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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Synthesis, structure and optical properties of two isotypic crystals, Na{sub 3}MO{sub 4}Cl (M=W, Mo)

Two isotypic compounds, Na{sub 3}MO{sub 4}Cl (M = W, Mo) have been obtained from the high temperature solution, and their structures were determined by single-crystal X-ray diffraction. Both of them crystallize in the space group P4/nmm of tetragonal system with the unit cells: a=7.5181(15), c=5.360(2) for Na{sub 3}WO{sub 4}Cl and a=7.4942(12), c=5.3409(18) for Na{sub 3}MoO{sub 4}Cl. The structure exhibits a 3D network built up by the ClNa{sub 6} groups, and the MO{sub 4} groups reside in the tunnels of the 3D network. The structural similarities and differences between Na{sub 3}MO{sub 4}Cl (M=W, Mo) and Sr{sub 3}MO{sub 4}F (M=Al, Ga) have been discussed. Meanwhile, detailed structure comparison analyses between Na{sub 3}MO{sub 4}Cl (M=W, Mo) and Na{sub 3}MO{sub 4}F (M=W, Mo) indicate that the different connection modes of ClNa{sub 6} and FNa{sub 6} make Na{sub 3}MO{sub 4}Cl and Na{sub 3}MO{sub 4}F crystallize in different structures. The IR spectra were used to verify the validity of the structure. The diffuse reflectance spectra show that the UV absorption edges are about 249 nm (4.99 eV) and 265 nm (4.69 eV) for Na{sub 3}WO{sub 4}Cl and Na{sub 3}MoO{sub 4}Cl, respectively. In addition, the first-principles theoretical studies are also carried out to aid the understanding of electronic structures and linear optical properties. - Graphical abstract: Two isotypic compounds, Na{sub 3}MO{sub 4}Cl (M=W, Mo) have been obtained from the high temperature solution. Both of them crystallize in the space group P4/nmm of tetragonal system. The structure exhibits a 3D network built up by the ClNa{sub 6} groups, and the MO{sub 4} groups reside in the tunnels of the 3D network. - Highlights: • Structure and properties of Na{sub 3}MO{sub 4}Cl (M=W, Mo) are reported for the first time. • They show a 3D network built by ClNa{sub 6}, and WO{sub 4} lies in the tunnels of the network. • IR spectra were used to verify the validity of the structure. • Band structures and density of states have been calculated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Investigation into the effect on structure of oxoanion doping in Na{sub 2}M(SO{sub 4}){sub 2}·2H{sub 2}O

In this paper an investigation into the effect of transition metal ion and selenate/fluorophosphate doping on the structures of Na{sub 2}M(SO{sub 4}){sub 2}·2H{sub 2}O (M=transition metal) materials is reported. In agreement with previous reports, the monoclinic (Kröhnkite) structure is adopted for M=Mn, Fe, Co, Cu, while for the smallest first row divalent transition metal ion, M=Ni, the triclinic (Fairfieldite structure) is adopted. On selenate doping there is a changeover in structure from monoclinic to triclinic for M=Fe, Co, Cu, with the larger Fe{sup 2+} system requiring the highest level of selenate to complete the changeover. Thus the results suggest that the relative stability of the two structure types is influenced by the relative size of the transition metal: oxoanion group, with the triclinic structure favoured for small transition metals/large oxoanions. The successful synthesis of fluorophosphate doped samples, Na{sub 2}M(SO{sub 4}){sub 2−x}(PO{sub 3}F){sub x}·2H{sub 2}O was also obtained for M=Fe, Co, Cu, with the results showing a changeover in structure from monoclinic to triclinic for M=Co, Cu for very low levels (x=0.1) of fluorophosphate. In the case of M=Fe, the successful synthesis of fluorophosphates samples was achieved for x≤0.3, although no change in cell symmetry was observed. Rather in this particular case, the X-ray diffraction patterns showed evidence for selective peak broadening, attributed to local disorder as a result of the fluorophosphate group disrupting the H-bonding network. Overall the work highlights how isovalent doping can be exploited to alter the structures of Na{sub 2}M(SO{sub 4}){sub 2}·2H{sub 2}O systems. - Graphical abstract: Partial substitution of sulfate in Na{sub 2}M(SO{sub 4}){sub 2}0.2H{sub 2}O (M=Co, Cu) by selenate or fluorophosphate leads to a structural change from the monoclinic Kröhnkite to the triclinic Fairfieldite structure. - Highlights: • The successful synthesis of Na{sub 2}M(SO{sub 4}){sub 2}·2H{sub 2}O (M=transition metal) phases doped with selenate and fluorophosphate. • A change in structure is observed on selenate doping for M=Fe, Co, Cu. • This change in structure is also observed on fluorophosphate doping for M=Co, Cu. • The work highlights how isovalent doping can be exploited to alter the structures of Na{sub 2}M(SO{sub 4}){sub 2}·2H{sub 2}O systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Water Network Tool for Resilience (WNTR). User Manual, Version 0.2.3

The Water Network Tool for Resilience (WNTR, pronounced winter) is a Python package designed to simulate and analyze resilience of water distribution networks. Here, a network refers to the collection of pipes, pumps, valves, junctions, tanks, and reservoirs that make up a water distribution system. WNTR has an application programming interface (API) that is flexible and allows for changes to the network structure and operations, along with simulation of disruptive incidents and recovery actions. WNTR is based upon EPANET, which is a tool to simulate the movement and fate of drinking water constituents within distribution systems. Users are encouraged to be familiar with the use of EPANET and/or should have background knowledge in hydraulics and pressurized pipe network modeling before using WNTR. EPANET has a graphical user interface that might be a useful tool to facilitate the visualization of the network and the associated analysis results. Information on EPANET can be found at https://www.epa.gov/water-research/epanet. WNTR is compatible with EPANET 2.00.12 [Ross00]. In addition, users should have experience using Python, including the installation of additional Python packages. General information on Python can be found at https://www.python.org/.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Prototype Distributed Ledger Technology of UF 6 Cylinder Tracking in Ethereum

We have created a demonstration permissioned Distributed Ledger Technology (DLT) datastore for the UF 6 cylinder tracking safeguards use-case utilizing the Ethereum DLT framework and using Solidity for smart contract code. Our demonstration creates a simulated dataset representing tracking of 75,000 UF 6 cylinders across 11 example nuclear facilities worldwide. Our DLT system allows for easy input and reading of shipping and receiving data, including a Graphical User Interface (GUI). Sandia’s Emulytics capability was leveraged to help create the DLT node network and assess performance. We find that our DLT prototype can easily handle to ~150,000 UF 6 cylinder shipments per year worldwide, without any excessive computational or storage burden on the IAEA or Member States. Next steps could include a demonstration to the IAEA and potentially demonstrating integration with TradeLens, a DLT in use by a consortium of international shipping companies representing over half of world shipping trade.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1983

An interactive graphics program for manipulation and display of panel method geometry

Modern aerodynamic panel methods that handle large, complex geometries have made evident the need to interactively manipulate, modify, and view such configurations. With this purpose in mind, the GEOM program was developed. It is a menu driven, interactive program that uses the Tektronix PLOT 10 graphics software to display geometry configurations which are characterized by an abutting set of networks. These networks are composed of quadrilateral panels which are described by the coordinates of their corners. GEOM is divided into fourteen executive controlled functions. These functions are used to build configurations, scale and rotate networks, transpose networks defining M and N lines, graphically display selected networks, join and split networks, create wake networks, produce symmetric images of networks, repanel and rename networks, display configuration cross sections, and output network geometry in two formats. A data base management system is used to facilitate data transfers in this program. A sample session illustrating various capabilities of the code is included as a guide to program operation.

Hall, J. F.↗
NASA NTRS1991

Numerical Aerodynamic Simulation Program

Report describes developments that occurred at National Aerodynamics Simulation (NAS) facility at Ames Research Center during years 1987 and 1988. Begins with description of NAS processing network, which is large network of computers. Followed by summary of early achievements of program and advanced features of network, including installation of supercomputers, installation of standard operating system and communication software on all processors, accessibility to users at remote facilities across United States, and emphasis on development of graphics workstations to display results of numerical simulation.

Bailey, F. R.↗
NASA NTRS1978

Creation of lumped parameter thermal model by the use of finite elements

In the finite difference technique, the thermal network is represented by an analogous electrical network. The development of this network model, which is used to describe a physical system, often requires tedious and mental data preparation and checkout by the analyst which can be greatly reduced through the use of the computer programs to develop automatically the mathematical model and associated input data and graphically display the analytical model to facilitate model verification. Three separate programs are involved which are linked through common mass storage files and data card formats. These programs are SPAR, CINGEN and GEOMPLT, and are used to (1) develop thermal models for the MITAS II thermal analyzer program; (2) produce geometry plots of the thermal network; and (3) produce temperature distribution and time history plots.

Source record↗
DOE OSTI2017

Machine Learning

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

Anomaly detection↗
NASA NTRS1992

Implementation of an intelligent control system

A laboratory testbed facility which was constructed at NASA LeRC for the development of an Intelligent Control System (ICS) for reusable rocket engines is described. The framework of the ICS consists of a hierarchy of various control and diagnostic functions. The traditional high speed, closed-loop controller resides at the lowest level of the ICS hierarchy. Above this level resides the diagnostic functions which identify engine faults. The ICS top level consists of the coordination function which manages the interaction between an expert system and a traditional control system. The purpose of the testbed is to demonstrate the feasibility of the OCS concept by implementing the ICS as the primary controller in a simulation of the Space Shuttle Main Engine (SSME). The functions of the ICS which are implemented in the testbed are as follows: an SSME dynamic simulation with selected fault mode models, a reconfigurable controller, a neural network for sensor validation, a model-based failure detection algorithm, a rule based failure detection algorithm, a diagnostic expert system, an intelligent coordinator, and a user interface which provides a graphical representation of the event occurring within the testbed. The diverse nature of the ICS has led to the development of a distributed architecture consisting of specialized hardware and software for the implementation of the various functions. This testbed is made up of five different computer systems. These individual computers are discussed along with the schemes used to implement the various ICS components. The communication between computers and the timing and synchronization between components are also addressed.

Simon, D. L.↗
NASA NTRS1990

Determining crop residue type and class using satellite acquired data

LANDSAT Thematic Mapper (TM) data for March 23, 1987 with accompanying ground truth data for the study area in Miami County, IN were used to determine crop residue type and class. Principle components and spectral ratioing transformations were applied to the LANDSAT TM data. One graphic information system (GIS) layer of land ownership was added to each original image as the eighth band of data in an attempt to improve classification. Maximum likelihood, minimum distance, and neural networks were used to classify the original, transformed, and GIS-enhanced remotely sensed data. Crop residues could be separated from one another and from bare soil and other biomass. Two types of crop residue and four classes were identified from each LANDSAT TM image. The maximum likelihood classifier performed the best classification for each original image without need of any transformation. The neural network classifier was able to improve the classification by incorporating a GIS-layer of land ownership as an eighth band of data. The maximum likelihood classifier was unable to consider this eighth band of data and thus, its results could not be improved by its consideration.

Zhuang, Xin↗
NASA NTRS1993

MIRAGE: The data acquisition, analysis, and display system

Developed for the NASA Johnson Space Center and Life Sciences Directorate by GE Government Services, the Microcomputer Integrated Real-time Acquisition Ground Equipment (MIRAGE) system is a portable ground support system for Spacelab life sciences experiments. The MIRAGE system can acquire digital or analog data. Digital data may be NRZ-formatted telemetry packets of packets from a network interface. Analog signal are digitized and stored in experimental packet format. Data packets from any acquisition source are archived to a disk as they are received. Meta-parameters are generated from the data packet parameters by applying mathematical and logical operators. Parameters are displayed in text and graphical form or output to analog devices. Experiment data packets may be retransmitted through the network interface. Data stream definition, experiment parameter format, parameter displays, and other variables are configured using spreadsheet database. A database can be developed to support virtually any data packet format. The user interface provides menu- and icon-driven program control. The MIRAGE system can be integrated with other workstations to perform a variety of functions. The generic capabilities, adaptability and ease of use make the MIRAGE a cost-effective solution to many experimental data processing requirements.

Rosser, Robert S.↗
NASA NTRS1987

Analyzing Feedback Control Systems

Interactive controls analysis (INCA) program developed to provide user-friendly environment for design and analysis of linear control systems, primarily feedback control. Designed for use with both small- and large-order systems. Using interactive-graphics capability, INCA user quickly plots root locus, frequency response, or time response of either continuous-time system or sampled-data system. Configuration and parameters easily changed, allowing user to design compensation networks and perform sensitivity analyses in very convenient manner. Written in Pascal and FORTRAN.

Bauer, Frank H.↗
NASA NTRS1987

Lewis Information Network (LINK): Background and overview

The NASA Lewis Research Center supports many research facilities with many isolated buildings, including wind tunnels, test cells, and research laboratories. These facilities are all located on a 350 acre campus adjacent to the Cleveland Hopkins Airport. The function of NASA-Lewis is to do basic and applied research in all areas of aeronautics, fluid mechanics, materials and structures, space propulsion, and energy systems. These functions require a great variety of remote high speed, high volume data communications for computing and interactive graphic capabilities. In addition, new requirements for local distribution of intercenter video teleconferencing and data communications via satellite have developed. To address these and future communications requirements for the next 15 yrs, a project team was organized to design and implement a new high speed communication system that would handle both data and video information in a common lab-wide Local Area Network. The project team selected cable television broadband coaxial cable technology as the communications medium and first installation of in-ground cable began in the summer of 1980. The Lewis Information Network (LINK) became operational in August 1982 and has become the backbone of all data communications and video.

Schulte, Roger R.↗
DOE OSTI2016

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power.In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation.The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware.This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

JESNET Expert Assistant

The Johnson Space Center Engineering and Science Network (JESNET) supports the JSC Engineering and Space and Life Sciences Directorates by providing a network of interconnected ETHERNETS with a wide diversity of processing and routing nodes throughout the Johnson Space Center and off-site contractor locations. The JESNET provides the primary method of communication between the various computers that support the Directorates. Lockheed Engineering and Sciences Co. (LESC) is tasked with the operation and maintenance of certain primary portions of this network. Since maintenance of this network depends on the timely identification and subsequent repair of problems on the network, the decision was made to develop a JESNET Expert Assistant (JEA) to assist LESC personnel in the operation and maintenance of the JESNET. The JEA is being implemented in CLIPS, DEC Graphical Kernal System (GKS(TM)) and C on a DEC MicroVAX(TM) with VMS(TM).

Hansen, Roger F.↗
NASA NTRS1994

Representing Learning With Graphical Models

Probabilistic graphical models are being used widely in artificial intelligence, for instance, in diagnosis and expert systems, as a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. Their development and use spans several fields including artificial intelligence, decision theory and statistics, and provides an important bridge between these communities. This paper shows by way of example that these models can be extended to machine learning, neural networks and knowledge discovery by representing the notion of a sample on the graphical model. Not only does this allow a flexible variety of learning problems to be represented, it also provides the means for representing the goal of learning and opens the way for the automatic development of learning algorithms from specifications.

Buntine, Wray L.↗
NASA NTRSDate not supplied

Enabling Interoperability in Earth System Digital Twins (ESDT): Integrating Observations, Models, and AI for Actionable Insights Through NASA'S Intelligent Systems Technology Program

NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.

Earth System↗
NASA NTRS2003

Designing Facilities for Collaborative Operations

A methodology for designing operational facilities for collaboration by multiple experts has begun to take shape as an outgrowth of a project to design such facilities for scientific operations of the planned 2003 Mars Exploration Rover (MER) mission. The methodology could also be applicable to the design of military "situation rooms" and other facilities for terrestrial missions. It was recognized in this project that modern mission operations depend heavily upon the collaborative use of computers. It was further recognized that tests have shown that layout of a facility exerts a dramatic effect on the efficiency and endurance of the operations staff. The facility designs (for example, see figure) and the methodology developed during the project reflect this recognition. One element of the methodology is a metric, called effective capacity, that was created for use in evaluating proposed MER operational facilities and may also be useful for evaluating other collaboration spaces, including meeting rooms and military situation rooms. The effective capacity of a facility is defined as the number of people in the facility who can be meaningfully engaged in its operations. A person is considered to be meaningfully engaged if the person can (1) see, hear, and communicate with everyone else present; (2) see the material under discussion (typically data on a piece of paper, computer monitor, or projection screen); and (3) provide input to the product under development by the group. The effective capacity of a facility is less than the number of people that can physically fit in the facility. For example, a typical office that contains a desktop computer has an effective capacity of .4, while a small conference room that contains a projection screen has an effective capacity of around 10. Little or no benefit would be derived from allowing the number of persons in an operational facility to exceed its effective capacity: At best, the operations staff would be underutilized; at worst, operational performance would deteriorate. Elements of this methodology were applied to the design of three operations facilities for a series of rover field tests. These tests were observed by human-factors researchers and their conclusions are being used to refine and extend the methodology to be used in the final design of the MER operations facility. Further work is underway to evaluate the use of personal digital assistant (PDA) units as portable input interfaces and communication devices in future mission operations facilities. A PDA equipped for wireless communication and Ethernet, Bluetooth, or another networking technology would cost less than a complete computer system, and would enable a collaborator to communicate electronically with computers and with other collaborators while moving freely within the virtual environment created by a shared immersive graphical display.

Norris, Jeffrey↗