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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Aerosol-Radiation-Cloud Interactions in the South-East Atlantic: Future Suborbital Activities to Address Knowledge Gaps in Satellite and Model Assessments

Southern Africa produces almost a third of the Earth's biomass burning (BB) aerosol particles. Particles lofted into the mid-troposphere are transported westward over the South-East (SE) Atlantic, home to one of the three permanent subtropical stratocumulus (Sc) cloud decks in the world. The SE Atlantic stratocumulus deck interacts with the dense layers of BB aerosols that initially overlay the cloud deck, but later subside and may mix into the clouds. These interactions include adjustments to aerosol-induced solar heating and microphysical effects, and their global representation in climate models remains one of the largest uncertainties in estimates of future climate. Hence, new observations over the SE Atlantic have significant implications for global climate change scenarios. Our understanding of aerosol-cloud interactions in the SE Atlantic is hindered both by the lack of knowledge on aerosol and cloud properties, as well as the lack of knowledge about detailed physical processes involved. Most notably, we are missing knowledge on the absorptive and cloud nucleating properties of aerosols, including their vertical distribution relative to clouds, on the locations and degree of aerosol mixing into clouds, on the processes that govern cloud property adjustments, and on the importance of aerosol effects on clouds relative to co-varying synoptic scale meteorology. We discuss the current knowledge of aerosol and cloud property distributions based on satellite observations and sparse suborbital sampling. Recent efforts to make full use of A-Train aerosol sensor synergies will be highlighted. We describe planned field campaigns in the region to address the existing knowledge gaps. Specifically, we describe the scientific objectives and implementation of the five synergistic, international research activities aimed at providing some of the key aerosol and cloud properties and a process-level understanding of aerosol-cloud interactions over the SE Atlantic: NASA's ORACLES, the UK Met Office's CLARIFY-2016, the DoE's LASIC, NSF's ONFIRE, and CNRS' AEROCLO-SA.

aerosols↗
NASA NTRS2017

Sensitivity Analysis of the Bone Fracture Risk Model

Introduction: The probability of bone fracture during and after spaceflight is quantified to aid in mission planning, to determine required astronaut fitness standards and training requirements and to inform countermeasure research and design. Probability is quantified with a probabilistic modeling approach where distributions of model parameter values, instead of single deterministic values, capture the parameter variability within the astronaut population and fracture predictions are probability distributions with a mean value and an associated uncertainty. Because of this uncertainty, the model in its current state cannot discern an effect of countermeasures on fracture probability, for example between use and non-use of bisphosphonates or between spaceflight exercise performed with the Advanced Resistive Exercise Device (ARED) or on devices prior to installation of ARED on the International Space Station. This is thought to be due to the inability to measure key contributors to bone strength, for example, geometry and volumetric distributions of bone mass, with areal bone mineral density (BMD) measurement techniques. To further the applicability of model, we performed a parameter sensitivity study aimed at identifying those parameter uncertainties that most effect the model forecasts in order to determine what areas of the model needed enhancements for reducing uncertainty. Methods: The bone fracture risk model (BFxRM), originally published in (Nelson et al) is a probabilistic model that can assess the risk of astronaut bone fracture. This is accomplished by utilizing biomechanical models to assess the applied loads; utilizing models of spaceflight BMD loss in at-risk skeletal locations; quantifying bone strength through a relationship between areal BMD and bone failure load; and relating fracture risk index (FRI), the ratio of applied load to bone strength, to fracture probability. There are many factors associated with these calculations including environmental factors, factors associated with the fall event, mass and anthropometric values of the astronaut, BMD characteristics, characteristics of the relationship between BMD and bone strength and bone fracture characteristics. The uncertainty in these factors is captured through the use of parameter distributions and the fracture predictions are probability distributions with a mean value and an associated uncertainty. To determine parameter sensitivity, a correlation coefficient is found between the sample set of each model parameter and the calculated fracture probabilities. Each parameters contribution to the variance is found by squaring the correlation coefficients, dividing by the sum of the squared correlation coefficients, and multiplying by 100. Results: Sensitivity analyses of BFxRM simulations of preflight, 0 days post-flight and 365 days post-flight falls onto the hip revealed a subset of the twelve factors within the model which cause the most variation in the fracture predictions. These factors include the spring constant used in the hip biomechanical model, the midpoint FRI parameter within the equation used to convert FRI to fracture probability and preflight BMD values. Future work: Plans are underway to update the BFxRM by incorporating bone strength information from finite element models (FEM) into the bone strength portion of the BFxRM. Also, FEM bone strength information along with fracture outcome data will be incorporated into the FRI to fracture probability.

mathematical models↗
NASA NTRSDate not supplied

TPSAS-NF1676L-14454-DND

FUN3D Core Capabilities - Established as a research code in late 1980’s; now supports numerous internal and external efforts across the speed range - Solves 2D/3D steady and unsteady Euler and RANS equations on node-based mixed element grids for compressible and incompressible flows - General dynamic mesh capability: any combination of rigid / overset / morphing grids, including 6-DOF effects - Aeroelastic modeling using mode shapes, full FEM, CC, etc. - Constrained / multipoint adjoint-based design and mesh adaptation - Distributed development team using agile/extreme software practices including 24/7 regression and performance testing - Capabilities fully integrated, online documentation, training videos, tutorials

Eric J Nielsen↗
NASA NTRS1991

Blood Volume: Its Adaptation to Endurance Training

Expansion of blood volume (hypervolemia) has been well documented in both cross-sectional and longitudinal studies as a consequence of endurance exercise training. Plasma volume expansion can account for nearly all of the exercise-induced hypervolemia up to 2-4 wk; after this time expansion may be distributed equally between plasma and red cell volumes. The exercise stimulus for hypervolemia has both thermal and nonthermal components that increase total circulating plasma levels of electrolytes and proteins. Although protein and fluid shifts from the extravascular to intravascular space may provide a mechanism for rapid hypervolemia immediately after exercise, evidence supports the notion that chronic hypervolemia associated with exercise training represents a net expansion of total body water and solutes. This net increase of body fluids with exercise training is associated with increased water intake and decreased urine volume output. The mechanism of reduced urine output appears to be increased renal tubular reabsorption of sodium through a more sensitive aldosterone action in man. Exercise training-induced hypervolemia appears to be universal among most animal species, although the mechanisms may be quite different. The hypervolemia may provide advantages of greater body fluid for heat dissipation and thermoregulatory stability as well as larger vascular volume and filling pressure for greater cardiac stroke volume and lower heart rates during exercise.

Convertino, Victor A.↗
NASA NTRSDate not supplied

AMSR-2 Daily Snow Depth Data Product Using a Neural Network Algorithm Trained by Collocated ICESat-2 Measurements

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu 1 et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu 2 et al. (2022,2024) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, a neural network (NN) algorithm, employing several channels from AMSR-2 and the humidity vertical profiles GMAO GEOS-IT, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained NN snow-depth was applied to 2014-2020 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. This paper used AMSR-2 data, a passive microwave instrument to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
NASA NTRS2001

Ar Atmosphere: Implications for Structure and Composition of Mercury's Crust

We examine the possibilities of sustaining an argon atmosphere by diffusion from the upper 10 km of crust, and alternatively by effusion from a molten or previously molten area at great depth . Ar-40 in the atmospheres of the planets is a measure of potassium abundance in the interiors since Ar-40 is a product of radiogenic decay of K-40 by electron capture with the subsequent emission of a 1.46 eV gamma-ray. Although the Ar-40 in the earth's atmosphere is expected to have accumulated since the late bombardment, Ar-40 in surface-bounded exospheres is eroded quickly by photoionization and electron impact ionization. Thus, the argon content in the exospheres of the Moon, Mercury and probably Europa is representative of current effusion rather than accumulation over the lifetime of the body. Argon content will be a function of K content, temperature, grain size distribution, connected pore volume and possible seismic activity. Although Mercury and the Moon differ in many details, we can train the solutions to diffusion equations to predict the average lunar atmosphere. Then these parameters can be varied for Hermean conditions. Assuming a lunar crustal potassium abundance of 300 ppm, the observed argon atmosphere requires equilibrium between the argon production in the upper 9 Km of the moon (1.135 x 10(exp -3) cm(exp -3) s(exp -1)) and its loss. Hodges et al. conclude that this loss rate and the observed time variability requires argon release through seismic activity, tapping a deep argon source. An important observation is that the extreme surface of the Moon is enhanced in argon rather than depleted, as one would expect from outgassing of radiogenic argon. Manka and Michel concluded that ion implantation explains the surface enhancement of Ar-40. About half of the argon ions produced in the lunar atmosphere would return to the surface, where they would become embedded in the rocks. Similarly, at Mercury we expect the surface rocks to be enhanced in Ar-40 wherever the magnetosphere has been open over time. Thus the measurement of surface composition will reveal the long-term effects of solar wind-magnetosphere interaction. Additional information is contained in the original extended abstract.

Killen, R. M.↗
NASA NTRS2005

Conflict Resolution Performance in an Experimental Study of En Route Free Maneuvering Operations

NASA has developed a far-term air traffic management concept, termed Distributed Air/Ground Traffic Management (DAG-TM). One component of DAG-TM, En Route Free Maneuvering, allows properly trained flight crews of equipped autonomous aircraft to assume responsibility for separation from other autonomous aircraft and from Instrument Flight Rules (IFR) aircraft. Ground-based air traffic controllers continue to separate IFR traffic and issue flow management constraints to all aircraft. To examine En Route Free Maneuvering operations, a joint human-in-the-loop experiment was conducted in summer 2004 at the NASA Ames and Langley Research Centers. Test subject pilots used desktop flight simulators to resolve traffic conflicts and adhere to air traffic flow constraints issued by subject controllers. The experimental airspace integrated both autonomous and IFR aircraft at varying traffic densities. This paper presents a subset of the En Route Free Maneuvering experimental results, focusing on airborne and ground-based conflict resolution, and the effects of increased traffic levels on the ability of pilots and air traffic controllers to perform this task. The results show that, in general, increases in autonomous traffic do not significantly impact conflict resolution performance. In addition, pilot acceptability of autonomous operations remains high throughout the range of traffic densities studied. Together with previously reported findings, these results continue to support the feasibility of the En Route Free Maneuvering component of DAG-TM.

Doble, Nathan A.↗
NASA NTRS2008

Operational Characteristics and Plasma Measurements in a Low-Energy FARAD Thruster

Pulsed inductive plasma accelerators are spacecraft propulsion devices in which energy is stored in a capacitor and then discharged through an inductive coil. The device is electrodeless, inducing a plasma current sheet in propellant located near the face of the coil. The propellant is accelerated and expelled at a high exhaust velocity (order of 10 km/s) through the interaction of the plasma current with an induced magnetic field. The Faraday Accelerator with RF-Assisted Discharge (FARAD) thruster is a type of pulsed inductive plasma accelerator in which the plasma is preionized by a mechanism separate from that used to form the current sheet and accelerate the gas. Employing a separate preionization mechanism in this manner allows for the formation of an inductive current sheet at much lower discharge energies and voltages than those found in previous pulsed inductive accelerators like the Pulsed Inductive Thruster (PIT). In this paper, we present measurements aimed at quantifying the thruster's overall operational characteristics and providing additional insight into the nature of operation. Measurements of the terminal current and voltage characteristics during the pulse help quantify the output of the pulsed power train driving the acceleration coil. A fast ionization gauge is used to measure the evolution of the neutral gas distribution in the accelerator prior to a pulse. The preionization process is diagnosed by monitoring light emission from the gas using a photodiode, and a time-resolved global view of the evolving, accelerating current sheet is obtained using a fast-framing camera. Local plasma and field measurements are obtained using an array of intrusive probes. The local induced magnetic field and azimuthal current density are measured using B-dot probes and mini-Rogowski coils, respectively. Direct probing of the number density and electron temperature is performed using a triple probe.

Polzin, K. A.↗
NASA NTRS2016

Evaluation of the Cloud Fields in the UK Met Office HadGEM3-UKCA Model Using the CCCM Satellite Data Product to Advance Our Understanding of the Influence of Clouds on Tropospheric Composition and Chemistry

To determine the role of clouds in driving inter-annual and inter-seasonal variability of trace gases in the troposphere and lower stratosphere with a particular focus on the importance of cloud modification of photolysis. To evaluate the cloud fields and their vertical distribution in the HadGEM3 model utilizing CCCM, a unique 3-D cloud data product merged from multiple A-Train satellites (CERES, CloudSat, CALIPSO, and MODIS) developed at the NASA Langley Research Center.

Varma, Sunil↗
NASA NTRS2008

Stay Alive--Simulation for Situational Safety Awareness

STAY ALIVE is an idea for a safety awareness simulation prototype, powered by gaming technology, that would make safety training enlightening, engaging and fun. Recalling initial instructions and using situational awareness principles, participants would escape a fire by choosing the appropriate door. Escape times would be measured while stressors increased. This presentation describes how STAY ALIVE utilizes first person point of view (PoV), a generic scenario, immersion- and presence-enhancing design, and ease of distribution to provide more people opportunity to realize, review, analyze and practice effective awareness behaviors. The goals for this prototype include facilitating interest in first-person PoV safety training and eliciting further suggestions on prevention technologies.

Ruder, Michelle↗
NASA NTRS1974

Simulation techniques for estimating error in the classification of normal patterns

Methods of efficiently generating and classifying samples with specified multivariate normal distributions were discussed. Conservative confidence tables for sample sizes are given for selective sampling. Simulation results are compared with classified training data. Techniques for comparing error and separability measure for two normal patterns are investigated and used to display the relationship between the error and the Chernoff bound.

Whitsitt, S. J.↗
NASA NTRS2004

Software for Autonomous Spacecraft Maneuvers

The AutoCon computer programs facilitate and accelerate the planning and execution of orbital control maneuvers of spacecraft while analyzing and resolving mission constraints. AutoCon-F is executed aboard spacecraft, enabling the spacecraft to plan and execute maneuvers autonomously; AutoCon-G is designed for use on the ground. The AutoCon programs utilize advanced techniques of artificial intelligence, including those of fuzzy logic and natural-language scripting, to resolve multiple conflicting constraints and automatically plan maneuvers. These programs can be used to satisfy requirements for missions that involve orbits around the Earth, the Moon, or any planet, and are especially useful for missions in which there are requirements for frequent maneuvers and for resolution of complex conflicting constraints. During operations, the software targets new trajectories, places and sizes maneuvers, and controls spacecraft burns. AutoCon-G provides a userfriendly graphical interface, and can be used effectively by an analyst with minimal training. AutoCon-F reduces latency and supports multiple-spacecraft and formation-flying missions. The AutoCon architecture supports distributive processing, which can be critical for formation- control missions. AutoCon is completely object-oriented and can easily be enhanced by adding new objects and events. AutoCon-F was flight demonstrated onboard GSFC's EO-1 spacecraft flying in formation with Landsat-7.

Bristow, John↗
NASA NTRS2011

A Human Factors Approach to Bridging Systems and Introducing New Technologies

The application of human factors in aviation has grown to cover a wide range of disciplines and methods capable of assessing human-systems integration at many levels. For example, at the individual level, pilot workload may be studied while at the team level, coordinated workload distribution may be the focal point. At the organizational level, the way in which individuals and teams are supported by training and standards, policies and procedures may introduce additional, relevant topics. A consideration of human factors at each level contributes to our understanding of successes and failures in pilot performance, but this system focused on the flight deck alone -- is only one part of the airspace system. In the FAA's NextGen plan to overhaul the National Airspace System (NAS), new capabilities will enhance flightdeck systems (pilots), flight operations centers (dispatchers) and air traffic control systems (controllers and air traffic managers). At a minimum, the current roles and responsibilities of these three systems are likely to change. Since increased automation will be central to many of the enhancements, the role of automation is also likely to change. Using NextGen examples, a human factors approach for bridging complex airspace systems will be the main focus of this presentation. It is still crucial to consider the human factors within each system, but the successful implementation of new technologies in the NAS requires an understanding of the collaborations that occur when these systems intersect. This human factors approach to studying collaborative systems begins with detailed task descriptions within each system to establish a baseline of the current operations. The collaborative content and context are delineated through the review of regulatory and advisory materials, letters of agreement, policies, procedures and documented practices. Field observations and interviews also help to fill out the picture. Key collaborative functions across systems are identified and placed on a phase-of-flight timeline including information requirements, decision authority and use of automation, as well as level of frequency and criticality.

Kanki, Barbara G.↗
NASA NTRS2016

A First Look at the Upcoming SISO Space Reference FOM

Spaceflight is difficult, dangerous and expensive; human spaceflight even more so. In order to mitigate some of the danger and expense, professionals in the space domain have relied, and continue to rely, on computer simulation. Simulation is used at every level including concept, design, analysis, construction, testing, training and ultimately flight. As space systems have grown more complex, new simulation technologies have been developed, adopted and applied. Distributed simulation is one those technologies. Distributed simulation provides a base technology for segmenting these complex space systems into smaller, and usually simpler, component systems or subsystems. This segmentation also supports the separation of responsibilities between participating organizations. This segmentation is particularly useful for complex space systems like the International Space Station (ISS), which is composed of many elements from many nations along with visiting vehicles from many nations. This is likely to be the case for future human space exploration activities. Over the years, a number of distributed simulations have been built within the space domain. While many use the High Level Architecture (HLA) to provide the infrastructure for interoperability, HLA without a Federation Object Model (FOM) is insufficient by itself to insure interoperability. As a result, the Simulation Interoperability Standards Organization (SISO) is developing a Space Reference FOM. The Space Reference FOM Product Development Group is composed of members from several countries. They contribute experiences from projects within NASA, ESA and other organizations and represent government, academia and industry. The initial version of the Space Reference FOM is focusing on time and space and will provide the following: (i) a flexible positioning system using reference frames for arbitrary bodies in space, (ii) a naming conventions for well-known reference frames, (iii) definitions of common time scales, (iv) federation agreements for common types of time management with focus on time stepped simulation, and (v) support for physical entities, such as space vehicles and astronauts. The Space Reference FOM is expected to make collaboration politically, contractually and technically easier. It is also expected to make collaboration easier to manage and extend.

Mueller, Bjorn↗
NASA NTRS2011

Collaborative Supervised Learning for Sensor Networks

Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

Wagstaff, Kiri L.↗
NASA NTRS1995

Bayes classification of interferometric TOPSAR data

We report the Bayes classification of terrain types at different sites using airborne interferometric synthetic aperture radar (INSAR) data. A Gaussian maximum likelihood classifier was applied on multidimensional observations derived from the SAR intensity, the terrain elevation model, and the magnitude of the interferometric correlation. Training sets for forested, urban, agricultural, or bare areas were obtained either by selecting samples with known ground truth, or by k-means clustering of random sets of samples uniformly distributed across all sites, and subsequent assignments of these clusters using ground truth. The accuracy of the classifier was used to optimize the discriminating efficiency of the set of features that was chosen. The most important features include the SAR intensity, a canopy penetration depth model, and the terrain slope. We demonstrate the classifier's performance across sites using a unique set of training classes for the four main terrain categories. The scenes examined include San Francisco (CA) (predominantly urban and water), Mount Adams (WA) (forested with clear cuts), Pasadena (CA) (urban with mountains), and Antioch Hills (CA) (water, swamps, fields). Issues related to the effects of image calibration and the robustness of the classification to calibration errors are explored. The relative performance of single polarization Interferometric data classification is contrasted against classification schemes based on polarimetric SAR data.

Michel, T. R.↗
NASA NTRS2019

Overview of Developments in Voluntary Consensus Standards for Counterfeit Parts Avoidance

The presentation provides an introduction to the electronic parts supply chain and various standards used for preventing counterfeit parts. The presentation provides detailed instruction on various non-destructive techniques, destructive and analytical steps for inspecting suspect counterfeit parts. The techniques are based on the AS6171 Aerospace Standard which standardizes the test and inspection procedures, workmanship criteria, and minimum training and certification requirements to detect counterfeit electrical, electronic, and electromechanical parts. Also included are discussions of other inspection, sampling and distribution standards.

Sood, Bhanu↗