify they key factors that sustain or intensify malaria transmission; and 3) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity. Remote sensing and GIs are the essential elements of this project. The NASA Earth science data sets used in this project include AVHRR Pathfinder, TRMM, MODIS, NSIPP and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of precipitation, temperatures, humidity and vegetation. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Examples for spatio-temporal modeling of malaria transmissions in Southeast Asia are given. Discrete event simulations were used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to Southeast Asia. The model and techniques are equally applicable to other regions of the world, when appropriate epidemiological and vector ecological parameters are used as input.

Kiang, Richard↗
NASA NTRS1995

Fractal mechanisms and heart rate dynamics. Long-range correlations and their breakdown with disease

Under healthy conditions, the normal cardiac (sinus) interbeat interval fluctuates in a complex manner. Quantitative analysis using techniques adapted from statistical physics reveals the presence of long-range power-law correlations extending over thousands of heartbeats. This scale-invariant (fractal) behavior suggests that the regulatory system generating these fluctuations is operating far from equilibrium. In contrast, it is found that for subjects at high risk of sudden death (e.g., congestive heart failure patients), these long-range correlations break down. Application of fractal scaling analysis and related techniques provides new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as motivating development of novel physiologic models of systems that appear to be heterodynamic rather than homeostatic.

NASA Discipline Cardiopulmonary↗
NASA NTRS2009

A Science Data System Approach for the SMAP Mission

Though Science Data System (SDS) development has not traditionally been part of the mission concept phase, lessons learned and study of past Earth science missions indicate that SDS functionality can greatly benefit algorithm developers in all mission phases. We have proposed a SDS approach for the SMAP Mission that incorporates early support for an algorithm testbed, allowing scientists to develop codes and seamlessly integrate them into the operational SDS. This approach will greatly reduce both the costs and risks involved in algorithm transitioning and SDS development.

PCS↗
NASA NTRS2008

Risk-Assessment for Equipment Operating on the Lunar Surface

Particle-size distribution of lunar dust simulant is evaluated using scanning electron spectroscopy in order to consider approaches to evaluating risk to individual mechanical components operating on the lunar surface. Assessing component risk and risk-mitigation during actual operations will require noninvasive continuous data gathering on numerous parameters. Those data sets would best be evaluated using data-mining algorithms to assess risk, and recovery from risk, of individual mechanical components in real-time.

Richmond, R. C.↗
NASA NTRS2000

CNES-NASA Studies of the Mars Sample Return Orbiter Aerocapture Phase

A Mars Sample Return (MSR) mission has been proposed as a joint CNES (Centre National d'Etudes Spatiales) and NASA effort in the ongoing Mars Exploration Program. The MSR mission is designed to return the first samples of Martian soil to Earth. The primary elements of the mission are a lander, rover, ascent vehicle, orbiter, and an Earth entry vehicle. The Orbiter has been allocated only 2700 kg on the launch phase to perform its part of the mission. This mass restriction has led to the decision to use an aerocapture maneuver at Mars for the orbiter. Aerocapture replaces the initial propulsive capture maneuver with a single atmospheric pass. This atmospheric pass will result in the proper apoapsis, but a periapsis raise maneuver is required at the first apoapsis. The use of aerocapture reduces the total mass requirement by approx. 45% for the same payload. This mission will be the first to use the aerocapture technique. Because the spacecraft is flying through the atmosphere, guidance algorithms must be developed that will autonomously provide the proper commands to reach the desired orbit while not violating any of the design parameters (e.g. maximum deceleration, maximum heating rate, etc.). The guidance algorithm must be robust enough to account for uncertainties in delivery states, atmospheric conditions, mass properties, control system performance, and aerodynamics. To study this very critical phase of the mission, a joint CNES-NASA technical working group has been formed. This group is composed of atmospheric trajectory specialists from CNES, NASA Langley Research Center and NASA Johnson Space Center. This working group is tasked with developing and testing guidance algorithms, as well as cross-validating CNES and NASA flight simulators for the Mars atmospheric entry phase of this mission. The final result will be a recommendation to CNES on the algorithm to use, and an evaluation of the flight risks associated with the algorithm. This paper will describe the aerocapture phase of the MSR mission, the main principles of the guidance algorithms that are under development, the atmospheric entry simulators developed for the evaluations, the process for the evaluations, and preliminary results from the evaluations.

Fraysse, H.↗
NASA NTRS2008

Processing LiDAR Data to Predict Natural Hazards

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Fairweather, Ian↗
NASA NTRS2011

Description and Status of the DC Lightning Mapping Array

The DC Lightning Mapping Array (DC LMA) centered on the Washington, DC metro region has been in operation since 2006. During that time the DC LMA has provided real time data to regional National Weather Service (NSF) Sterling, VA forecast office for operations support and the NOAA Meteorological Development Laboratory (MDL) for new product development and assessment. Data from this network (as well as other from other LMA systems) are now being used to create proxy Geostationary Lightning Mapper (GLM) data sets for GOES-R risk reduction and algorithm development activities. In addition, since spring 2009 data are provided to the Storm Prediction Center in support of Hazardous Weather Testbed and GOES-R Proving Ground activities during the Spring Program. Description, status and plans will be discussed.

Blakeslee, Richard↗
NASA NTRS2011

Description and Status of the North Alabama Lightning Mapping Array

The North Alabama Lightning Mapping Array (LMA) is a network LMA detectors that detects and maps lightning using VHF radiation (TV Channel 5) in a region centered about Huntsville, Alabama that includes North Alabama, Central Tennessee and parts of Georgia and Mississippi. The North Alabama LMA has been in operation since late 2001, and has been providing real time data to regional National Weather Service (NSF) Weather Forecast Offices (WFOs) since mid 2003 through the NASA Short-term Prediction Research and Transition (SPoRT) center. Data from this network (as well as other from other LMA systems) are now being used to create proxy Geostationary Lightning Mapper (GLM) data sets for GOES-R risk reduction and algorithm development activities. In addition, since spring 2009 data are provided to the Storm Prediction Center in support of Hazardous Weather Testbed and GOES-R Proving Ground activities during the Spring Program. Description, status and plans will be discussed.

Blakeslee, Richard J.↗
NASA NTRS2011

Implementation of Enhanced Propulsion Control Modes for Emergency Flight Operation

Aircraft engines can be effective actuators to help pilots avert or recover from emergency situations. Emergency control modes are being developed to enhance the engines performance to increase the probability of recovery under these circumstances. This paper discusses a proposed implementation of an architecture that requests emergency propulsion control modes, allowing the engines to deliver additional performance in emergency situations while still ensuring a specified safety level. In order to determine the appropriate level of engine performance enhancement, information regarding the current emergency scenario (including severity) and current engine health must be known. This enables the engine to operate beyond its nominal range while minimizing overall risk to the aircraft. In this architecture, the flight controller is responsible for determining the severity of the event and the level of engine risk that is acceptable, while the engine controller is responsible for delivering the desired performance within the specified risk range. A control mode selector specifies an appropriate situation-specific enhanced mode, which the engine controller then implements. The enhanced control modes described in this paper provide additional engine thrust or response capabilities through the modification of gains, limits, and the control algorithm, but increase the risk of engine failure. The modifications made to the engine controller to enable the use of the enhanced control modes are described, as are the interaction between the various subsystems and importantly, the interaction between the flight controller/pilot and the propulsion control system. Simulation results demonstrate how the system responds to requests for enhanced operation and the corresponding increase in performance.

Csank, Jeffrey T.↗
NASA NTRS2009

A New View of Radiation-Induced Cancer: Integrating Short- and Long-Term Processes. Part II: Second Cancer Risk Estimation

As the number of cancer survivors grows, prediction of radiotherapy-induced second cancer risks becomes increasingly important. Because the latency period for solid tumors is long, the risks of recently introduced radiotherapy protocols are not yet directly measurable. In the accompanying article, we presented a new biologically based mathematical model, which, in principle, can estimate second cancer risks for any protocol. The novelty of the model is that it integrates, into a single formalism, mechanistic analyses of pre-malignant cell dynamics on two different time scales: short-term during radiotherapy and recovery; long-term during the entire life span. Here, we apply the model to nine solid cancer types (stomach, lung, colon, rectal, pancreatic, bladder, breast, central nervous system, and thyroid) using data on radiotherapy-induced second malignancies, on Japanese atomic bomb survivors, and on background US cancer incidence. Potentially, the model can be incorporated into radiotherapy treatment planning algorithms, adding second cancer risk as an optimization criterion.

Shuryak, Igor↗
NASA NTRS2008

GOES-R Geostationary Lightning Mapper Performance Specifications and Algorithms

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series will carry a GLM that will provide continuous day and night observations of lightning. The mission objectives for the GLM are to: (1) Provide continuous, full-disk lightning measurements for storm warning and nowcasting, (2) Provide early warning of tornadic activity, and (2) Accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997- present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. The science data will consist of lightning "events", "groups", and "flashes". The algorithm is being designed to be an efficient user of the computational resources. This may include parallelization of the code and the concept of sub-dividing the GLM FOV into regions to be processed in parallel. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama, Oklahoma, Central Florida, and the Washington DC Metropolitan area) are being used to develop the prelaunch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution.

Mach, Douglas M.↗