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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Generation After Next Propulsor Research: Robust Design for Embedded Engine Systems

The National Aeronautics and Space Administration, United Technologies Research Center and Virginia Polytechnic and State University have contracted to pursue multi-disciplinary research into boundary layer ingesting (BLI) propulsors for generation after next environmentally responsible subsonic fixed wing aircraft. This Robust Design for Embedded Engine Systems project first conducted a high-level vehicle system study based on a large commercial transport class hybrid wing body aircraft, which determined that a 3 to 5 percent reduction in fuel burn could be achieved over a 7,500 nanometer mission. Both pylon-mounted baseline and BLI propulsion systems were based on a low-pressure-ratio fan (1.35) in an ultra-high-bypass ratio engine (16), consistent with the next generation of advanced commercial turbofans. An optimized, coupled BLI inlet and fan system was subsequently designed to achieve performance targets identified in the system study. The resulting system possesses an inlet with total pressure losses less than 0.5%, and a fan stage with an efficiency debit of less than 1.5 percent relative to the pylon-mounted, clean-inflow baseline. The subject research project has identified tools and methodologies necessary for the design of next-generation, highly-airframe-integrated propulsion systems. These tools will be validated in future large-scale testing of the BLI inlet / fan system in NASA's 8 foot x 6 foot transonic wind tunnel. In addition, fan unsteady response to screen-generated total pressure distortion is being characterized experimentally in a JT15D engine test rig. These data will document engine sensitivities to distortion magnitude and spatial distribution, providing early insight into key physical processes that will control BLI propulsor design.

Arend, David J.↗
DOE OSTI2019

Implementation and Evaluation of IEC 61850-based Distributed Control System for Microgrid Applications

This paper presents a resilient, modular and scalable distributed control architecture for microgrid systems which uses distributed agents controlling individual loads, network switches, generators or storage devices to provide intelligent power management and efficient microgrid operation. The communication between the distributed agents is realized by implementing IEC 61850 data models and GOOSE messages. The data models include state representation, measurements at various points, and dispatch requests for reliable operation of microgrid control system. System resiliency is enhanced through implementing distributed agent architecture with no single point of failure.

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DOE OSTI2022

Locating underground features with seismic data processing

Methods are presented for determining the location of underground features (e.g., CO 2 ). One method includes capturing, by sensors distributed throughout a region, seismic traces associated with seismic signals generated by a seismic source. For multiple sensors, active noise is identified or passive noise is measured within each seismic trace and values for attributes associated with the active or passive noise are determined. Further, an unsupervised machine-learning model, based on the values of the attributes, is utilized to determine noise characteristics for multiple sensors. The sensors are grouped in clusters based on the noise characteristics for each sensor. For multiple clusters, a noise filter is created based on the noise characteristics of the sensors in the cluster, and the noise filter of the cluster is applied, for multiple sensors, to the seismic traces of the sensor. Additionally, the filtered seismic traces are analyzed to determine a location of CO 2 underground.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Distributed Intrusion Detection System using Semantic-based Rules for SCADA in Smart Grid

Cyber-physical system (CPS) security for the smart grid enables secure communication for the SCADA and wide-area measurement system data. Power utilities world-wide use various SCADA protocols, namely DNP3, Modbus, and IEC 61850, for the data exchanges across substation field devices, remote terminal units (RTUs), and control center applications. Adversaries may exploit compromised SCADA protocols for the reconnaissance, data exfiltration, vulnerability assessment, and injection of stealthy cyberattacks to affect power system operation. In this paper, we propose an efficient algorithm to generate robust rule sets. We integrate the rule sets into an intrusion detection system (IDS), which continuously monitors the DNP3 data traffic at a substation network and detects intrusions and anomalies in real-time. To enable CPS-aware wide-area situational awareness, we integrated the methodology into an open-source distributed-IDS (D-IDS) framework. The D-IDS facilitates central monitoring of the detected anomalies from the geographically distributed substations and to the control center. The proposed algorithm provides an optimal solution to detect network intrusions and abnormal behavior. Different types of IDS rules based on packet payload, packet flow, and time threshold are generated. Further, IDS testing and evaluation is performed with a set of rules in different sequences. The detection time is measured for different IDS rules, and the results are plotted. All the experiments are conducted at Power Cyber Lab, Iowa State University, for multiple power grid models. After successful testing and evaluation, knowledge and implementation are transferred to field deployment.

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NASA NTRS1991

EMASS (trademark): An expandable solution for NASA space data storage needs

The data acquisition, distribution, processing, and archiving requirements of NASA and other U.S. Government data centers present significant data management challenges that must be met in the 1990's. The Earth Observing System (EOS) project alone is expected to generate daily data volumes greater than 2 Terabytes (2 x 10(exp 12) Bytes). As the scientific community makes use of this data, their work will result in larger, increasingly complex data sets to be further exploited and managed. The challenge for data storage systems is to satisfy the initial data management requirements with cost effective solutions that provide for planned growth. The expendable architecture of the E-Systems Modular Automated Storage System (EMASS(TM)), a mass storage system which is designed to support NASA's data capture, storage, distribution, and management requirements into the 21st century is described.

Peterson, Anthony L.↗
NASA NTRS1987

Geometric description and grid generation for space vehicles

The understanding and analysis of three dimensional fluid behavior around reentry vehicles, like the Space Shuttle Orbiter, is of considerable importance for flight applications. Vehicle designers need pressure, heat transfer, and shear distributions to support design activities. Traditionally these have been obtained by experimental data. Significant reduction in design costs can be obtained if the calculated results based on finite difference procedures are used to verify and supplement experimental results. Computing the flow field over Space Shuttle Orbiter, which has complex cross sectional geometries is a major problem. A first step in this direction is development of body definition and grid generation procedure. In this project a digitized body definition and grid generation procedure, developed earlier at JSC, is modified and applied to the Shuttle Orbiter X24C-10D. Two dimensional grid and body definition for various sections of the space vehicle is obtained using the differential equation method. This study demonstrates that this geometry and grid generation procedure can be applied to other aerospace vehicles such as aerospaceplanes.

Krishna, Madakasira G.↗
DOE OSTI2022

Toward designing effective exascale scientific computing workflows: experiences and best practices

Many fields within scientific computing have embraced advances in big-data analysis and machine learning, which often requires the deployment of large, distributed and complicated workflows that may combine training neural networks, performing simulations, running inference, and performing database queries and data analysis in asynchronous, parallel and pipelined execution frameworks. Such a shift has brought into focus the need for scalable, efficient workflow management solutions with reproducibility, error and provenance handling, traceability, and checkpoint-restart capabilities, among other needs. Here, we discuss challenges and best-practices for deploying exascale-generation computational science workflows on resources at the Oak Ridge Leadership Computing Facility (OLCF). We present our experiences with large-scale deployment of distributed workflows on the Summit supercomputer, including for bioinformatics and computational biophysics, materials science, and deep learning model optimization. We also present problems and solutions created by working within a Python-centric software base on traditional HPC systems, and discuss steps that will be required before the convergence of HPC, AI, and data science can be fully realized. Our results point to a wealth of exciting new possibilities for harnessing this convergence to tackle new scientific challenges.

Coletti, Mark↗
NASA NTRS2016

The Matsu Wheel: A Cloud-Based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for cloud-based processing of Earth satellite imagery with practical applications to aid in natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework allows batches of analytics, scanning for new data, to be applied to data as it flows in. In the Matsu Wheel, the data only need to be accessed and preprocessed once, regardless of the number or types of analytics, which can easily be slotted into the existing framework. The Matsu Wheel system provides a significantly more efficient use of computational resources over alternative methods when the data are large, have high-volume throughput, may require heavy preprocessing, and are typically used for many types of analysis. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The result products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.

0↗
DOE OSTI2020

Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices

Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein graphs for downstream analysis, functional annotation, and gene location. Performance and scalability of protein similarity search have proven to be a bottleneck in many bioinformatics pipelines due to increase in cheap and abundant sequencing data. This work presents a new distributed-memory software PASTIS. PASTIS relies on sparse matrix computations for efficient identification of possibly similar proteins. We use distributed sparse matrices for scalability and show that the sparse matrix infrastructure is a great fit for protein similarity search when coupled with a fully-distributed dictionary of sequences that allow remote sequence requests to be fulfilled. Our algorithm incorporates the unique bias in amino acid sequence substitution in search without altering basic sparse matrix model, and in turn, achieves ideal scaling up to millions of protein sequences.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Earth Science Data Archive and Access at the NASA/Goddard Space Flight Center Distributed Active Archive Center (DAAC)

The Goddard Distributed Active Archive Center (DAAC), as an integral part of the Earth Observing System Data and Information System (EOSDIS), is the official source of data for several important earth remote sensing missions. These include the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS) launched in August 1997, the Tropical Rainfall Measuring Mission (TRMM) launched in November 1997, and the Moderate Resolution Imaging Spectroradiometer (MODIS) scheduled for launch in mid 1999 as part of the EOS AM-1 instrumentation package. The data generated from these missions supports a host of users in the hydrological, land biosphere and oceanographic research and applications communities. The volume and nature of the data present unique challenges to an Earth science data archive and distribution system such as the DAAC. The DAAC system receives, archives and distributes a large number of standard data products on a daily basis, including data files that have been reprocessed with updated calibration data or improved analytical algorithms. A World Wide Web interface is provided allowing interactive data selection and automatic data subscriptions as distribution options. The DAAC also creates customized and value-added data products, which allow additional user flexibility and reduced data volume. Another significant part of our overall mission is to provide ancillary data support services and archive support for worldwide field campaigns designed to validate the results from the various satellite-derived measurements. In addition to direct data services, accompanying documentation, WWW links to related resources, support for EOSDIS data formats, and informed response to inquiries are routinely provided to users. The current GDAAC WWW search and order system is being restructured to provide users with a simplified, hierarchical access to data. Data Browsers have been developed for several data sets to aid users in ordering data. These Browsers allow users to specify spatial, temporal, and other parameter criteria in searching for and previewing data.

Leptoukh, Gregory↗
NASA NTRS1992

EMASS (tm): An expandable solution for NASA space data storage needs

The data acquisition, distribution, processing, and archiving requirements of NASA and other U.S. Government data centers present significant data management challenges that must be met in the 1990's. The Earth Observing System (EOS) project alone is expected to generate daily data volumes greater than 2 Terabytes (2(10)(exp 12) Bytes). As the scientific community makes use of this data their work product will result in larger, increasingly complex data sets to be further exploited and managed. The challenge for data storage systems is to satisfy the initial data management requirements with cost effective solutions that provide for planned growth. This paper describes the expandable architecture of the E-Systems Modular Automated Storage System (EMASS (TM)), a mass storage system which is designed to support NASA's data capture, storage, distribution, and management requirements into the 21st century.

Peterson, Anthony L.↗
NASA NTRS1990

Perpendicular heating of electrons by upper hybrid waves generated by a ring distribution

Satellite observations of electron conical distributions with enhanced fluxes just outside the loss cone suggest that telectrons have been heated perpendicularly to the magnetic field in the mid-altitude polar magnetosphere. To understand electron conical distributions, plasma simulations are conducted to examine an upper hybrid wave instability of a ring electron distribution perpendicular to the magnetic field in a cold electron background. The simulations indicate that both the cold and ring distributions are heated perpendicularly during the saturation stage. From the plasma data, a ring distribution can be identified as a trapped distribution function with an enhancement near 90-deg pitch angle in the phase space density plot. It is suggested that the ring distribution might provide an additional free energy source for generating upper hybrid waves associated with electron conical events.

Lin, C. S.↗
DOE OSTI2020

Mapping valence electron distributions with multipole density formalism using 4D-STEM

Recent advancement in aberration correction and detector technology opened a door to various applications using 4D-STEM, which yields a diffraction pattern for each scanning position within a crystal unit-cell in scanning transmission electron microscopy (STEM) and generates incredible amounts of data in momentum space. Currently 4D-STEM analysis relies on the center-of-mass of the diffraction patterns in electric field and charge density mapping. It only derives the total projected charge density and is limited to phase objects, e.g. extremely thin samples. Here, we propose a new analytical method to accurately map aspherical valence electron distributions with atom-centered multipolar functions formalism using the whole 4D-STEM dataset. We demonstrate that, with the full dynamical calculations for various sample thicknesses, the method is sensitive not only to the miniscule charge transfer, but also to the atomic site symmetry and aspherical electron orbitals. The process of the refinement is much more robust and reliable than quantitative convergent beam electron diffraction.

4D-STEM↗
NASA NTRS1993

Characterization of cavity flow fields using pressure data obtained in the Langley 0.3-Meter Transonic Cryogenic Tunnel

Static and fluctuating pressure distributions were obtained along the floor of a rectangular-box cavity in an experiment performed in the LaRC 0.3-Meter Transonic Cryogenic Tunnel. The cavity studied was 11.25 in. long and 2.50 in. wide with a variable height to obtain length-to-height ratios of 4.4, 6.7, 12.67, and 20.0. The data presented herein were obtained for yaw angles of 0 deg and 15 deg over a Mach number range from 0.2 to 0.9 at a Reynolds number of 30 x 10(exp 6) per ft with a boundary-layer thickness of approximately 0.5 in. The results indicated that open and transitional-open cavity flow supports tone generation at subsonic and transonic speeds at Mach numbers of 0.6 and above. Further, pressure fluctuations associated with acoustic tone generation can be sustained when static pressure distributions indicate that transitional-closed and closed flow fields exist in the cavity. Cavities that support tone generation at 0 deg yaw also supported tone generation at 15 deg yaw when the flow became transitional-closed. For the latter cases, a reduction in tone amplitude was observed. Both static and fluctuating pressure data must be considered when defining cavity flow fields, and the flow models need to be refined to accommodate steady and unsteady flows.

Tracy, M. B.↗
NASA NTRS1993

Electromagnetic ion cyclotron waves stimulated by modest magnetospheric compressions

AMPTE/CCE magnetic field and particle data are used to test the suggestion that increased hot proton temperature anisotropy resulting from convection during magnetospheric compression is responsible for the enhancement in Pc 1 emission via generation of electromagnetic ion cyclotron (EMIC) waves in the dayside outer equatorial magnetosphere. The relative increase in magnetic field is used to gauge the strength of the compression, and an image dipole model is used to estimate the motion of the plasma during compression. Proton data are used to analyze the evolution of the proton distribution and the corresponding changes in EMIC wave activity expected during the compression. It is suggested that enhancements in dynamic pressure pump the energetic proton distributions in the outer magnetosphere, driving EMIC waves. Waves are expected to be generated most readily close to the magnetopause, and transient pressure pulses may be associated with bursts of EMIC waves, which would be observed on the ground in association with ionospheric transient signatures.

Anderson, B. J.↗
NASA NTRS1994

A data and information system for processing, archival, and distribution of data for global change research

Work on this project was focused on information management techniques for Marshall Space Flight Center's EOSDIS Version 0 Distributed Active Archive Center (DAAC). The centerpiece of this effort has been participation in EOSDIS catalog interoperability research, the result of which is a distributed Information Management System (IMS) allowing the user to query the inventories of all the DAAC's from a single user interface. UAH has provided the MSFC DAAC database server for the distributed IMS, and has contributed to definition and development of the browse image display capabilities in the system's user interface. Another important area of research has been in generating value-based metadata through data mining. In addition, information management applications for local inventory and archive management, and for tracking data orders were provided.

Graves, Sara J.↗
NASA NTRS1998

Measurements of Heat Transfer, Flow, and Pressures in a Simulated Turbine Blade Internal Cooling Passage

An experimental study was made to obtain quantitative information on heat transfer, flow, and pressure distribution in a branched duct test section that had several significant features of an internal cooling passage of a turbine blade. The objective of this study was to generate a set of experimental data that could be used for validation of computer codes that would be used to model internal cooling. Surface heat transfer coefficients and entrance flow conditions were measured at nominal entrance Reynolds numbers of 45,000, 335,000, and 726,000. Heat transfer data were obtained by using a steady-state technique in which an Inconel heater sheet is attached to the surface and coated with liquid crystals. Visual and quantitative flow-field data from particle image velocimetry measurements for a plane at midchannel height for a Reynolds number of 45,000 were also obtained. The flow was seeded with polystyrene particles and illuminated by a laser light sheet. Pressure distribution measurements were made both on the surface with discrete holes and in the flow field with a total pressure probe. The flow-field measurements yielded flow-field velocities at selected locations. A relatively new method, pressure sensitive paint, was also used to measure surface pressure distribution. The pressure paint data obtained at Reynolds numbers of 335,000 and 726,000 compared well with the more standard method of measuring pressures by using discrete holes.

Russell, Louis M.↗
DOE OSTI2025

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

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