Results for “information fusion” — Page 11

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRSDate not supplied

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Nathan R. Pavlovic↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS1999

Dynamics of the Molten Contact Line

The purpose of this program is to develop a basic understanding of how a molten material front spreads over a solid that is below its melting point, arrests, and freezes. Our hope is that the work will contribute toward a scientific knowledge base for certain new applications involving molten droplet deposition, including the "printing" of arbitrary three-dimensional objects by precise deposition of individual molten microdrops that solidify after impact. Little information is available at this time on the capillarity-driven motion and arrest of molten contact line regions. Schiaffino and Sonin investigated the arrest of the contact line of a molten microcrystalline wax spreading over a subcooled solid "target" of the same material. They found that contact line arrest takes place at an apparent liquid contact angle that depends primarily on the Stefan number S=c(T(sub f) -T(sub t)/L based on the temperature difference between the fusion point and the target temperature, and proposed that contact line arrest occurs when the liquid's dynamic contact angle approaches the angle of attack of the solidification front just behind the contact line. They also showed, however, that the conventional continuum equations and boundary conditions have no meaningful solution for this angle. The solidification front angle is determined by the heat flux just behind the contact line, and the heat flux is singular at that point. By comparing experiments with numerical computations, Schiaffino and Sonin estimated that the conventional solidification model must break down within a distance of order 0.1 - 1 microns of the contact line. The physical mechanism for this breakdown is as yet undetermined, and no first-principles theory exists for the contact angle at arrest. Schiaffino and Sonin also presented a framework for understanding how to moderate Weber number molten droplet deposition in terms of similarity laws and experimentation. The study is based on experiments with three molten materials- molten wax on solid wax, water on ice, and mercury on frozen mercury- which between them span a considerable range of the deposition/solidification similarity parameters. Correlations are obtained for the spreading velocity, spreading time scales, the spreading factor (i.e. ratio of deposited drop's final footprint radius and the drop's initial radius), post-spreading liquid oscillation amplitudes and time scales, and bulk solidification time scales. Duthaler carried out an experimental and theoretical investigation of the relationship between the liquid's apparent contact angle and the Capillary number Ca=mu U/sigma based on contact line speed, for molten materials spreading over subcooled solids. This relationship is required for modeling of melt spreading. We have adapted Voinov's methodology to the molten contact line and formulated a theoretical model for the Ca vs. contact angle relationship, based Schiaffino and Sonin#s (1997a,b) wedge-like solidification front model. With the solidification front angle taken from Schiaffino and Sonin, the model is in good agreement with the experimental results for Ca vs. contact angle. Duthaler also extended the experimental investigation of droplet deposition and contact line freezing to more materials, including solder on glass, solder on solder, water on ice, and molten microcrystalline wax on wax. The latter also included tests on inclined targets. Deposition tests have also been done with molten octacosane (C28H58) on various targets. An important objective of our program has been the development of micron-scale sensors for measuring the transient temperature at a point on the substrate surface as a molten contact line moves over it. The expectation is that this temperature history will yield a better understanding of the thermal process in the contact line region. The sensors are of the thermistor type, either 2.5 microns or 1.5 microns square, microfabricated with silicon-based technology on either pure silicon or amorphous silicon dioxide chips. Each chip has 32 sensors distributed on its surface in arrays. The time response is better than 10 ms. At the time of writing, sensor calibration is in progress. Results on thermal transients during contact line passage will be discussed at the conference. While we expect that the data will provide information on the near-contact-line heat transfer process, we also foresee possible problems. First, the spatial resolution of the sensors may be insufficient to resolve the near-contact-line region. Second, the sensors protrude about 0.5 microns above the substrate surface, and may affect the contact line motion. Third, a sensor's temperature history depends on both the heat flux distribution into it from the fusion front and the thermal properties of the substrate below it and the solidified melt between it and the fusion front. The heat flux distribution in the contact line region must therefore be unfolded from computations of the overall system's transient thermal response.

Sonin, Ain A.↗

NASA NTRS2009

Ocean, Land and Meteorology Studies Using Space-Based Lidar Measurements

CALIPSO's main mission objective is studying the climate impact of clouds and aerosols in the atmosphere. CALIPSO also collects information about other components of the Earth's ecosystem, such as oceans and land. This paper introduces the physics concepts and presents preliminary results for the valueadded CALIPSO Earth system science products. These include ocean surface wind speeds, column atmospheric optical depths, ocean subsurface backscatter, land surface elevations, atmospheric temperature profiles, and A-train data fusion products.

Hu,Yongxiang↗

NASA NTRS2011

Handling Heavenly Jewels - 35 Years of Antarctic Meteorite Processing at Johnson Space Center

The ANSMET program began in 1976, and since that time more than 18,000 meteorites have been processed in the Meteorite Processing Lab at Johnson Space Center in Houston, TX[1]. The meteorites are collected and returned to JSC on a freezer truck and remain frozen until they are initially processed. Initial Processing of Meteorites: Initial processing involves drying the meteorites in a nitrogen glove box for 24 to 48 hours, photographing, measuring, weighing and writing a description of the interior and exterior. The meteorite is broken and a representative sample is sent to the Smithsonian Institution for classification. Newsletter & Requests: Once initial processing has been complete and the meteorites have been classified, the information is published in the Antarctic Meteorite Newsletter[2,3]. The newsletter is published twice yearly and is sent electronically to researchers around the world and is also available on line. Researchers are asked to fill out a request form and submit it to the Meteorite Working Group secretary. All sample requests will be reviewed by either the meteorite curator or the Meteorite Working Group de-pending on the type of meteorite and the research being conducted. Processing for Sample Requests: In the meteorite processing lab, meteorite samples are prepared several different ways. Most samples are prepared as chips obtained by use of stainless steel chisels in a chipping bowl or rock splitter. In special situations where a researcher needs a slab the meteorite samples can be bandsawed in a dry nitrogen glove box with a diamond blade, no liquids are ever introduced into the cabinet. The last type of sample preparation is thin/thick sections. The meteorite thin section lab at JSC can prepare standard 30-micron thin sections, thick sections of variable thickness (100 to 200 microns), or demountable sections using superglue. Information for researchers: It is important that re-searchers fill the sample request form completely, in order to make sure the meteorite is processed correctly[4]. Re-searchers should list any special requirements on the form, i.e. packaging of samples (poly vs. stainless), thick sections and thickness needed, superglue needed, interior chips, exterior chips, fusion crust, contamination issues, all concerns should be listed so processing can be done accurately and any concerns the researcher has can be addressed be-fore the meteorites are broken.

Satterwhite, C. E.↗

NASA NTRS2015

The Role of the NASA Global Hawk Link Module as an Information Nexus For Atmospheric Mapping Missions

The Link Module described in this paper was developed for the NASA Uninhabited Aerial System (UAS) Global Hawk Pacific Mission (GloPAC) Airborne Science Campaign; four flights of 30 hour duration, supporting the Aura Validation Experiment (AVE). It was used again during the Genesis and Rapid Intensification Processes (GRIP) experiment, a NASA Earth Science field experiment to better understand how tropical storms form and develop into major hurricanes. In these missions, the Link Module negotiated all communication over the high bandwidth Ku satellite link, archived all the science data from onboard experiments in a spatially enabled database, routed command and control of the instruments from the Global Hawk Operations Center, and re-transmitted select data sets directly to experimenters control and analysis systems. The availability of aggregated information from collections of sensors, and remote control capabilities, in real-time, is revolutionizing the way Airborne Science is being conducted. The Link Module NG now being flown in support of the NASA Earth Venture missions, the Hurricane and Severe Storm Sentinel (HS3) mission, and Airborne Tropical Tropopause Experiment (A TTREX) mission, has advanced data fusion technologies that are further advancing the Scientific productivity, flexibility and robustness of these systems. On-the-fly traffic shaping has been developed to allow the high definition video, used for critical flight control segments, to dynamically allocate variable bandwidth on demand. Historically, the Link Module evolved from the instrument and communication interface controller used by NASA's Pathfinder and Pathfinder plus solar powered UAS's in the late 1990' s. It later was expanded for use in the AIRDAS four channel scanner flown on the NASA Altus UAS, and then again to a module in the AMS twelve channel multispectral scanner flying on the NASA (Predator-b) Ikhana UAS. The current system is the answer to the challenges imposed by extremely long duration UASs, with on-board multi-instrument (>= 12) Sensor Webs.

Hurricanes↗

NASA NTRS2018

The Land Surface Data Toolkit (LDT v7.2) - A Data Fusion Environment for Land Data Assimilation Systems

The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.

droughts and floods↗

NASA NTRSDate not supplied

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗

NASA NTRSDate not supplied

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗

NASA NTRSDate not supplied

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗

NASA NTRS2001

Multi-Sensor Registration of Earth Remotely Sensed Imagery

Assuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).

LeMoigne, Jacqueline↗

NASA NTRS2011

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

Kumar, Sujay V.↗

NASA NTRS2012

An Information NEXUS: The NASA Global Hawk Link Module

The Link Module described in this paper was first developed for the NASA Global Hawk Pacific Mission (GloPAC), four flights of 30 hour duration, supporting the Aura Validation Experiment (AVE). Its second use was during the Genesis and Rapid Intensification Processes (GRIP) experiment, a NASA Earth Science field experiment to better understand how tropical storms form and develop into major hurricanes. In these missions, the Link module negotiated all communication over the high bandwidth Ku satellite link, archived al the science data from onboard experiments in a spatially enable database, routed command and control of the instruments from the Global Hawk Operations Center, and retransmitted select data sets directly to experimenters control and analysis systems. The availability of aggregated information from collections of sensors, and remote control capabilities, in real-time, is revolutionizing the way Airborne Science is being conducted. Also described is the next generation Link Module now being designed and tested to support the NASA Earth Venture missions, the Hurricane and Severe Storm Sentinel (HS3) mission, and Airborne Tropical Tropopause Experiment (ATTREX) mission. Advanced data fusion technologies being developed will further advance the Scientific productivity, flexibility and robustness of these systems. Historically, the Link module evolved from the instrument and communication interface controller used by NASA's Pathfinder and Pathfinder plus solar powered UAS's in the late 1990's. It later was expanded for use in the AIRDAS four channel scanner flown on the NASA Altus UAS, and then again to a module in the AMS twelve channel multispectral scanner flying on the NASA (Predator-b) Ikhana UAS. The current system is the next step in the evolution, a multi board system packaged in a Curtiss Wright MIL-spec, flight qualified enclosure.

Remote Sensing↗

NASA NTRSDate not supplied

Global-to-local air quality forecasts using the NASA GEOS Composition Forecast System

Since 2019, the NASA Global Earth Observing System (GEOS) model has been used to generate global, near-real-time estimates and daily five-day forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (~25 km) from the surface up to the lower mesosphere. This composition forecast system (“GEOS-CF”) combines the GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to deliver detailed analysis of a wide range of air pollutants, including the policy-relevant species such as ozone, carbon monoxide, nitrogen oxides, sulfur dioxide and fine particulate matter (PM2.5). Because GEOS-CF includes atmospheric levels up through the stratosphere, this system has been leveraged to support the Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite mission and provide stratospheric intrusion alerts to ground-based monitoring stations. We will present recent advances to GEOS-CF which target increased computational efficiency and accuracy. These include the incorporation of simplified chemistry mechanisms to accelerate model forecasts, use of model-observation data fusion techniques to provide highly localized forecasts, and assimilation of satellite observations to produce more accurate model analyses. We further discuss our attempts to make these tools publicly available on platforms outside the NASA domain, such as Google Earth Engine and Amazon Web Services with the goal to facilitate the integration of state-of-the-science air quality information onto platforms used by stakeholders, air quality managers, and the public.

Emma Knowland↗

NASA NTRS2020

Ignition and Burn in a Hybrid Nuclear Fuel for a Pulsed Rocket Engine

This work explores the parameter space of a cylindrical hybrid nuclear fuel in support of a z-pinch driven pulsed fission-fusion (PuFF) engine. 0D power balance and 1D burn wave calculations have been performed to explore the parameter space of hybrid cylindrical nuclear fuels. The boundary of minimal initial conditions needed to reach breakeven are found within the context of the model. The effects of initial conditions upon the yield at the end of burn wave expansion are also determined. The model is used to examine the minimum initial energies predicted to result in yields of a few MJ. The goal of this work is to guide fuel design, inform future models and experiments as well as look for the most efficient parameter space for ignition in a z-pinch driven hybrid nuclear reaction. The impact of initial parameters upon ignition, burn, and gain are discussed. It is found that a hybrid cylindrical target may breakeven with initial energy near the axis of approximately 4 to 6 MJ in lithium deuteride/uranium 235 fuel. It is also found that magnetic field can lower the threshold further of which the magnitude changes across the parameter space. The dual fusion/fission reactions are found to boost each other leading to lower initial driving energies needed to reach breakeven in the hybrid cylindrical nuclear fuel.

fission↗

NASA NTRS2001

Plasma Accelerator Development for Dynamic Formation of Plasma Liners: A Status Report

An experimental plasma accelerator for magnetic target fusion (MTF) applications under development at the NASA Marshall Space Flight Center is described. The accelerator is a pulsed plasma thruster and has been tested experimentally and plasma jet velocities of approximately 50 km/sec have been obtained. The plasma jet structure has been photographed with 10 ns exposure times to reveal a stable and repeatable plasma structure. Data for velocity profile information has been obtained using light pipes embedded in the gun walls to record the plasma transit at various barrel locations. Preliminary spatially resolved spectral data and magnetic field probe data are also presented. A high speed triggering system has been developed and tested as a means of reducing the gun "jitter". This jitter is being characterized and future work for second generation "ultra-low jitter" gun development is being identified.

Thio, Y. C. Francis↗

NASA NTRS2005

Earth Observing System (EOS) Snow and Ice Products for Observation and Modeling

Snow and ice are the key components of the Earth's cryosphere, and their influence on the Earth's energy balance is very significant due at least in part to the large areal extent and high albedo characterizing these features. Large changes in the cryosphere have been measured over the last century and especially over the past decade, and remote sensing plays a pivotal role in documenting these changes. Many of NASA's Earth Observing System (EOS) products derived from instruments on the Terra, Aqua, and Ice, Cloud and land Elevation Satellite (ICESat) satellites are useful for measuring changes in features that are associated with climate change. The utility of the products is continually enhanced as the length of the time series increases. To gain a more coherent view of the cryosphere and its historical and recent changes, the EOS products may be employed together, in conjunction with other sources of data, and in models. To further this goal, the first EOS Snow and Ice Products Workshop was convened. The specific goals of the workshop were to provide current and prospective users of EOS snow and ice products up-to-date information on the products, their validation status and future enhancements, to help users utilize the data products through hands-on demonstrations, and to facilitate the integration of EOS products into models. Oral and poster sessions representing a wide variety of snow and ice topics were held; three panels were also convened to discuss workshop themes. Panel discussions focused on data fusion and assimilation of the products into models. Approximately 110 people attended, representing a wide array of interests and organizations in the cryospheric community.

Hall, D.↗