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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

The Conservation of Structure and Mechanism of Catalytic Action in a Family of Thiamin Pyrophosphate (TPP)-dependent Enzymes

Thiamin pyrophosphate (TPP)-dependent enzymes are a divergent family of TPP and metal ion binding proteins that perform a wide range of functions with the common decarboxylation steps of a -(O=)C-C(OH)- fragment of alpha-ketoacids and alpha- hydroxyaldehydes. To determine how structure and catalytic action are conserved in the context of large sequence differences existing within this family of enzymes, we have carried out an analysis of TPP-dependent enzymes of known structures. The common structure of TPP-dependent enzymes is formed at the interface of four alpha/beta domains from at least two subunits, which provide for two metal and TPP-binding sites. Residues around these catalytic sites are conserved for functional purpose, while those further away from TPP are conserved for structural reasons. Together they provide a network of contacts required for flip-flop catalytic action within TPP-dependent enzymes. Thus our analysis defines a TPP-action motif that is proposed for annotating TPP-dependent enzymes for advancing functional proteomics.

Dominiak, P.↗
NASA NTRS2007

Characterizing SWCNT Dispersion in Polymer Composites

The new wave of single wall carbon nanotube (SWCNT) infused composites will yield structurally sound multifunctional nanomaterials. The SWCNT network requires thorough dispersion within the polymer matrix in order to maximize the benefits of the nanomaterial. However, before any nanomaterials can be used in aerospace applications a means of quality assurance and quality control must be certified. Quality control certification requires a means of quantification, however, the measurement protocol mandates a method of seeing the dispersion first. We describe here the new tools that we have developed and implemented to first be able to see carbon nanotubes in polymers and second to measure or quantify the dispersion of the nanotubes.

Lillehei, Peter T.↗
NASA NTRS2007

Eye Safe, Visible Wavelength Lidar Systems: Design and Operational Advances, Results and Potential

In the early nineties the first of the eye safe visible wavelength lidar systems known now as Micro Pulse Lidar (MPL) became operational. The important advance of the design was a system that, unlike most existing lidar, operated at eye safe energy densities and could thus operate unattended for full time monitoring. Since that time there have been many dozens of these systems produced and applied for full time profiling of atmospheric cloud and aerosol structure. There is currently an observational network of MPL sites to support global climate research. In thc course of application of these instruments there have been significant improvements in the, design and performance of the systems. In the last half decade particularly there has been significant application and technical development of MPL systems. In this paper we review progress. The current MPL systems in use are all single wavelength systems designed for cloud and aerosol applications. For the cloud and aerosol applications, both lidar depolarization and multi wavelength measurements have significant applications. These can be accomplished with the MPL, approach. The main current challenge for the lidar network activity are in the area of the reliability, repeatability and efficiency of data processing. The network makes use of internet data downloads and automated processing. The heights of all cloud and aerosol layers are needed. The recent emphasis has been in operationally deriving aerosol extinction cross section. Future emphasis will include adding cirrus optical parameters. For operational effectiveness, improvements to simplify routine data signal calibration are being researched. Overall the MPL systems have proven very effective. A large data base of results from globally distributed sites can be easily accessed through the internet. Applications have included atmospheric model development. Validation of current global satellite observations of aerosol and clouds, including now orbital lidar observations, was a primary goal for NASA. Although sampling issues require careful consideration, results have proven useful.

Spinhirne, James↗
NASA NTRS2009

The Lunar Quest Program and the International Lunar Network (ILN)

The Lunar and Planetary Science group at Marshall provides core capabilities to support the Agency's lunar exploration goals. ILN Anchor Nodes are currently in development by MSFC and APL under the Lunar Quest Program at MSFC. The Science objectives of the network are to understand the interior structure and composition of the moon. Pre-phase A engineering assessments are complete, showing a design that can achieve the science requirements, either on their own (if 4 launched) or in concert with international partners. Risk reduction activities are ongoing. The Lunar Quest Program is a Science-based program with the following goals: a) Fly small/medium science missions to accomplish key science goals; b) Build a strong lunar science community; c) Provide opportunities to demonstrate new technologies; and d) Where possible, help ESMD and SOMG goals and enhance presence of science in the implementation of the VSE. The Lunar Quest Program will be guided by recommendations from community reports.

Cohen, Barbara A.↗
NASA NTRS2012

Small Radioisotope Power System at NASA Glenn Research Center

In April 2009, NASA Glenn Research Center (GRC) formed an integrated product team (IPT) to develop a Small Radioisotope Power System (SRPS) utilizing a single Advanced Stirling Convertor (ASC) with passive balancer for possible use by the International Lunar Network (ILN) program. The ILN program is studying the feasibility of implementing a multiple node seismometer network to investigate the internal lunar structure. A single ASC produces approximately 80 W(sub e) and could potentially supply sufficient power for that application. The IPT consists of Sunpower, Inc., to provide the single ASC with balancer, The Johns Hopkins University Applied Physics Laboratory (JHU/APL) to design an engineering model Single Convertor Controller (SCC) for an ASC with balancer, and NASA GRC to provide technical support to these tasks and to develop a simulated lunar lander test stand. A controller maintains stable operation of an ASC. It regulates the alternating current produced by the linear alternator of the convertor, provides a specified output voltage, and maintains operation at a steady piston amplitude and hot end temperature. JHU/APL also designed an ASC dynamic engine/alternator simulator to aid in the testing and troubleshooting of the SCC. This paper describes the requirements, design, and development of the SCC, including some of the key challenges and the solutions chosen to overcome those issues. In addition, it describes the plans to analyze the effectiveness of a passive balancer to minimize vibration from the ASC, characterize the effect of ASC vibration on a lunar lander, characterize the performance of the SCC, and integrate the single ASC, SCC, and lunar lander test stand to characterize performance of the overall system.

Dugala, Gina M.↗
NASA NTRS2012

An Integrated, Layered-Spinel Composite Cathode for Energy Storage Applications

At low operating temperatures, commercially available electrode materials for lithium-ion batteries do not fully meet the energy and power requirements for NASA fs exploration activities. The composite cathode under development is projected to provide the required energy and power densities at low temperatures and its usage will considerably reduce the overall volume and weight of the battery pack. The newly developed composite electrode material can provide superior electrochemical performance relative to a commercially available lithium cobalt system. One advantage of using a composite cathode is its higher energy density, which can lead to smaller and lighter battery packs. In the current program, different series of layered-spinel composite materials with at least two different systems in an integrated structure were synthesized, and the volumetric and gravimetric energy densities were evaluated. In an integrated network of a composite electrode, the effect of the combined structures is to enhance the capacity and power capabilities of the material to levels greater than what is possible in current state-of-the-art cathode systems. The main objective of the current program is to implement a novel cathode material that meets NASA fs low temperature energy density requirements. An important feature of the composite cathode is that it has at least two components (e.g., layered and spinel) that are structurally integrated. The layered material by itself is electrochemically inactive; however, upon structural integration with a spinel material, the layered material can be electrochemically activated, thereby delivering a large amount of energy with stable cycling. A key aspect of the innovation has been the development of a scalable process to produce submicronand micron-scale particles of these composite materials. An additional advantage of using such a composite electrode material is its low irreversible loss (.5%), which is primarily due to the unique activation of the composite. High columbic efficiency (greater than 99%) upon cycling may indicate the formation of a stable SEI (solid-electrolyte interface) layer, which can contribute to long cycle life. The innovation in the current program, when further developed, will enable the system to maintain high energy and power densities at low temperatures, improve efficiency, and further stabilize and enhance the safety of the cell.

Hagh, Nader↗
NASA NTRSDate not supplied

Lessons Learned in the Implementation of a Training Knowledge Management System for the SERVIR Network

The SERVIR program is a unique partnership between NASA, the U.S. Agency for International Development (USAID), focusing on building capacity to use Earth observations for addressing development challenges. In that context, between 2004 and 2020, the program delivered approximately 365 trainings to almost 10,000 professionals. More recently, between November 2020 and August 2021, the SERVIR network executed some 55 training events addressing SERVIR’s 4 priority thematic areas, and roughly a quarter of SERVIR trainings overall have focused on themes related to Ecological Forecasting. Due to the ongoing COVID-19 pandemic, almost four-fifths of recent training events have been virtual, with the remainder being in-person under limited circumstances. The large number of training events delivered represents both an opportunity and a challenge in terms of knowledge management. While the training materials developed can later be reused in other parts of the SERVIR network, prior to recently, the lack of a central repository for those materials has prevented wider dissemination and use. The recently developed Training Knowledge Management System (TKMS) is now becoming an integral part of the SERVIR Capacity Building Framework, supporting the exchange of resources and methods for conducting training activities across the network. This presentation focuses on the structure of this system, as well as on the anticipated benefits for the User Communities for Earth Observations of Terrestrial Systems.

training↗
NASA NTRS2003

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

NASA Discipline Neuroscience↗
NASA NTRS1985

Climatology of the equatorial lower stratosphere

Twenty years of radiosonde data have been analyzed in an attempt to develop a latitudinal structure climatology of winds, temperature and geopotential at 30 and 50 mb in the equatorial stratosphere. The fine latitudinal resolution provided by the WMO station network reveals several interesting features in the latitudinal structure of the annual and quasi-biennial cycles which dominate this region. For example, the westerly and easterly acceleration phases of the quasi-biennial oscillation are markedly different. Westerly accelerations appear first at the equator, spreading outward with time to higher latitudes, and are more intense, on average, than the easterly accelerations. The easterly accelerations are more uniform in latitude, but less uniform in time, sometimes occurring in two stages. The quasi-biennial wind and temperature oscillations are symmetric about the equator, while the annual harmonic in zonal wind is antisymmetric about the equator, but is not proportional to the Coriolis parameters. Monthly mean zonal wind and temperature appear to be in thermal wind balance at the equator. Some brief remarks are also made concerning variability of the quasi-biennial oscillation and the effects of El Chichon.

Dunkerton, T. J.↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T 2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

neural networks↗
NASA NTRS1996

User-Centered Indexing for Adaptive Information Access

We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Leading and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments.

Chen, James R.↗
NASA NTRS1990

Neurobiological computational models in structural analysis and design

The present paper examines the role of neural computing strategies in structural analysis and design. A principal focus of the work resides in the use of neural networks to represent the force-displacement relationship in static structural analysis. Such models provide computationally efficient capabilities for reanalysis, and appear to be well suited for application in numerical optimum design. The paper presents an overview of the neural computing approach, with special emphasis on supervised learning techniques adopted in the present work. Special features of such learning strategies which have a direct bearing on numerical accuracy and efficiency, are examined in the context of representative structural optimization problems.

Hajela, P.↗
NASA NTRS1991

Neurobiological computational models in structural analysis and design

This paper examines the role of neural computing strategies in structural analysis and design. A principal focus of the work resides in the use of neural networks to represent the force-displacement relationship in static structural analysis. Such models provide computationally efficient capabilities for reanalysis, and appear to be well suited for application in numerical optimum design. The paper presents an overview of the neutral computing approach, with special emphasis on supervised learning techniques adopted in the present work. Special features of such learning strategies which have a direct bearing on numerically accuracy and efficiency, are examined in the context of representative structural optimization problems.

Hajela, P.↗
NASA NTRS2005

ALI (Autonomous Lunar Investigator): Revolutionary Approach to Exploring the Moon with Addressable Reconfigurable Technology

Addressable Reconfigurable Technology (ART) based structures: Mission Concepts based on Addressable Reconfigurable Technology (ART), originally studied for future ANTS (Autonomous Nanotechnology Swarm) Space Architectures, are now being developed as rovers for nearer term use in lunar and planetary surface exploration. The architecture is based on the reconfigurable tetrahedron as a building block. Tetrahedra are combined to form space-filling networks, shaped for the required function. Basic structural components are highly modular, addressable arrays of robust nodes (tetrahedral apices) from which highly reconfigurable struts (tetrahedral edges), acting as supports or tethers, are efficiently reversibly deployed/stowed, transforming and reshaping the structures as required.

Clark, P. E.↗
NASA NTRS2011

Network Flow Simulation of Fluid Transients in Rocket Propulsion Systems

Fluid transients, also known as water hammer, can have a significant impact on the design and operation of both spacecraft and launch vehicle propulsion systems. These transients often occur at system activation and shutdown. The pressure rise due to sudden opening and closing of valves of propulsion feed lines can cause serious damage during activation and shutdown of propulsion systems. During activation (valve opening) and shutdown (valve closing), pressure surges must be predicted accurately to ensure structural integrity of the propulsion system fluid network. In the current work, a network flow simulation software (Generalized Fluid System Simulation Program) based on Finite Volume Method has been used to predict the pressure surges in the feed line due to both valve closing and valve opening using two separate geometrical configurations. The valve opening pressure surge results are compared with experimental data available in the literature and the numerical results compared very well within reasonable accuracy (< 5%) for a wide range of inlet-to-initial pressure ratios. A Fast Fourier Transform is preformed on the pressure oscillations to predict the various modal frequencies of the pressure wave. The shutdown problem, i.e. valve closing problem, the simulation results are compared with the results of Method of Characteristics. Most rocket engines experience a longitudinal acceleration, known as "pogo" during the later stage of engine burn. In the shutdown example problem, an accumulator has been used in the feed system to demonstrate the "pogo" mitigation effects in the feed system of propellant. The simulation results using GFSSP compared very well with the results of Method of Characteristics.

Bandyopadhyay, Alak↗
NASA NTRS1986

An analysis of mesoscale VAS retrievals using statistical structure functions

Mesoscale sounding data from a truth rawinsonde network and a VAS from March 6-7, 1982 are statistically studied. The parameters of temperature, geopotential height, and mixing ratio at 850, 700, 500, 300, and 200 mb levels, and thickness and precipitable water are examined. The VAS retrieval gradients derived from a physical algorithm and from a regression technique are analyzed; the analysis reveals that both techniques have similar error characteristics and capabilities for calculating gradients of mesoscale temperature and geopotential height. Ground-truth rawinsonde data and VAS retrievals are compared; it is observed that VAS data describes weaker gradients than rawinsonde data.

Fuelberg, H. E.↗