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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

A Testbed for Geomagnetic Data Assimilation

Geomagnetic data assimilation merges past and present-day observations of the Earth’s mag-netic field with numerical geodynamo models and the results are used to initialize forecasts.We present a new ‘proxy model’ that can be used to test, or rapidly prototype, numerical tech-niques for geomagnetic data assimilation. The basic idea for constructing a proxy is to capturethe conceptual difficulties one encounters when assimilating observations into high-resolution,3-D geodynamo simulations, but at a much lower computational cost. The framework of usingproxy models as ‘gate-keepers’ for numerical methods that could/should be considered formore extensive testing on operational models has proven useful in numerical weather predic-tion, where advances in data assimilation and, hence, improved forecast skill, are at least inpart enabled by the common use of a wide range of proxy models. We also present a largeset of systematic data assimilation experiments with the proxy to reveal the importance oflocalization and inflation in geomagnetic data assimilation.

K Gwirtz↗
DOE OSTI2022

Assessment of Outliers in Alloy Datasets Using Unsupervised Techniques

We report advancements in data analytics techniques have enabled complex, disparate datasets to be leveraged for alloy design. Identifying outliers in a dataset can reduce noise, identify erroneous and/or anomalous records, prevent overfitting, and improve model assessment and optimization. In this work, two alloy datasets (9-12% Cr ferritic martensitic steels, and austenitic stainless steels) have been assessed for outliers using unsupervised techniques and supplemented with domain knowledge. Principal component analysis and k-means clustering were applied to the data, and points were assessed as outliers based on their distance away from other points in the cluster and from other points in the dataset. The outlier characteristics were investigated to determine both cluster-specific and overall trends in the properties of the outlier points. The approach demonstrated here is extensible to other alloy datasets for outlier identification and evaluation to improve the reliability of machine learning and modeling predictions for advanced alloy design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications (Full Report)

The Modular Autonomous Research System (MARS) was developed to address the pressing need for faster, more reliable, and more adaptable scientific discovery. Traditional experimentation is limited by manual labor, long cycle times, and fragmented data streams, which constrain the ability to explore complex chemical and materials design spaces. To overcome these limitations, we created an integrated, modular platform that combines laboratory robotics, diverse measurement instruments, and a central data infrastructure with artificial intelligence–driven decision-making. The system links liquid handling robots, robotic arms, and optical plate readers into a closed loop where experiments are executed automatically, data is analyzed in real time, and subsequent experimental conditions are adaptively chosen to maximize information gain. Over the course of the project, MARS was validated on two primary test cases—spectroscopic metal–ligand binding assays and peptide-directed mineralization—which highlighted the system’s ability to handle uncertainty and variability in experimental measurements. To further demonstrate modularity and extensibility, we also established additional testbeds in electrochemistry for catalyst discovery and electrolyte formulation for advanced batteries. The results show that MARS can reliably conduct autonomous campaigns with minimal human intervention, adapt to distinct scientific domains, and provide a scalable model for future self-driving laboratories. This work establishes new capabilities for modular, uncertainty-aware automation and directly supports the need for advanced, data-driven research platforms capable of accelerating discovery across a wide range of scientific and national security missions.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1974

An integrated PCM data system for full scale aeronautics testing

An integrated PCM data system is being developed at Ames Research Center to gather test data on advanced STOL propulsive lift, VTOL, rotary wing, and V/STOL control systems concepts as they pass through wind-tunnel, test-stand, flight-simulator and flight-test phases. Identical airborne signal conditioning and PCM encoding is used on test aircraft and wind tunnel models. An 80,000 word/second PCM installation will be the first all PCM-instrumented rotary wing development project. The system uses both dedicated and time-shared computers for fast data analysis with maximum use of resources. This system development shows one way to bring separate data user groups together over a common data base, while sharing computing resources for minimum cost.-

Reynolds, D. R.↗
NASA NTRS2011

Development of a Web-Based Visualization Platform for Climate Research Using Google Earth

Recently, it has become easier to access climate data from satellites, ground measurements, and models from various data centers, However, searching. accessing, and prc(essing heterogeneous data from different sources are very tim -consuming tasks. There is lack of a comprehensive visual platform to acquire distributed and heterogeneous scientific data and to render processed images from a single accessing point for climate studies. This paper. documents the design and implementation of a Web-based visual, interoperable, and scalable platform that is able to access climatological fields from models, satellites, and ground stations from a number of data sources using Google Earth (GE) as a common graphical interface. The development is based on the TCP/IP protocol and various data sharing open sources, such as OPeNDAP, GDS, Web Processing Service (WPS), and Web Mapping Service (WMS). The visualization capability of integrating various measurements into cE extends dramatically the awareness and visibility of scientific results. Using embedded geographic information in the GE, the designed system improves our understanding of the relationships of different elements in a four dimensional domain. The system enables easy and convenient synergistic research on a virtual platform for professionals and the general public, gr$tly advancing global data sharing and scientific research collaboration.

Sun, Xiaojuan↗
NASA NTRS2018

Web-Based Geospatial Visualization of GPM Data with CesiumJS

Advancements in the capabilities of JavaScript frameworks and web browsing technology have made online visualization of large geospatial datasets such as those coming from precipitation satellites viable. These data benefit from being visualized on and above a three-dimensional surface. The open-source JavaScript framework CesiumJS (http://cesiumjs.org), developed by Analytical Graphics, Inc., leverages the WebGL protocol to do just that. This presentation will describe how CesiumJS has been used in three-dimensional visualization products developed as part of the NASA Precipitation Processing System (PPS) STORM data-order website. Existing methods of interacting with Global Precipitation Measurement (GPM) Mission data primarily focus on two-dimensional static images, whether displaying vertical slices or horizontal surface/height-level maps. These methods limit interactivity with the robust three-dimensional data coming from the GPM core satellite. Integrating the data with CesiumJS in a web-based user interface has allowed us to create the following products. We have linked with the data-order interface an on-the-fly visualization tool for any GPM/partner satellite orbit. A version of this tool also focuses on high-impact weather events. It enables viewing of combined radar and microwave-derived precipitation data on mobile devices and in a way that can be embedded into other websites. We also have used CesiumJS to visualize a method of integrating gridded precipitation data with modeled wind speeds that animates over time. Emphasis in the presentation will be placed on how a variety of technical methods were used to create these tools, and how the flexibility of the CesiumJS framework facilitates creative approaches to interact with the data.

3D↗
DOE OSTI2022

Results of Oklo Diffusion Couple Experiments

Through the Gateway for Accelerated Innovation in Nuclear (GAIN) initiative, the U.S. Department of Energy funded a GAIN Nuclear Energy Voucher to perform out-of-pile experiments related to the fuel design for Oklo Inc., headquartered in Santa Clara, CA. GAIN’s goal is to accelerate the innovation and application of advanced nuclear technologies, and voucher recipients do not receive direct financial awards but are provided access to national laboratory capabilities at no cost. This report documents the results of diffusion couple experiments performed as part of the GAIN-funded project for Oklo. This work addresses the specific problem of filling the gap in understanding of the fuel-cladding chemical interaction (FCCI) that result from the past focus on a particular fuel design. These knowledge and mechanistic modeling gaps make it difficult to extrapolate from prior fuel performance data to advance an economically competitive fuel design. Specifically, the current state-of-the-art fuel performance modeling cannot separate the entangled effects of temperature ranges, burnups, materials, and geometries.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1987

Boundary layer simulation improvement

The current Boundary Layer Integral Matrix Procedure - Version J (BLIMPJ) is advanced with regard to the previously modeled calculation methods accounting for surface roughness, relaminarization, condensed phase, and thick boundary layer effects. Current analytical limitations will be relaxed to permit more realistic wall and flow conditions and to use appropriate turbulence models with specific emphasis for the projected Orbit Transfer Vehicle (OTV) engine application. The updates will be incorporated into the BLIMPJ computer program, and the modified program will be made operational on the MSFC CRAY computer system. Experimental feasibility studies were conducted to find out how to obtain quality test data with advanced instrumentation for concept verification purposes.

Praharaj, Sarat C.↗
NASA NTRS1993

Development and evaluation of a profile negotiation process for integrating aircraft and air traffic control automation

The development and evaluation of the profile negotiation process (PNP), an interactive process between an aircraft and air traffic control (ATC) that integrates airborne and ground-based automation capabilities to determine conflict-free trajectories that are as close to an aircraft's preference as possible, are described. The PNP was evaluated in a real-time simulation experiment conducted jointly by NASA's Ames and Langley Research Centers. The Ames Center/TRACON Automation System (CTAS) was used to support the ATC environment, and the Langley Transport Systems Research Vehicle (TSRV) piloted cab was used to simulate a 4D Flight Management System (FMS) capable aircraft. Both systems were connected in real time by way of voice and data lines; digital datalink communications capability was developed and evaluated as a means of supporting the air/ground exchange of trajectory data. The controllers were able to consistently and effectively negotiate nominally conflict-free vertical profiles with the 4D-equipped aircraft. The actual profiles flown were substantially closer to the aircraft's preference than would have been possible without the PNP. However, there was a strong consensus among the pilots and controllers that the level of automation of the PNP should be increased to make the process more transparent. The experiment demonstrated the importance of an aircraft's ability to accurately execute a negotiated profile as well as the need for digital datalink to support advanced air/ground data communications. The concept of trajectory space is proposed as a comprehensive approach for coupling the processes of trajectory planning and tracking to allow maximum pilot discretion in meeting ATC constraints.

Green, Steven M.↗
NASA NTRS2017

Creative Analytics of Mission Ops Event Messages

Historically, tremendous effort has been put into processing and displaying mission health and safety telemetry data; and relatively little attention has been paid to extracting information from missions time-tagged event log messages. Todays missions may log tens of thousands of messages per day and the numbers are expected to dramatically increase as satellite fleets and constellations are launched, as security monitoring continues to evolve, and as the overall complexity of ground system operations increases. The logs may contain information about orbital events, scheduled and actual observations, device status and anomalies, when operators were logged on, when commands were resent, when there were data drop outs or system failures, and much much more. When dealing with distributed space missions or operational fleets, it becomes even more important to systematically analyze this data. Several advanced information systems technologies make it appropriate to now develop analytic capabilities which can increase mission situational awareness, reduce mission risk, enable better event-driven automation and cross-mission collaborations, and lead to improved operations strategies: Industry Standard for Log Messages. The Object Management Group (OMG) Space Domain Task Force (SDTF) standards organization is in the process of creating a formal standard for industry for event log messages. The format is based on work at NASA GSFC. Open System Architectures. The DoD, NASA, and others are moving towards common open system architectures for mission ground data systems based on work at NASA GSFC with the full support of the commercial product industry and major integration contractors. Text Analytics. A specific area of data analytics which applies statistical, linguistic, and structural techniques to extract and classify information from textual sources. This presentation describes work now underway at NASA to increase situational awareness through the collection of non-telemetry mission operations information into a common log format and then providing display and analytics tools to provide in-depth assessment of the log contents. The work includes: Common interface formats for acquiring time-tagged text messages Conversion of common files for schedules, orbital events, and stored commands to the common log format Innovative displays to depict thousands of messages on a single display Structured English text queries against the log message data store, extensible to a more mature natural language query capability Goal of speech-to-text and text-to-speech additions to create a personal mission operations assistant to aid on-console operations. A wide variety of planned uses identified by the mission operations teams will be discussed.

events↗
NASA NTRSDate not supplied

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system. The industry has experienced a steady decrease in fatalities over the years. This can be contributed to both improved flight critical systems with redundant hardware and software protections as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main practice for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave with the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety creating labels for the data requires huge amount of efforts and is largely expensive. As a result, in this article, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach (as an upper bound) as well as an supervised clustering based on K-Means (as a lower bound) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Milad Memarzadeh↗
NASA NTRS2020

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This can be attributed to both improved flight critical systems with redundant hardware and software protections, as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main approach for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave within the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety, creating labels for the data requires huge amount of effort and is largely impractical. To address this challenge, we developed a Convolutional Variational Auto-Encoder (CVAE), which is an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach as well as unsupervised clustering-based approach using KMeans++ and kernel-based approach using One-Class Support Vector Machine (OC-SVM) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Memarzadeh, Milad↗
NASA NTRS2016

HIAD on ULA (HULA) Orbital Reentry Flight Experiment Concept

This paper describes a proposed orbital velocity reentry flight test of a Hypersonic Inflatable Aerodynamic Decelerator (HIAD). The flight test builds upon ground development activities that continue to advance the materials, design, and manufacturing techniques for the inflatable structure and flexible thermal protection system (F-TPS) that comprise the inflatable heat shield. While certain aspects of material and system performance can be assessed using a variety of ground testing capabilities, only orbital velocity energy on a trajectory through the gradient density of the atmosphere can impart the combined aerodynamic and aeroheating design environments in real time. To achieve this at limited cost, the HIAD would be delivered to a spin-stabilized entry trajectory as a secondary payload on the Centaur stage of a United Launch Alliance (ULA) Atlas V launch vehicle. Initial trajectory studies indicate that the combination of launch vehicle capability and achievable reentry vehicle ballistic numbers make this a strategic opportunity for technology development. This 4 to 6 meter diameter scale aeroshell flight, referred to as HIAD on ULA (HULA), would also contribute to ULA asset recovery development. ULA has proposed that a HIAD be utilized as part of the Sensible, Modular, Autonomous Return Technology (SMART) initiative to enable recovery of the Vulcan launch vehicle booster main engines [1], including a Mid-Air Recovery (MAR) to gently return these assets for reuse. Whereas HULA will attain valuable aerothermal and structural response data toward advancing HIAD technology, it may also provide a largest-to-date scaled flight test of the MAR operation, which in turn would allow the examination of a nearly pristine post-entry aeroshell. By utilizing infrared camera imaging, HULA will also attain aft-side thermal response data, enhancing understanding of the aft side aerothermal environment, an area of high uncertainty. The aeroshell inflation will utilize a heritage design compressed gas system to minimize development costs. The data will be captured to both an onboard recorder and a recorder that is jettisoned and recovered separately from the reentry vehicle to mitigate risk. This paper provides an overview, including the architecture and flight concept of operations, for the proposed HULA flight experiment.

Dinonno, J. M.↗
NASA NTRS2002

Technology Assessments within NASA's Integrated Technology Assessment Center

NASA's Advanced Space Transportation Program (ASTP) founded the Integrated Technology Assessment Center (ITAC) to provide a comprehensive, systematic approach to identify long-term technology needs, to quantify payoffs for technology investments, and to assess the progress of ASTP-sponsored technology programs in the hypersonics/Earth-to-orbit area. To accomplish these goals, the ITAC has assembled an experienced team representing a broad sector of the aerospace community and developed a systematic assessment process complete with supporting tools. In the ITAC approach, concepts for transportation systems are first selected based on relevance to the ASTP. Models of these concepts are then developed and data on advanced technologies are collected. Projections of key technology characteristics with respect to the specific concepts of interest are made. Both the models and technology projections are then fed into the ITAC's probabilistic systems analysis framework. The probabilistic outputs are weighed against metrics of interest to ASTP and a multivariate decision making process is used to provide inputs for technology prioritization within the ASTP. At present, the ITAC program is working to evaluate a variety of technologies for three two-stage hypersonic vehicle concepts. Concepts include an all rocket, vertical take off-horizontal landing (VTHL) system, a horizontal take-off-horizontal landing (HTHL) RBCC-propelled first stage/all rocket second stage system, and an HTHL turbine-based first stage/all rocket second stage system. This paper will provide a status update of the ITAC program including current results and plans.

Taylor, J. L.↗
NASA NTRS2002

Technology Assessments within NASA's Integrated Technology Assessment Center

NASA's Advanced Space Transportation Program (ASTP) founded the Integrated Technology Assessment Center (ITAC) to provide a comprehensive, systematic approach to identify long-term technology needs, to quantify payoffs for technology investments, and to assess the progress of ASTP-sponsored technology programs in the hypersonics/Earth-to-orbit area. To accomplish these goals, the ITAC has assembled an experienced team representing a broad sector of the aerospace community and developed a systematic assessment process complete with supporting tools. In the ITAC approach, concepts for transportation systems are first selected based on relevance to the ASTP. Models of these concepts are then developed and data on advanced technologies are collected. Projections of key technology characteristics with respect to the specific concepts of interest are made. Both the models and technology projections are then fed into the ITAC's probabilistic systems analysis framework. The probabilistic outputs are weighed against metrics of interest to ASTP and a multivariate decision making process is used to provide inputs for technology prioritization within the ASTP. At present, the ITAC program is working to evaluate a variety of technologies for three two-stage hypersonic vehicle concepts. Concepts include an all rocket, vertical take off-horizontal landing (VTHL) system, a horizontal takeoff-horizontal landing (HTHL) RBCC-propelled first stage/all rocket second stage system, and an HTHL turbine-based first stage/all rocket second stage system. This paper will provide a status update of the ITAC program including current results and plans.

Taylor, J. L.↗
NASA NTRSDate not supplied

Generation of Land Surface Reflectance with Combined Geo-KOMPSAT-2A AMI and Himawari 8 AHI Observations

The latest generation of geostationary satellites has opened a new era of Earth observations with unprecedented spatiotemporal resolution and spectral range. Together with GOES 16/17 ABI, FY4-A AGRI, and Himawari-8 AHI, a new Korean geostationary satellite (Geo-KOMPSAT-2A AMI) has operationally collected a full-disk image in 16 channels every ten minutes since July 2019, allowing diurnal land surface monitoring over a large proportion of Asia and all of Oceania. Retrieving accurate surface reflectance (SR) over land from GK-2A/AMI is a challenging but high priority objective. One of the challenges is the absence of a spectral band in the 2.2 m SWIR range from AMI, which is required by many atmospheric correction algorithms to retrieve atmospheric aerosol properties. To remedy this issue, we adopt a strategy that combines concurrent GK-2A/AMI and Himawari 8/AHI observations in order to derive AMI SR. We have adapted the NASA Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to process the data stream from Himawari 8/AHI. The advantages of the MAIAC algorithm is its capability to exploit the high temporal frequency and varying illumination geometry of the geostationary data for advanced cloud/snow detection, aerosol retrieval, and characterization of surface bidirectional reflectance properties. Leveraging the similarities of spectral bands and the sun-target-sensor geometry between AMI and AHI, we are able to create denser time series of observations and enhanced BRDF samples over most of the spatial coverage of AMI (and AHI). The combined stereo-type observations not only help derive SR for AMI but also enhance retrievals of the corresponding AHI surface products. We evaluate the resulting AMI SR using ground (AERONET) observations and corresponding MODIS products. Further, we discuss potential challenges in utilizing the geostationary satellite data for land surface monitoring.

geostationary satellite↗
NASA NTRS2004

Progress in High Power Density SOFC Material Development for Aerospace Applications

Solid oxide fuel cell (SOFC) systems for aircraft applications require order of magnitude increase in specific power density and long life under aircraft operating conditions. Advanced SOFC materials and fabrication processes are being developed at NASA GRC to increase specific power density and durability of SOFC cell and stack. Initial research efforts for increasing specific power density are directed toward increasing the operating temperature for the SOFC system and reducing the weight of the stack. While significant research is underway to develop anode supported SOFC system operating at temperatures in the range of 650 - 850 C for ground power generation applications, such temperatures may not yield the power densities required for aircraft applications. For electrode-supported cells, SOFC stacks with power densities greater than 1.0 W/sq cm are favorable at temperatures in excess of 900 C. The performance of various commercial and developmental anode supported cells is currently being evaluated in the temperature range of 900 to 1000 C to assess the performance gains and materials reliability. The results from these studies will be presented. Since metal interconnects developed for lower temperature operation are not practical at these high temperatures, advanced perovskite based ceramic interconnects with high electronic conductivity and lower sintering temperatures are being developed. Another option for increasing specific power density of SOFC stacks is to decrease the stack weight. Since the interconnect contributes to a significant portion of the stack weight, considerable weight benefits can be derived by decreasing its thickness. Eliminating the gas channels in the interconnect by engineering the pore structure in both anode and cathode can offer significant reduction in thickness of the ceramic interconnect material. New solid oxide fuel cells are being developed with porous engineered electrode supported structures with a 10 - 20 micron thin electrolyte. The performance data for advanced SOFC cells with engineered porosity in both electrodes will be presented.

Cable, Thomas L.↗
DOE OSTI2021

Displacement Analysis of Geothermal Field Based on PSInSAR And SOM Clustering Algorithms A Case Study of Brady Field, Nevada—USA

The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is –19 mm/yr with an average value of –6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field.

54 ENVIRONMENTAL SCIENCES↗