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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Open Data Integration (ODIN): A Concurrent, Distributed Message-Based Architecture and Framework for Disaster Response

The Runtime for Airspace Concept Evaluation (RACE) is an open-source software architecture and framework to build configurable, highly concurrent and distributed message-based systems that offer scalable, low-latency performance on commodity hardware. RACE was used in commercial aviation applications to rapidly build systems that span several machines (including synchronized displays), interface existing hardware simulators and other live data feeds, and incorporate sophisticated visualization components such as NASA WorldWind. These RACE applications validated elements of the FAA’s System Wide Information Management (SWIM) Program, handling up to 1000 messages/sec from diverse sources (SFDPS, TFM-DATA, TAIS, ASDE-X, ITWS and local ADS) for 4,500 simultaneous flights tracked in the next-generation air transportation system’s digital backbone. We have since generalized RACE to support Open Data Integration (ODIN) applications outside aviation. Systems built with RACE/ODIN can be deployed in the field, on commodity hardware, and operate with limited or intermittent connectivity to the outside world. Our primary use case is a web-server with local/persistent data storage that runs within and only serves the stakeholder network (e.g. an incident command post). We are tailoring the RACE/ODIN system to support wildland fire management for the upcoming NASA Wildland Fire Safety Demonstration Series. RACE-ODIN is under consideration for application in the Scalable Traffic Management for Emergency Response Operations project, or STEReO, which aims to create a system that can be deployed during emergencies, to coordinate multiple elements of disaster response. Such data sources predominantly come from existing services on the internet (e.g. weather and satellite data, imported from so called "edge servers") but can also include dynamic (real-time) data from computer simulations and within the stakeholder network (such as aircraft and personnel tracking information). We will present the architecture and ODIN system demonstration incorporating local data from instrumented power-line towers, interpolated weather data and geospatial data from space-based platforms.

Joseph C Coughlan↗
NASA NTRSDate not supplied

Open Data Integration (ODIN): A Concurrent, Distributed Message-Based Architecture and Framework for Disaster Response

The Runtime for Airspace Concept Evaluation (RACE) is an open-source software architecture and framework to build configurable, highly concurrent and distributed message-based systems that offer scalable, low-latency performance on commodity hardware. RACE was used in commercial aviation applications to rapidly build systems that span several machines (including synchronized displays), interface existing hardware simulators and other live data feeds, and incorporate sophisticated visualization components such as NASA WorldWind. These RACE applications validated elements of the FAA’s System Wide Information Management (SWIM) Program, handling up to 1000 messages/sec from diverse sources (SFDPS, TFM-DATA, TAIS, ASDE-X, ITWS and local ADS) for 4,500 simultaneous flights tracked in the next-generation air transportation system’s digital backbone. We have since generalized RACE to support Open Data Integration (ODIN) applications outside aviation. Systems built with RACE/ODIN can be deployed in the field, on commodity hardware, and operate with limited or intermittent connectivity to the outside world. Our primary use case is a web-server with local/persistent data storage that runs within and only serves the stakeholder network (e.g. an incident command post). We are tailoring the RACE/ODIN system to support wildland fire management for the upcoming NASA Wildland Fire Safety Demonstration Series. RACE-ODIN is under consideration for application in the Scalable Traffic Management for Emergency Response Operations project, or STEReO, which aims to create a system that can be deployed during emergencies, to coordinate multiple elements of disaster response. Such data sources predominantly come from existing services on the internet (e.g. weather and satellite data, imported from so called "edge servers") but can also include dynamic (real-time) data from computer simulations and within the stakeholder network (such as aircraft and personnel tracking information). We will present the architecture and ODIN system demonstration incorporating local data from instrumented power-line towers, interpolated weather data and geospatial data from space-based platforms.

Guillaume P Brat↗
DOE OSTI2024

Evaluation of In-Situ AM Process Monitoring Techniques and Potential for Detecting Process Anomalies and Undesirable Microstructures

The US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program is pursuing rapid qualification of new materials for fabrication of nuclear relevant components using advanced manufacturing techniques. Particular interest is placed on code-qualifying stainless steel (SS) 316H processed by laser powder bed fusion (LPBF) additive manufacturing. A paradigm that incorporates data from in-situ sensing during the printing, ex-situ characterization, and advanced artificial intelligence–based models was established under the Transformation Challenge Reactor (TCR) program to develop a pedigree for each fabricated component that could be tracked from the feedstock to the component’s release for application. Under the TCR program, the Peregrine software was developed as a tool for incorporating the vast amounts of in-situ and ex-situ characterization data collected; all data stored on a rapidly growing digital platform. The digital platform allows for users to link site-specific process anomalies to the macro- and microstructure. The platform will eventually be able to predict component performance, which will be crucial to qualifying materials and components in risk-averse industries such as those supporting and building nuclear reactors. Current in-situ process monitoring techniques that are already integrated with software like Peregrine are advantageous for identifying process anomalies including powder spatter, component edge swelling, recoating-build interactions, and so on. However, additional data are required to fully predict the resulting microstructures needed for identifying relationships to component performance. The rapid cooling rates observed in LPBF are some of the highest of any bulk manufacturing process, resulting in heterogenous microstructures and typically causing anisotropy in mechanical properties. Moreover, evolved residual thermal stresses are high, which can cause severe defects such as delamination or cracking. Therefore, other in-situ monitoring methods are warranted for exploration to measure and map the thermal history, and potentially the stress state, of each build. This report summarizes different in-situ monitoring strategies proposed for LPBF with a focus on the more developed sensor systems. Novel capabilities for measuring melt pool temperatures are also addressed to better inform modeling efforts.

36 MATERIALS SCIENCE↗
NASA NTRS2014

International Space Station Data Collection for Disaster Response

Natural disasters - including such events as tropical storms, earthquakes, floods, volcanic eruptions, and wildfires -effect hundreds of millions of people worldwide, and also cause billions of dollars (USD) in damage to the global economy. Remotely sensed data acquired by orbital sensor systems has emerged as a vital tool to identify the extent of damage resulting from a natural disaster, as well as providing near-real time mapping support to response efforts on the ground and humanitarian aid efforts. The International Space Station (ISS) is a unique terrestrial remote sensing platform for acquiring disaster response imagery. Unlike automated remote-sensing platforms it has a human crew; is equipped with both internal and externally-mounted remote sensing instruments; and has an inclined, low-Earth orbit that provides variable views and lighting (day and night) over 95 percent of the inhabited surface of the Earth. As such, it provides a useful complement to free-flyer based, sun-synchronous sensor systems in higher altitude polar orbits. While several nations have well-developed terrestrial remote sensing programs and assets for data collection, many developing nations do not have ready access to such resources. The International Charter, Space and Major Disasters (also known as the "International Disaster Charter", or IDC; http://www.disasterscharter.org/home) addresses this disparity. It is an agreement between agencies of several countries to provide - on a best-effort basis - remotely sensed data of natural disasters to requesting countries in support of disaster response. The lead US agency for interaction with the IDC is the United States Geological Survey (USGS); when an IDC request or "activation" is received, the USGS notifies the science teams for NASA instruments with targeting information for data collection. In the case of the ISS, the Earth Sciences and Remote Sensing (ESRS) Unit, part of the Astromaterials Research and Exploration Science Directorate and supporting the ISS Program Science Office at NASA's Johnson Space Center, receives notification from the USGS and coordinates targeting and data collection with the NASA ISS sensor teams. If data is collected, it is passed back to the USGS for posting on their Hazards Data Distribution System and made available for download. The ISS International Partners (CSA, ESA, JAXA, Roscosmos/Energia) have their own procedures for independently supporting IDC activations using their assets on ISS, and there is currently no joint coordination with NASA ISS sensor teams. Following completion of ISS assembly, NASA remote sensing assets began collecting IDC response data in May 2012. The initial NASA ISS sensor systems available to respond to IDC activations included the ISS Agricultural Camera (ISSAC), an internal multispectral visible-near infrared wavelength system mounted in the Window Observational Research Facility, or WORF; the Crew Earth Observations (CEO) Facility, where the crew collects imagery through Station windows using off-the-shelf handheld digital visible-wavelength cameras; and the Hyperspectral Imager for the Coastal Oceans (HICO), a visible to near-infrared system mounted externally on the Japan Experiment Module Exposed Facility. The ISSAC completed its primary mission and was removed from the WORF in January 2013. It was replaced by the very high resolution ISS SERVIR Environmental Research and Visualization System (ISERV) Pathfinder, a visible-wavelength digital camera, telescope, and pointing system. Since the start of IDC response by NASA sensors on the ISS in May 2012 and as of this report, there have been eighty IDC activations; NASA sensor systems have collected data for twenty-three of these events. Of the twenty-three successful data collections, five involved 2 or more ISS sensor systems responding to the same event. Data has also been collected by International Partners in response to natural disasters, most notably JAXA and Roscosmos/Energia through the Urugan program. Data collected in response to IDC activations is delivered by the ISS sensor teams to the ESRS for quality review and transfer to the USGS, where it is ingested into the Hazards Data Distribution System, or HDDS (https://hdds.usgs.gov/hdds2/; figure 1). This system allows the local agencies that issued the IDC activation request to review and download data. The data is then used to develop secondary products useful for humanitarian response such as flood maps. As of this report, approximately 1000 images collected by NASA ISS sensor systems have been downloaded from the HDDS, indicating that the ISS has assumed a valuable role in disaster response efforts. The ISS is also a unique platform in that it will have multiple users over its lifetime, and that no single remote sensing system has a permanent internal or external berth. This scheduled turnover provides for development of new remote sensing capabilities relevant to disaster response -as well as both research and applied science-and represents a significant contribution to continuance and enhancement of the NASA mission to investigate changes on our home planet.

Stefanov, William L.↗
NASA NTRS2002

Demonstrating Acquisition of Real-Time Thermal Data over Fires Utilizing UAVs

NASA-Ames Research Center, in collaboration with General Atomics Aeronautical Systems, Inc. has been developing real-time data acquisition and information delivery systems employing uninhabited aerial vehicle (UAV) technology for disaster mitigation and assessment demonstrations. Working in conjunction with the US Forest Service, a disaster community agency responsible for wildfire management and mitigation, we developed a large-scale wildfire demonstration called the First Response Experiment (FIRE). During that experiment in late summer 2001, the participants demonstrated the melding of innovative technologies such as UAV platforms, real-time data processing, and data telemetry for quick analysis of a disaster event. The General Atomics ALTUS UAV, the Airborne Infrared Disaster Assessment System (AIRDAS) and Over-The-Horizon (OTH) satellite data telemetry equipment were employed over a controlled burn to test the feasibility of a disaster monitoring and mitigation platform for hazardous duty. The ALTUS UAV was employed to demonstrate the long duration, altitude, and payload capability of unmanned platforms for acquiring disaster related data. The ALTUS has an operational altitude to 45,000 feet (13,700 in), with a flight duration of twenty-four hours and a payload capacity of over 300 lbs. (148.5 kg). This allows the platform to operate under the conditions that would be necessary for monitoring and mitigating disaster events throughout the Unites States. The four channel AIRDAS data (calibrated thermal infrared digital imagery of the fire event) was sent from the ALTUS UAV via a satellite communications system (NERA transponder and INMARSAT satellite) to a data archive server and an image processing work station at NASA-Ames Research Center, 400 miles away.

Wegener, Steven S.↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Threat Hunt Guide for BESS Environments

The rapid digitalization of the electric grid - driven by the integration of inverter-based resources (IBRs), battery energy storage systems (BESS), and advanced grid control platforms - has significantly enhanced grid efficiency, visibility, and flexibility. However, this evolution also introduces new cybersecurity risks, particularly through supply chain dependencies and operational blind spots at the grid edge. To address these challenges, Idaho National Laboratory (INL), through the Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) Rapid Risk initiative, conducted a series of rapid risk assessment engagements with energy organizations across the United States. Drawing on lessons learned from these engagements, INL developed the following threat hunting guide for asset owners and operators (AOOs) to enhance their cybersecurity visibility within BESS and IBR systems. The guide demonstrates how to use passive network monitoring to baseline device behavior, detect adversarial activity, and investigate anomalies without disrupting operations. By implementing these practices, energy sector stakeholders can improve coordination between cybersecurity and operations teams and strengthen the resilience of distributed energy resources (DERs) within the modern power grid. Prior to implementing any network monitoring, packet capture, or threat hunting activity described in this guide, AOOs are strongly advised to review applicable governance frameworks, legal requirements, and organizational policies. This guide is intended for informational and educational purposes only. It does not replace compliance with any federal, state, or local cybersecurity mandates or industry standards. Implementation of described configurations, technologies, or analytic workflows is performed at the discretion and responsibility of the asset owner and operator.

25 - ENERGY STORAGE↗
NASA NTRSDate not supplied

DLES Unreal Simulation Tool (DUST)

NASA’s future Artemis missions to the Moon seek to explore areas around the Lunar South Pole. Though humans have previously set foot on the lunar surface, the proposed region provides unique and challenging environments that require insight and investigation prior to arrival. Several teams throughout the agency are performing this site and mission planning, design, and analysis to support areas like the Human Landing System (HLS), surface mobility, habitation elements, and scientific exploration. The NASA Exploration Systems Simulation (NExSyS) team at Johnson Space Center is developing a graphical environment of the Lunar South Pole region. Lunar terrain information collected from the Lunar Reconnaissance Orbiter (LRO) is compiled and made available through Johnson Space Center’s Digital Lunar Exploration Sites (DLES) data sets. The DLES data is used to build this graphic environment. The process of ingesting and accurately modeling this information in a meaningful way for analysis creates its own challenges such as generating a performant model from the source data and the application of curvature. Additionally, the area around the Lunar South Pole experiences different lighting conditions than those observed from the Apollo missions. The need to use the lunar environmental data products provided by DLES combined with the capability to calculate date specific ephemerides in real-time has given rise to the development of the DLES Unreal Simulation Tool (DUST). DUST incorporates augmented terrain from the DLES product into a desktop application that allows exploration of the Lunar South Pole region and its complex lighting conditions. DUST leverages advanced capabilities in the recently released Unreal Engine 5 renderer by Epic Games such as double precision for positioning of planetary bodies and surface elements, multiple infinite light sources to represent the Sun and eventually Earthshine, high resolution shadow maps for dynamic shadow accuracy, real-time software ray-tracing for multi-surface bounce lighting to render sunlight reflected off surface elements and terrain features, and performance optimized level of detail shifting as the eyepoint changes in a scene. This paper details the DUST application, the technologies of the engine platform that enable scientific and engineering analysis, the unique techniques and processes developed to consume the DLES data sets, and how the tool is being used to support the Artemis program.

Lunar Visualization↗
NASA NTRS2005

Recent Advances in Radar Polarimetry and Polarimetric SAR Interferometry

The development of Radar Polarimetry and Radar Interferometry is advancing rapidly, and these novel radar technologies are revamping Synthetic Aperture Radar Imaging decisively. In this exposition the successive advancements are sketched; beginning with the fundamental formulations and high-lighting the salient points of these diverse remote sensing techniques. Whereas with radar polarimetry the textural fine-structure, target-orientation and shape, symmetries and material constituents can be recovered with considerable improvements above that of standard amplitude-only Polarization Radar ; with radar interferometry the spatial (in depth) structure can be explored. In Polarimetric-Interferometric Synthetic Aperture Radar (POL-IN-SAR) Imaging it is possible to recover such co-registered textural plus spatial properties simultaneously. This includes the extraction of Digital Elevation Maps (DEM) from either fully Polarimetric (scattering matrix) or Interferometric (dual antenna) SAR image data takes with the additional benefit of obtaining co-registered three-dimensional POL-IN-DEM information. Extra-Wide-Band POL-IN-SAR Imaging - when applied to Repeat-Pass Image Overlay Interferometry - provides differential background validation and measurement, stress assessment, and environmental stress-change monitoring capabilities with hitherto unattained accuracy, which are essential tools for improved global biomass estimation. More recently, by applying multiple parallel repeat-pass EWB-POL-D(RP)-IN-SAR imaging along stacked (altitudinal) or displaced (horizontal) flight-lines will result in Tomographic (Multi- Interferometric) Polarimetric SAR Stereo-Imaging , including foliage and ground penetrating capabilities. It is shown that the accelerated advancement of these modern EWB-POL-D(RP)-IN-SAR imaging techniques is of direct relevance and of paramount priority to wide-area dynamic homeland security surveillance and local-to-global environmental ground-truth measurement and validation, stress assessment, and stress-change monitoring of the terrestrial and planetary covers. In addition, various closely related topics of (i) acquiring additional and protecting existing spectral windows of the Natural Electromagnetic Spectrum (NES) pertinent to Remote Sensing; (ii) mitigating against common "Radio Frequency Interference (RFI)" and intentional Directive Jamming of Airborne & Space borne POL-IN-SAR Imaging Platforms are appraised.

Boerner, Wolfgang-Martin↗
NASA NTRS1995

The luminosity structure and objective classification of galaxies

The luminosity structure of spiral galaxies is studied using the technique of principal component analysis. It is found that approximately 94% of the variation in the luminosity distribution of galaxies can be accounted for by just two principal components. The principal luminosity components may contain valuable information about star formation history or whatever luminosity-regulating process occurs in galaxies. Practically, these principal components provide a new approach for the investigation of the luminosity structures of galaxies and their dependence on other properties. They also serve as an excellent objective classification system for galaxies. We introduce in this paper such a classification scheme and explore its various properties. The new system shows a number of very impressive characteristics. Most important, it can well segregate virtually all the important galactic properties we tested and does so much better than the conventional morphological classification systems. Of particular interest is that some distance-dependent parameters can also be determined to a surprisingly good accuracy; for example, absolute magnitude may be determined to an accuracy of approximately 0.6 mag (yet further improvement is believed to be highly possible). Second, the system is objective, and the classification procedure can be automated to a large degree; also the new system can apply to much smaller and fainter images than do eye-based clasification systems. These properties make the new system suitable for practical application, especially on very large (and deeper) digital image catalogs. Third, the classification is expressed in dimensionless numbers, yet the simple notation bears significant and easily understandable meaning, making it easy and convenient to use. Finally, the new system has another extremely useful feature: it provides a very powerful and convenient platform not only for classification, but also for easily recording, examining, and studying the variations and correlations of galaxy properties-all these may be carried out graphically by using the C-vectors and the C-diagrams introduced in the paper. We wil also give an example to demonstrate the use of the classification system for the study of the internal extinction problem in spiral galaxies.

Han, Mingshen↗
DOE OSTI2024

Hardware Fuzzing with An Emulator

Bugs in digital logic have led to some significant security vulnerabilities. Hardware bugs are particularly troublesome since they cannot be easily patched. Additionally, if the bug is in the root of trust, all trust built upon it can be vulnerable. Traditional testing either require a deep knowledge of the system, creative attack vectors and lots of human interaction. This is not scalable as there are very few engineers that can wear the hat of a designer, a verification engineer, and a cybersecurity expert. Hardware fuzzing is a relatively new research area in dynamic hardware testing. It has proven to be an effective method for discovering bugs, unexpected behaviors, and security vulnerabilities in software. While hardware fuzzing is new to the hardware domain, it has a strong track record in software testing. Fuzzing is a testing technique that randomly mutates the input data to uncover bugs or vulnerabilities in the design. It is especially good at finding corner cases that test engineers can not envision. Another advantage over other dynamic testing techniques is that, if done well, deep knowledge of the design is not required. Additionally, fuzzing scales well. If the system is set up correctly, it can run unsupervised for weeks if necessary. In this work, we propose using hardware fuzzing to improve the input vector generation for an information flow tracking tool. To get reasonable throughput of test vectors, an emulator is targeted as the execution platform. Efficient emulator execution has some specific requirements.

42 ENGINEERING↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
NASA NTRS2012

NASA Tech Briefs, April 2012

Topics include: Computational Ghost Imaging for Remote Sensing; Digital Architecture for a Trace Gas Sensor Platform; Dispersed Fringe Sensing Analysis - DFSA; Indium Tin Oxide Resistor-Based Nitric Oxide Microsensors; Gas Composition Sensing Using Carbon Nanotube Arrays; Sensor for Boundary Shear Stress in Fluid Flow; Model-Based Method for Sensor Validation; Qualification of Engineering Camera for Long-Duration Deep Space Missions; Remotely Powered Reconfigurable Receiver for Extreme Environment Sensing Platforms; Bump Bonding Using Metal-Coated Carbon Nanotubes; In Situ Mosaic Brightness Correction; Simplex GPS and InSAR Inversion Software; Virtual Machine Language 2.1; Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction; Pandora Operation and Analysis Software; Fabrication of a Cryogenic Bias Filter for Ultrasensitive Focal Plane; Processing of Nanosensors Using a Sacrificial Template Approach; High-Temperature Shape Memory Polymers; Modular Flooring System; Non-Toxic, Low-Freezing, Drop-In Replacement Heat Transfer Fluids; Materials That Enhance Efficiency and Radiation Resistance of Solar Cells; Low-Cost, Rugged High-Vacuum System; Static Gas-Charging Plug; Floating Oil-Spill Containment Device; Stemless Ball Valve; Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs; Oxygen-Methane Thruster; Lunar Navigation Determination System - LaNDS; Launch Method for Kites in Low-Wind or No-Wind Conditions; Supercritical CO2 Cleaning System for Planetary Protection and Contamination Control Applications; Design and Performance of a Wideband Radio Telescope; Finite Element Models for Electron Beam Freeform Fabrication Process Autonomous Information Unit for Fine-Grain Data Access Control and Information Protection in a Net-Centric System; Vehicle Detection for RCTA/ANS (Autonomous Navigation System); Image Mapping and Visual Attention on the Sensory Ego-Sphere; HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis; and IMAGESEER - IMAGEs for Education and Research.

Source record↗
NASA NTRS2018

Earth Science Engagement Through Art and Astronaut Photography

For over five decades of manned spaceflight missions, NASA astronauts have taken extraordinary photographs of Earth's surface and dynamic processes. Humans on board the International Space Station (ISS) have a unique platform to perform Earth observations at various viewing angles, seasons, and times of day. Astronaut photos taken from the ISS comprise a true‐color (RGB) dataset taken with multiple handheld digital cameras and lens types (prior to 2004, film cameras were in use). Earth observations through astronaut photography are an important and unique remote sensing method when monitoring natural disasters, urban growth, and environmental changes. While astronaut imagery can be used for earth science research, there is also an artistic aspect to the photography that fascinates a wide global population. A broader public audience can be introduced to earth science through high resolution, Earth art photos taken from the perspective of an astronaut. The Crew Earth Observations (CEO) Facility within the Earth Science and Remote Sensing Unit at NASA's Johnson Space Center supports the acquisition, analysis, and curation of astronaut photography of Earth's surface and atmosphere. CEO's website, the Gateway to Astronaut Photography of Earth (eol.jsc.nasa.gov), provides free public access to view, search, and download over three million images taken by astronauts throughout all of NASA's crewed spaceflight history, with an emphasis on current ISS imagery. The CEO Facility actively curates a digital collection of exceptional Earth art astronaut photos used for public engagement. Our new Downloadable Earth Art page focuses on broad earth science topics including: mountains, water, clouds, agriculture, as well as an "abstract" category. This continuously‐updated collection is comprised of freely accessible and high‐quality downloadable materials, such as single‐ and dual‐screen digital wallpapers. All Earth Art materials are presented with science‐based information that complements the artistic qualities of the imagery, and facilitate connections between general audiences and earth science from the International Space Station.

Meado, Andrea L.↗
NASA NTRS2014

Introduction

The 2014 SPIE Sensing Technologies for Global Health, Military Medicine and Environmental Monitoring conference embraced a wealth of state-of-the-art information in basic and applied science. This event covered the latest developments in the following areas: Non-invasive Disease Diagnostics for Global Health- This opening series of two consecutive sessions focused on oral biospecimen based rapid assays and point-of-care devices for the detection of pathogens causing infectious diseases, biomarkers for cancer, and analytes for noncommunicable diseases such as diabetes. They also covered presentations on the human proteasome and microbiome with linkage to human diseases and diagnostic approaches. The sessions were built on the past experience and expertise of the National Institutes of Health, National Institutes of Dental and Craniofacial Research. Military Medicine I: Traumatic Brain Injury and PTSD-This assembly covered oral-biomarker based diagnostics for brain damage and TBI as well as prevention and rehabilitation technologies. Neurorehabilitation and noninvasive neuromodulation were also discussed as critical approaches for effective functioning. Military Medicine II: Physiology and Medicine of Extreme Environments and Spaceflight-This scientific segment showcased physiological, pharmacological and diagnostic sensing methodologies during spaceflight per the National Aeronautics and Space Administration as well as military-relevant toxicans and future sensing trends per the Department of Defense. It also included latest technologies to determine hydration status in warfighters, eye surgery using the latest laser technologies, and sensing tools for blood analysis.  Sensing Technologies for Disease Diagnostics and Environmental Monitoring-This closing series of two consecutive sessions provided the venues to learn and discuss more results on the next generation of diagnostic tools and field technologies for diseases, including biomarker detection by digital imaging, multiplex technologies, capillary electrophoresis and molecular platforms serving as labs-on-chips. This conference allowed cross-fertilization of ideas, projects and collaborative work by a multidisciplinary audience of national and international colleagues from the academia, industry and federal government: The National Institutes of Health, National Aeronautics and Space Administration, and the Department of Defense.

Wotring, Virginia E.↗
NASA NTRS2018

Initial Approach to Collect Small Unmanned Aircraft System Off-Nominal Operational Situations Data

NASA is developing the Unmanned Aircraft System Traffic Management research platform to safely integrate small unmanned aircraft operations in large-scale at low-altitudes. As a part of this effort, small unmanned aircraft system off-nominal operational situations data collection process has been developed to take lessons learned and to reinforce operational compliance. In this paper, descriptions of variables used for digital data collection and an online report form for collection of observational data from the operators (contextual data) are provided. They are used to collect off-nominal data from the Unmanned Aircraft System Traffic Management National Campaign in 2017. The digital data show that 2 out of 118 campaign operations (1.7%) encountered loss of navigation. Since the campaign aircraft used Global Positioning System for navigation, it is likely that unobstructed view of the sky at the campaign locations contributed to this small number. Also, 4 out of 47 operations (8.5%) encountered loss of communications. A relatively short distance between ground control system and aircraft, ranging from 2300 feet to 4200 feet, likely contributed to this small number. There was no data to identify the loss of communications condition, aircraft received signal strength, for the remaining 71 operations suggesting that some operators may not be monitoring unmanned aircraft communications system performance or monitoring it with different parameters. For the contextual data, due to the low number of total reports during the campaign, no significant trends emerged. This is an initial attempt to collect contextual data from small unmanned aircraft operators about off-nominal situations, and changes will be made to the future data collection to improve the amount and quality of the information.

unmanned aviation systems traffic management (UTM)↗
NASA NTRS2018

Initial Approach to Collect Small Unmanned Aircraft System Off-Nominal Operational Situations Data

NASA is developing the Unmanned Aircraft System Traffic Management research platform to safely integrate small unmanned aircraft operations in large-scale at low-altitudes. As a part of this effort, small unmanned aircraft system off-nominal operational situations data collection process has been developed to take lessons learned and to reinforce operational compliance. In this paper, descriptions of variables used for digital data collection and an online report form for collection of observational data from the operators (contextual data) are provided. They are used to collect off-nominal data from the Unmanned Aircraft System Traffic Management National Campaign in 2017. The digital data show that 2 out of 118 campaign operations (1.7%) encountered loss of navigation. Since the campaign aircraft used Global Positioning System for navigation, it is likely that unobstructed view of the sky at the campaign locations contributed to this small number. Also, 4 out of 47 operations (8.5%) encountered loss of communications. A relatively short distance between ground control system and aircraft, ranging from 2300 feet to 4200 feet, likely contributed to this small number. There was no data to identify the loss of communications condition, aircraft received signal strength, for the remaining 71 operations suggesting that some operators may not be monitoring unmanned aircraft communications system performance or monitoring it with different parameters. For the contextual data, due to the low number of total reports during the campaign, no significant trends emerged. This is an initial attempt to collect contextual data from small unmanned aircraft operators about off-nominal situations, and changes will be made to the future data collection to improve the amount and quality of the information.

Jung, Jaewoo↗
NASA NTRS1996

Geodata Modeling and Query in Geographic Information Systems

Geographic information systems (GIS) deal with collecting, modeling, man- aging, analyzing, and integrating spatial (locational) and non-spatial (attribute) data required for geographic applications. Examples of spatial data are digital maps, administrative boundaries, road networks, and those of non-spatial data are census counts, land elevations and soil characteristics. GIS shares common areas with a number of other disciplines such as computer- aided design, computer cartography, database management, and remote sensing. None of these disciplines however, can by themselves fully meet the requirements of a GIS application. Examples of such requirements include: the ability to use locational data to produce high quality plots, perform complex operations such as network analysis, enable spatial searching and overlay operations, support spatial analysis and modeling, and provide data management functions such as efficient storage, retrieval, and modification of large datasets; independence, integrity, and security of data; and concurrent access to multiple users. It is on the data management issues that we devote our discussions in this monograph. Traditionally, database management technology have been developed for business applications. Such applications require, among other things, capturing the data requirements of high-level business functions and developing machine- level implementations; supporting multiple views of data and yet providing integration that would minimize redundancy and maintain data integrity and security; providing a high-level language for data definition and manipulation; allowing concurrent access to multiple users; and processing user transactions in an efficient manner. The demands on database management systems have been for speed, reliability, efficiency, cost effectiveness, and user-friendliness. Significant progress have been made in all of these areas over the last two decades to the point that many generalized database platforms are now available for developing data intensive applications that run in real-time. While continuous improvement is still being made at a very fast-paced and competitive rate, new application areas such as computer aided design, image processing, VLSI design, and GIS have been identified by many as the next generation of database applications. These new application areas pose serious challenges to the currently available database technology. At the core of these challenges is the nature of data that is manipulated. In traditional database applications, the database objects do not have any spatial dimension, and as such, can be thought of as point data in a multi-dimensional space. For example, each instance of an entity EMPLOYEE will have a unique value corresponding to every attribute such as employee id, employee name, employee address and so on. Thus, every Employee instance can be thought of as a point in a multi-dimensional space where each dimension is represented by an attribute. Furthermore, all operations on such data are one-dimensional. Thus, users may retrieve all entities satisfying one or more constraints. Examples of such constraints include employees with addresses in a certain area code, or salaries within a certain range. Even though constraints can be specified on multiple attributes (dimensions), the search for such data is essentially orthogonal across these dimensions.

Adam, Nabil↗