SEARCH · Search NASA

Results for “grid life”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6

All sources NASA NTRS DOE OSTI

NASA NTRS2018

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗

NASA NTRS1980

Process modifications for improved carbon fiber composites: Alleviation of the electrical hazards problem

Attempts to alleviate carbon-fiber-composite electrical hazards during airplane crash fires through fiber gasification are described. Thermogravimetric and differential scanning calorimetric experiments found several catalysts that caused fibers to combust when composites were exposed to test fires. Composites were tested in the 'Burn-Bang' apparatus and in high voltage electrical detection grid apparatus. In a standard three minute burn test modified composites released no fibers, while state-of-the-art composites released several hundred fiber fragments. Expected service life with and without catalytic modification was studied and electron microscopy and X-ray microanalysis furnished physical appearance and chemical composition data. An acrylic acid polymer fiber coating was developed that wet the carbon fiber surface uniformly with the catalyst, providing a marked contrast with the uneven coats obtained by solution-dipping.

Ramohalli, K.↗

NASA NTRS2019

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗

NASA NTRS2019

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗

NASA NTRS2019

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗

NASA NTRS1993

Operating Characteristics of a 15-cm dia. Ion Engine for Small Planetary Spacecraft

A 15-cm diameter, scaled-down version of the NASA light-weight 30-cm ion engine has been developed for potential application to very small planetary spacecraft. Integration of the 15-cm ion source into 4x15-cm segmented engine configuration results in a 30-cm equivalent engine which can be throttled over a 7-to-1 input power variation with a constant beam current in each of the four segments. Throttling the segmented engine by turning off individual segments can result in a significant decrease in the required service life (and qualification requirements) of the ion source components.

grids segmented ion engine↗

NASA NTRS2012

NASA's Evolutionary Xenon Thruster (NEXT) Long-Duration Test as of 736 kg of Propellant Throughput

The NASA s Evolutionary Xenon Thruster (NEXT) program is developing the next-generation solar-electric ion propulsion system with significant enhancements beyond the state-of-the-art NASA Solar Electric Propulsion Technology Application Readiness (NSTAR) ion propulsion system to provide future NASA science missions with enhanced mission capabilities. A Long-Duration Test (LDT) was initiated in June 2005 to validate the thruster service life modeling and to qualify the thruster propellant throughput capability. The thruster has set electric propulsion records for the longest operating duration, highest propellant throughput, and most total impulse demonstrated. At the time of this publication, the NEXT LDT has surpassed 42,100 h of operation, processed more than 736 kg of xenon propellant, and demonstrated greater than 28.1 MN s total impulse. Thruster performance has been steady with negligible degradation. The NEXT thruster design has mitigated several lifetime limiting mechanisms encountered in the NSTAR design, including the NSTAR first failure mode, thereby drastically improving thruster capabilities. Component erosion rates and the progression of the predicted life-limiting erosion mechanism for the thruster compare favorably to pretest predictions based upon semi-empirical ion thruster models used in the thruster service life assessment. Service life model validation has been accomplished by the NEXT LDT. Assuming full-power operation until test article failure, the models and extrapolated erosion data predict penetration of the accelerator grid grooves after more than 45,000 hours of operation while processing over 800 kg of xenon propellant. Thruster failure due to degradation of the accelerator grid structural integrity is expected after

Shastry, Rohit↗

NASA NTRS2012

NASA's Evolutionary Xenon Thruster (NEXT) Long-Duration Test as of 736 kg of Propellant Throughput

Shastry, Rohit↗

NASA NTRS1994

RoboLab and virtual environments

A useful adjunct to the manned space station would be a self-contained free-flying laboratory (RoboLab). This laboratory would have a robot operated under telepresence from the space station or ground. Long duration experiments aboard RoboLab could be performed by astronauts or scientists using telepresence to operate equipment and perform experiments. Operating the lab by telepresence would eliminate the need for life support such as food, water and air. The robot would be capable of motion in three dimensions, have binocular vision TV cameras, and two arms with manipulators to simulate hands. The robot would move along a two-dimensional grid and have a rotating, telescoping periscope section for extension in the third dimension. The remote operator would wear a virtual reality type headset to allow the superposition of computer displays over the real-time video of the lab. The operators would wear exoskeleton type arms to facilitate the movement of objects and equipment operation. The combination of video displays, motion, and the exoskeleton arms would provide a high degree of telepresence, especially for novice users such as scientists doing short-term experiments. The RoboLab could be resupplied and samples removed on other space shuttle flights. A self-contained RoboLab module would be designed to fit within the cargo bay of the space shuttle. Different modules could be designed for specific applications, i.e., crystal-growing, medicine, life sciences, chemistry, etc. This paper describes a RoboLab simulation using virtual reality (VR). VR provides an ideal simulation of telepresence before the actual robot and laboratory modules are constructed. The easy simulation of different telepresence designs will produce a highly optimum design before construction rather than the more expensive and time consuming hardware changes afterwards.

Giarratano, Joseph C.↗

NASA NTRS1993

(abstract) Evaluation and Development of Diamond Grids for Ion Optics

A research program is underway to establish the feasibility of fabricating 15 cm diameter ion engine optics from polycrystalline diamond film. Because of its high thermal conductivity, low CTE, and low sputter yield, diamond film has the potential of mitigating life limiting and performance degrading processes which have hindered flight qualification of ion thrusters to date. Results of experiments are reported.

ion engines diamond films ion optics fabrication f↗

NASA NTRS2010

Simulation of a Lunar Surface Base Power Distribution Network for the Constellation Lunar Surface Systems

The Lunar Surface Power Distribution Network Study team worked to define, breadboard, build and test an electrical power distribution system consistent with NASA's goal of providing electrical power to sustain life and power equipment used to explore the lunar surface. A testbed was set up to simulate the connection of different power sources and loads together to form a mini-grid and gain an understanding of how the power systems would interact. Within the power distribution scheme, each power source contributes to the grid in an independent manner without communication among the power sources and without a master-slave scenario. The grid consisted of four separate power sources and the accompanying power conditioning equipment. Overall system design and testing was performed. The tests were performed to observe the output and interaction of the different power sources as some sources are added and others are removed from the grid connection. The loads on the system were also varied from no load to maximum load to observe the power source interactions.

Mintz, Toby↗

NASA NTRS2002

13-kV Ion-Extraction System Being Developed for Inert Gas Ion Engines

A high-voltage ion optics design was chosen for an assumed outer planet or interstellar precursor mission that would require a long-life, high-power, high-specific-impulse krypton ion engine. Such an engine could support energetic space missions to the outer planets or beyond. Detailed performance and lifetime analyses and several inexpensive subscale grid tests were conducted at the NASA Glenn Research Center and at the Colorado State University under a NASA Glenn grant. A subscale grid set of the selected geometry shown was tested at voltages up to 13,000 V. This yielded a krypton ion beam current that would, when scaled to a full-size 50-cm diameter, produce an ion beam with a power of 30 kW at a specific impulse over 14,000 sec. The operational ion beam focusing limits, as a function of ion current per hole, were found to impose requirements of high uniformity on the discharge chamber plasma density. A full-size set of two-grid, 50-cm-diameter titanium ion optics has been fabricated and awaits testing.

Rawlin, Vincent K.↗

NASA NTRS2018

An Introduction to Global Navigation Satellite Systems

Products that rely on Global Navigation Satellite Systems (GNSS) have become an essential part of daily life for millions of people around the world. In addition to enabling navigation, these constellations of satellites and the signals they transmit provide a global, precise timing source, used in everything from electrical power grid phasing to synchronization of financial networks. This tutorial introduces the concept of radio navigation, describes the features of GNSS signals that make navigation possible, and explains how these signals are processed by GNSS receivers. The resulting measurements and error sources, such as atmospheric effects and multipath, are discussed. Then methods are presented for combining these measurements into a position, velocity, and time estimate. The tutorial concludes with a brief status report on the GNSS constellations, space applications and recent flight experiences, and active areas of research.

Ashman, Ben↗

NASA NTRS2019

Use of Global Navigation Satellite Systems in Space

Products that rely on Global Navigation Satellite Systems (GNSS) have become an essential part of daily life for millions of people around the world. In addition to enabling navigation, these constellations of satellites and the signals they transmit provide a global, precise timing source, used in everything from electrical power grid phasing to synchronization of financial networks. This colloquium introduces the concept of radio navigation, describes the features of GNSS signals that make navigation possible, and explains how these signals are processed by GNSS receivers. The resulting measurements and error sources, such as atmospheric effects and multipath, are discussed. Special consideration is given to the challenges of using GNSS in space, and the innovations that make it possible. A survey of space applications and recent flight experiences is provided. Active areas of research are discussed, including the use of GNSS for missions to the Moon.

Space service volume↗

NASA NTRS2019

Global Navigation Satellite Systems and How They Work

Products that rely on Global Navigation Satellite Systems (GNSS) have become an essential part of daily life for millions of people around the world. In addition to enabling navigation, these constellations of satellites and the signals they transmit provide a global, precise timing source, used in everything from electrical power grid phasing to synchronization of financial networks. This colloquium introduces the concept of radio navigation, describes the features of GNSS signals that make navigation possible, and explains how these signals are processed by GNSS receivers. The resulting measurements and error sources, such as atmospheric effects and multipath, are discussed. Special consideration is given to the challenges of using GNSS in space, and the innovations that make it possible. A survey of space applications and recent flight experiences is provided. Active areas of research are discussed, including the use of GNSS for missions to the Moon.

Ashman, Ben↗

NASA NTRS2020

An Introduction to Global Navigation Satellite Systems

Products that rely on Global Navigation Satellite Systems (GNSS) have become an essential part of daily life for millions of people around the world. In addition to enabling navigation, these constellations of satellites and the signals they transmit provide a global, precise timing source, used in everything from electrical power grid phasing to synchronization of financial networks. This lecture introduces the concept of radio navigation, describes the features of GNSS signals that make navigation possible, and explains how these signals are processed by GNSS receivers. The resulting measurements and error sources, such as atmospheric effects and multipath, are discussed. Special consideration is given to the challenges of using GNSS in space, and the innovations that make it possible. A survey of space applications and recent flight experiences is provided. Active areas of research are discussed, including the use of GNSS for missions to the Moon.

Ashman, Ben↗

NASA NTRS1993

Pressurized modules for Space Station Freedom

Crew workstations, storage, and facilities in the SSF Habitation module along with station housekeeping and supporting subsystems (power, thermal, and life support) are considered. The U.S. Laboratory and Habitation modules are based on a common structural design. The pressurized logistics modules (PLMs) have maximum commonality with the other pressurized modules including common ring frames and waffle grid skin; multilayer insulation and debris shield; and one common endcone.

Hopson, George D.↗

NASA NTRS2008

Low Cost Electric Propulsion Thruster for Deep Space Robotic Science Missions

Electric Propulsion (EP) has found widespread acceptance by commercial satellite providers for on-orbit station keeping due to the total life cycle cost advantages these systems offer. NASA has also sought to benefit from the use of EP for primary propulsion onboard the Deep Space-1 and DAWN spacecraft. These applications utilized EP systems based on gridded ion thrusters, which offer performance unequaled by other electric propulsion thrusters. Through the In-Space Propulsion Project, a lower cost thruster technology is currently under development designed to make electric propulsion intended for primary propulsion applications cost competitive with chemical propulsion systems. The basis for this new technology is a very reliable electric propulsion thruster called the Hall thruster. Hall thrusters, which have been flown by the Russians dating back to the 1970s, have been used by the Europeans on the SMART-1 lunar orbiter and currently employed by 15 other geostationary spacecraft. Since the inception of the Hall thruster, over 100 of these devices have been used with no known failures. This paper describes the latest accomplishments of a development task that seeks to improve Hall thruster technology by increasing its specific impulse, throttle-ability, and lifetime to make this type of electric propulsion thruster applicable to NASA deep space science missions. In addition to discussing recent progress on this task, this paper describes the performance and cost benefits projected to result from the use of advanced Hall thrusters for deep space science missions.

Manzella, David↗