Individualized Degradation Modeling and Prognostics in a Heterogeneous Group via Incorporating Intrinsic Covariate Information
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Automation & Control Systems↗
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Results for “Information Sharing”
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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Platform for Integrated Land use And Transportation Experiments and Simulation (PILATES) v1.0
PILATES allows for flexibly and at-scale coupling of multiple models to allow for multi-scale and multi-resolution simulation of regional-scale transport networks. In particular, it couples the MATSim-derived transportation modeling framework for Behavior, Energy, Autonomy and Mobility (BEAM) with other models operating at different time scales. Rather than tightly coupling supply and demand models using shared agents and memory within the same software process, PILATES orchestrates different model runs in a containerized framework. This structure requires passing information from the demand models to BEAM in the format of a synthetic population and agent plans, and from BEAM to the demand models in terms or origin/destination tables (also known as "skims"). This allows it to take advantage of the behavioral sophistication of existing activity-based models as well as the reinforcement learning structure of MATSim replanning and adopted by BEAM, in a way that requires minimal changes to existing models. It also takes advantage of the computational performance of BEAM, which allows for simulations with millions of agents to complete in reasonable time as well as allowing for detailed mechanistic simulation of the operation of on-demand modes.
Needell, Zachary↗
Advanced Computing, Data Science, and Artificial Intelligence Research Opportunities for Energy-Focused Transportation Science
The Energy Efficient Mobility Systems (EEMS) technology landscape is complex and rapidly evolving, which provides both tremendous opportunities and formidable challenges. Significant alterations to the mobility landscape are underway due to the advent of vehicle and infrastructure connectivity, autonomous driving, and rapid passenger- and freight-vehicle electrification. Advanced computing will play an increasingly important role in enabling the EEMS program to understand and identify the most important levers to improve the energy productivity of future integrated mobility systems. It is also driving new approaches to mobility and the research to unlock an affordable, efficient, safe, and accessible transportation future. Driving much of this change is the collection, analysis, and strategic use of massive amounts of diverse, complex data from infrastructure and vehicles with on-board sensors and data storage and transmission capabilities. Diverse and representative data are key to implementing approaches to maximize mobility energy productivity. While high-fidelity modeling of integrated transportation networks has strengthened our understanding of dynamic movement and behavior patterns, existing tools must be expanded beyond their current focus. This work necessitates data infrastructure investments (e.g., secure-streaming data platforms driven by ubiquitous sensors and video analytics) as well as investments in critical capabilities for large-scale automated analysis and organization using modern machine learning, statistics, and artificial intelligence. Other chief needs include agile, large-scale storage that can be quickly searched and queried for relevant data to support validation and model development, data-sharing agreements, and formatting standards for key data types. The future of public transit must be explored in greater detail, research must inform design, and opportunities must be identified for improving the mobility productivity of public transit in both urban and rural America.
33 ADVANCED PROPULSION SYSTEMS↗
Machine Learning for Automated Metadata Assignment in Buildings: Cooperative Research and Development (Final Report, CRADA Number CRD-18-00767)
RealTerm Energy and NREL have identified a shared vision to evaluate opportunities to facilitate the organization and assignment of metadata to building control system (BCS) data via industry-informed machine learning (ML). Manual metadata assignment is labor intensive and costly, slowing down any Energy Management and Information System (EMIS) deployment in the building space. This project aims to develop methodologies to accurately assign this metadata and significantly decrease the level of effort associated with deploying EMIS. The objective of this project is to identify/design methodologies to assign metadata to HVAC control points automatically. The identified methodologies will be programmed in analytics algorithms so they can ingest a list of points and produce a detailed tagging following the Haystack classification nomenclature. To validate the efficacy of each methodology, tagging results will be compared utilizing a list of points extracted from RealTerm's building database-as well as data extracted from the NREL campus via the Intelligent Campus program-enabling testing against large datasets with real world challenges. The developed methodologies may leverage building manager/operator input on a limited basis to add context to the classifying algorithms. The partnership aims to advance global efforts in areas related to the DOE missions through improving operational performance of commercial buildings. It is well documented that buildings fall out of commission after they are occupied, wasting significant energy and incurring associated costs simply due to poor operational performance. Emerging EMIS technologies that perform continuous commissioning help to address this issue, yet integration of these systems can be labor intensive both for the technology vendor and the building owner/operator. This project will enable more efficient and cost-effective analytics for buildings, enabling improvement in building operations at lower cost points.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Design, development, and flight test of a demonstration advanced avionics system
Ames Research Center initiated a program in 1975 to provide the critical information required for the design of integrated avionics suitable for general aviation. The program emphasized the use of data busing, distributed microprocessors, shared electronic displays and data entry devices, and improved functional capability. Design considerations included cost, reliability, maintainability, and modularity. As a final step, a demonstration advanced avionics system (DAAS) was designed, built, and flight tested in a Cessna 402, twin engine, general aviation aircraft. A functional description of the DAAS, including a description of the system architecture, is presented and the program and flight test results are briefly reviewed.
Denergy, D. G.↗
The network data delivery service: A real-time data connectivity system
A network data-sharing system, the Network Data Delivery Service (NDDS), is discussed. The NDDS system relies on a bipartite model consisting of the following: information producers (sources) and information consumers (sinks). The example provided by a dual-arm robotic system is examined.
Pardo-Castellote, Gerardo↗
What we have learned about Mars from SNC meteorites
The SNC meteorites are thought to be igneous martian rocks, based on their young crystallization ages and a close match between the composition of gases implanted in them during shock and the atmosphere of Mars. A related meteorite, ALH84001, may be older and thus may represent ancient martian crust. These petrologically diverse basalts and ultramafic rocks are mostly cumulates, but their parent magmas share geochemical and radiogenic isotopic characteristics that suggest they may have formed by remelting the same mantle source region at different times. Information and inferences about martian geology drawn from these samples include the following: Planetary differentiation occured early at approximately 4.5 GA, probably concurrently with accretion. The martian mantle contains different abundances of moderately volatile and siderophile elements and is more Fe-rich than that of the Earth, which has implications for its mineralogy, density, and origin. The estimated core composition has a S abundance near the threshold value for inner core solidification. The former presence of a core dynamo may be suggested by remanent magnetization in Shergottite-Nakhlite-Chassignite (SNC) meteorites, although these rocks may have been magnetized during shock. The mineralogy of martian surface units, inferred from reflectance spectra, matches that of basaltic shergottites, but SNC lithologies thought to have crystallized in the subsurface are not presently recognized. The rheological properties of martian magmas are more accurately derived form these metorites than from observations of martian flow morphology, although the sampled range of magma compositions islimited. Estimates of planetary water abundance and the amount of outgassed water based on these meteorites are contridictory but overlap estimates based on geological observations and atmospheric measurements. Stable isotope measurements indicate that the martian hydrosphere experienced only limited exchange with the lithosphere, but it is in isotopic equilibrium with the atmosphere and has been since 1.3 Ga. The isotopically heavy atmosphere/hydrosphere composition deduced from these rocks reflects a loss process more severe than current atmospheric evolution models, and the occurence of carbonates in SNC meteorites suggest that they, rather than scapolite or hydrous carbonates, are the major crustal sink for CO2. Weathering products in SNC meteorites support the idea of limited alteration of the lithosphere by small volumes of saline, CO2-bearing water. Atmospheric composition and evolution are further constrained by noble gases in these meteorites, although Xe and Kr isotopes suggest different origins for the atmosphere. Planetary ejection of these rocks has promoted an advance in the understanding of impact physics, which has been accomplished by a model involving spallation during large cratering events. Ejection of all the SNC meteorites (except ALH84001) in one or two events may provide a plausible solution to most constraints imposed by chronology, geochemistry, and cosmic ray exposure, although problems remain with this scenario; ALH84001 may represent older martian crust sampled during a separate impact.
Mcsween, Harry Y., Jr.↗
NASA Dryden Flight Research Center: Unmanned Aircraft Operations
This slide presentation reviews several topics related to operating unmanned aircraft in particular sharing aspects of unmanned aircraft from the perspective of a pilot. There is a section on the Global Hawk project which contains information about the first Global Hawk science mission, (i.e., Global Hawk Pacific (GloPac). Included in this information is GloPac science highlights, a listing of the GloPac Instruments. The second Global Hawk science mission was Genesis and Rapid Intensification Process (GRIP), for the NASA Hurricane Science Research Team. Information includes the instrumentation and the flights that were undertaken during the program. A section on Ikhana is next. This section includes views of the Ground Control Station (GCS), and a discussion of how the piloting of UAS is different from piloting in a manned aircraft. There is also discussion about displays and controls of aircraft. There is also discussion about what makes a pilot. The last section relates the use of Ikhana in the western states fire mission.
Pestana, Mark↗
Multi-Operator Multi-UAV (MOMU) Control: Exploring the Influence of Sensor Tools and Playbook Task Delegation
New concepts of operations for Unmanned Aerial Vehicles (UAVs) will require a change from the current 2:1 operator to vehicle crew configuration. One particular control paradigm, largely driven by logistics, manpower, and training burdens, as well as the desire to force multiply, involves a single operator simultaneously managing multiple UAVs. This mode of operations has shown to significantly increase cognitive workload and decrease situation awareness, as operators are required to simultaneously attend to multiple sources of information. One potential way to mitigate potential drawbacks of multi-vehicle control by a single operator is to migrate to a multi-operator multi-UAV (MOMU) crew configuration, whereby M operators control N (> M) vehicles. This type of crew configuration can be organized in several ways to dynamically manage cognitive workload, match operator qualifications and skills to mission requirements, increase utilization of available assets, and thereby achieve maximum force multiplication. The present experiment examined task performance in a simulated MOMU environment and evaluated the potential benefits of sensor management aids ("Tools") as well as integrated sensor and flight automation ("Plays") compared to a fully manual condition ("Manual"). Tools support the operator by facilitating rapid understanding and management of sensor information, while the Plays support the operator by offloading/automating subtasks. Six pairs of participants were recruited for this study and tasked with sharing a pool of UAVs in order to conduct reconnaissance, surveillance, and target acquisition (RSTA) missions in adjacent Areas of Operation (AOs). Participants were given four tasks to accomplish, in order of priority: 1) prosecute High Value Targets (HVTs); 2) identify / track targets (military vehicles); 3) identify / mark civilian vehicles; and 4) respond to chat messages. Performance on the mission tasks was measured in terms of accuracy and reaction time. A composite mission score was also calculated using a payoff matrix that weighted each task according to priority. The results indicate that Playbook demonstrated better performance overall with higher accuracy rates and the highest composite score compared to Tools and Manual. The implications of these results to supporting future MOMU concepts of operations is discussed.
Playbook↗
TPSAS-NF1676L-35202-DND
On March 20th, 2019, the National Aeronautics and Space Administration (NASA) and the Sistema de la Integración Centroamericana (SICA, Central American Integration System) issued a joint statement sharing their mutual desire to make Earth observations and remote sensing data and products more readily available to SICA member countries to inform their decision making. Regional capacity building in the use of Earth observations was one of the many opportunities for collaboration highlighted in this joint statement. As an initial capacity building effort, NASA deployed a series of feasibility projects through the DEVELOP Program to engage emerging and transitioning professionals with SICA member state partner agencies. The initial projects in summer 2019 addressed landslides in the Dominican Republic, near-coastal flooding in Guatemala and El Salvador, and environmental challenges to the Mesoamerican Reef off the coast of Belize. Additional projects were planned for fall 2019 with the objective of impacting environmental challenges for the remaining SICA member states. This presentation will summarize the results of projects conducted to date, address the lessons learned in the process of rapid engagement with regional entities, and consider how the partnership may evolve over time.
Kenton Ross↗
Use NASA Earthdata in the Cloud
With the impending arrival of new, high-data-volume Earth observation missions, NASA’s ability to effectively ingest, process, and archive large amounts of data requires the most cost-effective, flexible, and scalable data-management architectures and technologies. To meet these demands, NASA's Earth Science Data Systems (ESDS) Program is implementing a strategic vision to develop and operate multiple components of NASA's Earth Observing System Data and Information System (EOSDIS) in a commercial cloud environment. As more earth observing data is moved into the cloud, NASA EOSDIS wants to share our reasoning for moving data into the commercial cloud, as well as demonstrate how end-users can take advantage of both data in the cloud and cloud-deployed services. This presentation explains the enterprise reasons for moving data into the cloud, specifically the scalability of cloud systems, the flexibility to pivot to the most optimal language/system/architecture, and the reduced redundancy of a common architecture. Then we primarily focus on the user-driven reasons for moving to the cloud. Equal access to high performance computing helps all parties, especially users that don't have access to their own on-premise computing infrastructure. Rapid deployment allows users to spin up their own systems without needing the extensive platform previously required for large scale development. Cost effectiveness allows users a wide breadth of computing options, from super computer ability to small scale development, which can help underserved communities. Finally we link to internal resources that users can utilize to begin their cloud based development journey without having to be an expert in the field.
Nicholas Doty↗
Factors Influencing Adoption of Pooled Rideshare An Explorative Study on User-Centered Design and Services
The rise of real-time information communication through smartphones and wireless networks enabled the growth of ridesharing services. While personal rideshare services (individuals ride alone or with people they know) initially dominated the market, the popularity of pooled ridesharing (individuals share rides with strangers) has grown globally. However, pooled rideshare remains less common in the U.S., where personal vehicle usage is still the norm. Vehicle design and rideshare services may need to be tailored to user preferences to increase pooled rideshare adoption. A national U.S. survey ( N = 5,385) used exploratory and confirmatory factor analyses to identify four key factors influencing riders’ willingness to consider pooled rideshare: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. Understanding and implementing these user-centered design principles and service-related factors may be critical for increasing the future use of pooled rideshare services
Gangadharaiah, Rakesh↗
EDGE COVID-19: a web platform to generate submission-ready genomes from SARS-CoV-2 sequencing efforts
Abstract Summary Genomics has become an essential technology for surveilling emerging infectious disease outbreaks. A range of technologies and strategies for pathogen genome enrichment and sequencing are being used by laboratories worldwide, together with different and sometimes ad hoc, analytical procedures for generating genome sequences. A fully integrated analytical process for raw sequence to consensus genome determination, suited to outbreaks such as the ongoing COVID-19 pandemic, is critical to provide a solid genomic basis for epidemiological analyses and well-informed decision making. We have developed a web-based platform and integrated bioinformatic workflows that help to provide consistent high-quality analysis of SARS-CoV-2 sequencing data generated with either the Illumina or Oxford Nanopore Technologies (ONT). Using an intuitive web-based interface, this workflow automates data quality control, SARS-CoV-2 reference-based genome variant and consensus calling, lineage determination and provides the ability to submit the consensus sequence and necessary metadata to GenBank, GISAID and INSDC raw data repositories. We tested workflow usability using real world data and validated the accuracy of variant and lineage analysis using several test datasets, and further performed detailed comparisons with results from the COVID-19 Galaxy Project workflow. Our analyses indicate that EC-19 workflows generate high-quality SARS-CoV-2 genomes. Finally, we share a perspective on patterns and impact observed with Illumina versus ONT technologies on workflow congruence and differences. Availability and implementation https://edge-covid19.edgebioinformatics.org, and https://github.com/LANL-Bioinformatics/EDGE/tree/SARS-CoV2. Supplementary information Supplementary data are available at Bioinformatics online.
59 BASIC BIOLOGICAL SCIENCES↗
Regulatory utility of physiologically based pharmacokinetic modeling for assessing food impact in bioequivalence studies: A workshop summary report
This workshop report summarizes the presentations and panel discussion related to the use of physiologically based pharmacokinetic (PBPK) modeling approaches for food effect assessment, collected from Session 2 of Day 2 of the workshop titled “Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches.” The US Food and Drug Administration in collaboration with the Center for Research on Complex Generics organized this workshop where this particular session titled “Oral PBPK for Evaluating the Impact of Food on BE” presented successful cases of PBPK modeling approaches for food effect assessment. Recently, PBPK modeling has started to gain popularity among academia, industries, and regulatory agencies for its potential utility during bioavailability (BA) and/or bioequivalence (BE) studies of new and generic drug products to assess the impact of food on BA/BE. Considering the promises of PBPK modeling in generic drug development, the aim of this workshop session was to facilitate knowledge sharing among academia, industries, and regulatory agencies to understand the knowledge gap and guide the path forward. This report collects and summarizes the information presented and discussed during this session to disseminate the information into a broader audience for further advancement in this area.
60 APPLIED LIFE SCIENCES↗
An AI-accelerated pathway for reproducible and stable halide perovskites
Halide perovskites (HPs) have remarkable optoelectronic properties, and in the last decade their photovoltaic power conversion efficiency and light-emitting diode efficiency have skyrocketed. Despite the surge in research on these burgeoning materials, two key challenges in the field remain: material irreproducibility and instability. Their behavior is especially dynamic in response to environmental stressors, due to complex interactions with the perovskite crystal lattice. Here, in this review, we survey the latest achievements in HP materials research accomplished with the assistance of artificial intelligence (AI), through the implementation of automated experimentation and machine learning (ML) data analysis. Automated synthesis and characterization tackle problems with material irreproducibility by systematically controlling parameters with very high precision, creating massive datasets, and allowing methodical comparisons from which unbiased conclusions can be drawn. AI can reveal otherwise unnoticed trends, inform future experiments with the highest potential information gain, and forecast future performance. The review concludes with a forward viewpoint of how human-assisted closed-loop laboratories and shared databases allow halide perovskite materials’ processing, properties, and performance to be potentially optimized with AI, accelerating the development of highly reproducible and stable optoelectronic devices.
Hering, Abigail R. [Univ. of California, Davis, CA↗
Multidimensional images and aberrations in STEM
Recent advances in scanning transmission electron microscopy (STEM) have led to increased development of multi-dimensional STEM imaging modalities and novel image reconstruction methods. This interest arises because the main electron lens in a modern transmission electron microscope usually has a diffraction-space information limit that is significantly better than the real-space resolution of the same lens. This state-of-affairs is sometimes shared by other scattering methods in modern physics and contributes to a broader excitement surrounding multidimensional techniques that scan a probe while recording diffraction-space images, such as ptychography and scanning nano-beam diffraction. However, the contrasting resolution in the two spaces raises the question as to what is limiting their effective performance. Here, we examine this paradox by considering the effects of aberrations in both image and diffraction planes, and likewise separate the contributions of pre- and post-sample aberrations. In conclusion, this consideration provides insight into aberration-measurement techniques and might also indicate improvements for super-resolution techniques.
Hoglund, Eric R.↗
Modernization of Technical Requirements for Licensing of Advanced Non-Light Water Reactors: Probabilistic Risk Assessment Approach
This report, “Modernization of Technical Requirements for Licensing of Advanced Non-Light Water Reactors: Probabilistic Risk Assessment Approach,” represents a key element in the development of a methodology for the efficient licensing of advanced non-light water reactors (non-LWRs). It is the result of a Licensing Modernization Project (LMP) led by Southern Company and cost-shared by the U.S. Department of Energy (DOE). The LMP has developed detailed proposals for establishing licensing technical requirements to facilitate risk-informed and performance-based design and licensing of advanced non-LWRs. Such a methodology acknowledges enhancements in safety achievable with advanced designs and reflects more recent states of knowledge regarding safety and design innovation, creating an opportunity for reduced regulatory complexity with increased levels of safety. The project builds on best practices, as well as previous activities through DOE and industry-sponsored advanced reactor licensing initiatives.
42 ENGINEERING↗
Schema Elements for Granta Annual Report: FY2022
Granta: Materials Intelligence, also known as Granta:MI or Granta, is a commercial database software by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store relevant materials data. For a complete discussion of the use of Granta:MI at NSE sites, see the FY21 annual report. Granta:MI is used by five NSE sites locally, and all NSE sites have access to an enterprise instance on the Enterprise Secure Network (ESN) as well as an unclassified development instance. It has been recognized by NNSA management that a shared repository for additive manufacturing (AM) data would not only ensure data and knowledge is not lost but would provide a pool of information relating AM inputs (build parameters, raw materials properties, post-processing information) to the as-built properties of AM parts. Such a pool of data would enable optimization of AM build design and help NNSA achieve the goals of shortening fielding times for new components.
36 MATERIALS SCIENCE↗