Data Management, Organization & Analytics
Presented by LRST at the eXtremeMAT Stakeholder Advisory Board Review Meeting, 10/15
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Presented by LRST at the eXtremeMAT Stakeholder Advisory Board Review Meeting, 10/15
Having a vision that others agree to support and work toward is highly desirable but hard to achieve. We seem to lack a common and shared understanding of the vision—or worse, multiple visions (vivid mental images or documented statements) with varying areas of focus and details: • some appear to be similar but have differing underlying goals and characteristics, or • some reflect differing viewpoints as to effects on various stakeholders. These desirable and undesirable situations apply to realizing visions for enterprise and industry, including the electricity sector. Multiple industry stakeholder groups have developed goals, industry vision statements, and characterizations of the future. The viewpoints are promoted, discussed, refined by their stakeholder group, and often published to promote broad understanding and to inform or influence others. The GridWise Architecture Council asked itself how well-aligned these characterizations of the future are. If these publications collectively set the overall direction for the industry, it is useful to identify their answers to questions such as, where is the electric industry headed, guided by what objectives, and with what role(s) for the customers, electric utilities, and other stakeholders? Are these goals, visions, and future states moving toward a common vision, do they provide value for the stakeholders, and are they likely to meet the objective stated? This paper addresses these questions via an assessment and characterization of a sampling of stakeholder groups’ publicly available vision and future state reports for the electricity industry. Identified electric power grid architectural topic areas needing further work are described, along with GridWise Architecture Council analysis and observation, potential collaborative work efforts, and suggested next steps.
Teams at NASA have conducted studies of time-delayed communications as it effects human exploration. In October 2012, the Advanced Exploration Systems (AES) Analog Missions project conducted a Technical Interchange Meeting (TIM) with the primary stakeholders to share information and experiences of studying time delay, to build a coherent picture of how studies are covering the problem domain, and to determine possible forward plans (including how to best communicate study results and lessons learned, how to inform future studies and mission plans, and how to drive potential development efforts). This initial meeting s participants included personnel from multiple NASA centers (HQ, JSC, KSC, ARC, and JPL), academia, and ESA. It included all of the known studies, analog missions, and tests of time delayed communications dating back to the Apollo missions including NASA Extreme Environment Mission Operations (NEEMO), Desert Research and Technology Studies (DRATS/RATS), International Space Station Test-bed for Analog Research (ISTAR), Pavilion Lake Research Project (PLRP), Mars 520, JPL Mars Orbiters/Rovers, Advanced Mission Operations (AMO), Devon Island analog missions, and Apollo experiences. Additionally, the meeting attempted to capture all of the various functional perspectives via presentations by disciplines including mission operations (flight director and mission planning), communications, crew, Capcom, Extra-Vehicular Activity (EVA), Behavioral Health and Performance (BHP), Medical/Surgeon, Science, Education and Public Outreach (EPO), and data management. The paper summarizes the descriptions and results from each of the activities discussed at the TIM and includes several recommendations captured in the meeting for dealing with time delay in human exploration along with recommendations for future development and studies to address this issue.
This paper will explore the challenges and triumphs of implementing advanced robotic systems and the supporting technology for the waste management industry. Deployment-ready solutions for autonomous robotic platforms are new to the radioactive waste management industry and have been slow to implement across the complex. Specifically, it will explore Hanford Tank Operations Contractor's implementation of ground-based autonomous robotic platforms to enhance workforce safety and efficiency. The first objective of the project is to demonstrate safe operation of autonomous instrumented robots, followed by a scaling up of capabilities for the platform to meet any additional requests of internal stakeholders. Autonomous robotics platforms offer a fantastic range of capabilities that will aid in decreasing long-term liability of radioactive waste management industry by enhancing clean-up efforts and worker safety. Over the past five years, deployment-ready solutions for autonomous robotic platforms have come a long way. Autonomous robotic platforms are increasingly available to purchase from many vendors, often for prices under $100,000. Additionally, the vendors who produce these products are efficient in accommodating customized solutions to unique challenges, such as those often found in the radioactive waste management industry. However, the newness of this technology to the radioactive waste management industry and has resulted in a slow implementation across the complex. This paper will explore the challenges and triumphs of implementing advanced robotic systems and supporting technology for the waste management industry. Specifically, it will explore Hanford Tank Operations Contractor's implementation of ground based autonomous robotic platforms to enhance workforce safety and efficiency. While the company has utilized robots in many parts of the tank farms mission, this was first attempted in the deployment of an autonomous platform that required no operator with only minor maintenance and created challenges not yet encountered. These challenges included stakeholder buy-in, operational safety, procurement uncertainties, adaption of 'off the shelf products, overcoming the 'Fukushima complexity' perception, security concerns, and the initial criteria down select. Hanford Tank Operations Contractor's management identified the need for a workforce protecting low cost robotics solution in the fall of 2017. The project scope generated was part of the response to the Tank Vapor Assessment Team recommendation to enhance the safety of the workforce by utilizing new technology. Since the projects inception, it has been incorporated into the Tank Stewardship Plan. This project was unique to Hanford Tank Operations Contractor, as the company does not frequently incorporate advanced technology for field deployment quickly. The project was also progressive to the Hanford Tank Operations Contractor as it functions as a partnership project with a local university. The first objective of the project is to demonstrate safe operations of autonomous instrumented robotics, monitoring and collecting data, and the wireless transmission of data to a central computing system. Following the completion of this first objective, the project will scale up capabilities of the platform to meet the requests of additional internal stakeholders throughout the tank farms - including completion of specific in-field tasks. (authors)
The objective of the AI Roadmap meeting is to engage with all stakeholders in aviation in an open conversation about our approach and the guiding principles that can help us in moving forward in the technological landscape of AI/ML. The objective of the Technical Exchange Meeting is to identify categories of safety concerns associated with having an AI component in the aircraft we identified in the previous Technical Exchange Meetings. The speakers will bring their experience to the discussion to stimulate active participation with all stakeholders.
The objective of the AI Roadmap meeting is to engage with all stakeholders in aviation in an open conversation about our approach and the guiding principles that can help us in moving forward in the technological landscape of AI/ML. The objective of the Technical Exchange Meeting is to identify categories of safety concerns associated with having an AI component in the aircraft we identified in the previous Technical Exchange Meeting on January 24, 2024. This presentation is to spur the discussion regarding the use of AI/ML in civil aviation and stimulate active participation with all stakeholders.
As part of the experiment design and planning, the critical experiment design team (CEDT), as well as additional stakeholders, convened a series of meetings to discuss the goals and requirements of execution for this experiment. The slides from these meetings are attached in Appendix A. The following sections summarize the outcome of those discussions and present the planned experimental configurations and measurements. The stated goals of this experiment are as follows: 1) Measure time-tagged list-mode data for configurations exceeding neutron multiplication of 100; 2) Provide intercomparison between LLNL, LANL, and IRSN detector systems and methodologies; 3) Generate experiment execution report(s) useful to a fundamental physics benchmark for the ICSBEP; 4) Leverage existing critical experiment and detector system benchmarks to limit required modeling and uncertainty analysis.
NASA is returning to the Moon to stay. To establish a sustained lunar presence, astronauts will pack spacesuits, surface-mobility tools, rovers, and decades’ worth of knowledge. The U.S. Spacesuit Knowledge Capture (SKC) and Strategic Communications (Strat Comm) team is specialized in capturing, preserving, and sharing space-related knowledge with NASA scientists, technicians, engineers, vendors, and the public to support space exploration. Since the SKC Program’s 2007 inception, its focus has been to capture and share valuable spacesuit-related knowledge with the NASA community. As the program evolved, its notoriety, funding, scope, and staffing expanded from a one-person, part-time, unfunded operation to a small-team, funded entity. Currently, this team has been formulated with a diverse skillset to meet the requirements of its stakeholders. The SKC and Strat Comm team has used its skills to produce over 260 recorded knowledge captures of subject-matter experts (SMEs) and photoshoots. These knowledge captures are in the form of photographs, lectures, workshops, vignettes, videos, and interviews containing essential space-related knowledge. To help educate the space community and public, this trove of information (e.g., videos of world-class facilities, photographs, and SME lectures), produced inside NASA Johnson Space Center, can be obtained through various sources. During Fiscal Year 2024, the SKC and Strat Comm team will focus on several initiatives. Examples of initiatives include the following: 1) share lessons learned during NASA’s internal venues such as Safety & Health Day and Day of Remembrance; 2) share knowledge with the public, educators, and students through a media production titled Exploring the Moon; and 3) highlight NASA’s unique capabilities and expertise in a video series titled What’s Behind This Door? This paper discusses the team's approach, unique capture capability, initiatives, and much more.
NASA is returning to the Moon to stay. To establish a sustained lunar presence, astronauts will pack spacesuits, surface-mobility tools, rovers, and decades’ worth of knowledge. The U.S. Spacesuit Knowledge Capture (SKC) and Strategic Communications (Strat Comm) team is specialized in capturing, preserving, and sharing space-related knowledge with NASA scientists, technicians, engineers, vendors, and the public to support space exploration. Since the SKC Program’s 2007 inception, its focus has been to capture and share valuable spacesuit-related knowledge with the NASA community. As the program evolved, its notoriety, funding, scope, and staffing expanded from a one-person, part-time, unfunded operation to a small-team, funded entity. Currently, this team has been formulated with a diverse skillset to meet the requirements of its stakeholders. The SKC and Strat Comm team has used its skills to produce over 260 recorded knowledge captures of subject-matter experts (SMEs) and photoshoots. These knowledge captures are in the form of photographs, lectures, workshops, vignettes, videos, and interviews containing essential space-related knowledge. To help educate the space community and public, this trove of information (e.g., videos of world-class facilities, photographs, and SME lectures), produced inside NASA Johnson Space Center, can be obtained through various sources. During Fiscal Year 2024, the SKC and Strat Comm team will focus on several initiatives. Examples of initiatives include the following: 1) share lessons learned during NASA’s internal venues such as Safety & Health Day and Day of Remembrance; 2) share knowledge with the public, educators, and students through a media production titled Exploring the Moon; and 3) highlight NASA’s unique capabilities and expertise in a video series titled What’s Behind This Door? This paper discusses the team's approach, unique capture capability, initiatives, and much more.
Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transformative applications of machine learning to critical scientific use-cases more fragmented and less clear in pathways to impact. This paper introduces an ontology for scientific benchmarking developed through a unified, community-driven effort that extends the MLCommons ecosystem to cover physics, chemistry, materials science, biology, climate science, and more. Building on prior initiatives such as XAI-BENCH, FastML Science Benchmarks, PDEBench, and the SciMLBench framework, our effort consolidates a large set of disparate benchmarks and frameworks into a single taxonomy of scientific, application, and system-level benchmarks. New benchmarks can be added through an open submission workflow coordinated by the MLCommons Science Working Group and evaluated against a six-category rating rubric that promotes and identifies high-quality benchmarks, enabling stakeholders to select benchmarks that meet their specific needs. The architecture is extensible, supporting future scientific and AI/ML motifs, and we discuss methods for identifying emerging computing patterns for unique scientific workloads. The MLCommons Science Benchmarks Ontology provides a standardized, scalable foundation for reproducible, cross-domain benchmarking in scientific machine learning. A companion webpage for this work has also been developed as the effort evolves: https://mlcommons-science.github.io/benchmark/
Several cities in Malaysia have established plans to reduce their CO2 emissions, in addition to Malaysia submitting a Nationally Determined Contribution to reduce its carbon intensity (against GDP) by 45% in 2030 compared to 2005. Meeting these emissions reduction goals will require a joint effort between governments, industries, and corporations at different scales and across sectors. In collaboration with national and sub-national stakeholders, we developed and used a global integrated assessment model to explore emissions mitigation pathways in Malaysia and Kuala Lumpur. Guided by current climate action plans, we created a suite of scenarios to reflect uncertainties in policy ambition, level of adoption, and implementation for reaching carbon neutrality. Through iterative engagement with all parties, we refined the scenarios and focus of the analysis to best meet the stakeholders’ needs. We found that Malaysia can reduce its carbon intensity and reach carbon neutrality by 2050, and that action in Kuala Lumpur can play a significant role. Decarbonization of the power sector paired with extensive electrification, energy efficiency improvements in buildings, transportation, and industry, and the use of advanced technologies such as hydrogen and carbon capture and storage will be major drivers to mitigate emissions, with carbon dioxide removal strategies being key to eliminate residual emissions. This study highlights the participatory process in which stakeholders contributed to the development of the model and guided the analysis, as well as insights into Malaysia’s decarbonization potential and the role of multilevel governance.
The Y-12 National Security Complex (Y-12) operates seven diverse enriched uranium processing facilities and maintains a comprehensive nuclear criticality safety (NCS) program. Given the magnitude of the hazard, the NCS program receives significant oversight. Describing the scope and health of the Y-12 NCS program to sometimes non-expert stakeholders with limited time had historically focused on recent events and “hot topics” which did not always convey sufficient context (i.e., how to differentiate between an individual performance issue and a systemic concern). Y-12 created the NCS Program Health (NCSPH) Model to provide a complete and holistic framework to quantify and communicate NCS program health. The model is constructed in a tiered fashion with the top tier broken into three (3) Tier 2 elements, fourteen (14) Tier 3 elements, and 104 Tier 4 and 5 elements. This granularity ensures every feature of the NCS Program is accounted for and stakeholders can see how these features support the collective whole. The model is used as the framework for NCS communications including program plans, reports, health surveys, and meeting agendas. The model has substantially improved stakeholder appreciation for the entirety of the NCS program and how events and assessment results factor into an overarching conclusion on NCS program health. While some details are Y-12 specific, the NCSPH model can easily be tailored for any other site with an NCS program.
With the establishment in 2018 of the Los Alamos Legacy Cleanup Contract, the Department of Energy's (DOE) Environmental Management Los Alamos Field Office (EM-LA) and its contractor Newport News Nuclear BWXT Los Alamos (N3B) embarked on an expansive program to engage a broad range of local and regional stakeholders in the decision-making process for cleanup of legacy wastes (pre-1999) at the Los Alamos National Laboratory (LANL). From the inception of LANL during the Manhattan Project until the issuance of the new cleanup contract, the site was managed by a single management and operations (M and O) contactor whose main focus was national security. Segmenting from laboratory operations a separate cleanup contract managed by DoE's Office of Environmental Management (EM) brought new opportunities for the cleanup of legacy waste. That change likewise has opened new opportunities for engaging the community on legacy cleanup decisions. The new legacy cleanup contract - an unclassified contract - emphasizes public outreach, marking a cultural shift in how stakeholders in Los Alamos and throughout northern New Mexico are engaged. Without the constraints of classified work, EM-LA and N3B have increased the level of transparency with the public regarding cleanup activities. EM-LA and N3B have crafted an approach that best meets the values, priorities and needs of the neighboring communities by adapting the model successfully employed at Rocky Flats. Through enhanced stakeholder involvement, EM-LA will implement decisions that have considered broad input and deliver a safe and efficient cleanup of the site. EM-LA and N3B began the expanded stakeholder outreach program in January 2019, leading up to four Environmental Management Cleanup Forums held in the summer of 2019. EM-LA and N3B are now in the process of initiating a series of meetings with various stakeholder groups to determine their 'values' as they relate to cleanup using a novel approach that elicits and records values. The entire process is documented, ensuring that should questions arise about how, when and why decisions were made, they can be easily addressed. In addition to other public meetings to present cleanup scenarios and solicit active public involvement in cleanup decisions, the EM-LA and N3B approach involves discussions with stakeholders who want to explore specific technical issues and cleanup discussions in depth. EM-LA and N3B are also evaluating and implementing a variety of communications tools to help ensure the engagement plan sets the gold standard for transparency in legacy cleanup and stakeholder involvement. (authors)
Much of solar activity within a sunspot cycle occurs as bursts, or 'seasons' of strong activity over several months, separated by periods of much less activity. The most important space weather effects occur during these bursts. Previous modeling and forecasting efforts have focused on time-scales of hours-to-days and decades-to-centuries. The recent discovery of Rossby waves in the Sun, together with recently developed global models of solar MHD Rossby waves and their interactions with differential rotation and spot-producing magnetic fields, reveal the opportunity to simulate and predict the occurrence, strength and location of enhanced activity bursts a few weeks up to several months in advance. We now have a golden opportunity to fill in this gap in time-scales of forecasting space weather. This requires a) continuous observations of solar Rossby waves by various techniques; b) development of coupled nonlinear MHD models that simulate both global Rossby waves and the much smaller spatial scale emergence of new active regions; c) application of advanced data assimilation techniques to couple surface observations to update the model-system to integrate forward in time for creating forecasts months ahead. Then it will be possible to build operational prediction models to meet the needs of customers and stakeholders, including support of future NASA missions, regarding what kind and level of space weather to expect a few weeks to several months ahead.
The federal government is the nation's largest energy consumer. Mandated by law, the Federal Energy Management Program (FEMP) works with its stakeholders to enable federal agencies to meet energy-related goals, identify affordable solutions, facilitate public-private partnerships, and provide energy leadership to the country by identifying government best practices.
A fast-tracked multifaceted approach that integrated NASA, industry, and academia was successfully executed to advance the novel concept of radiation pressure by means of a thin diffractive film. This pioneering new approach to light sailing was found to offer advantages over reflective sails - especially for missions that include close orbits or a close fly-by of the sun.The research effort included experiments, numerical modeling, and an "incubator meeting" that brought together over 35 researchers and stakeholders to uncover some of the most feasible means of advancing both the TRL and mission capabilities of diffractive sailcraft. One of the outcomes of the incubator meeting was to focus this Phase I research on a solar polar orbiter mission for heliophysics experiments. NASA decadal surveys and other reports have repeatedly pointed out that scientists have only a paucity of information about the sun beyond the ecliptic plane. The TRL has been advanced from 1 to 3 during this Phase I research with the help of experiments that have verified the predicted force and mechanical control afforded by diffractive sails. Knowledge gained from the experiments and numerical models was not only disseminated in peer reviewed publications and conferences, but it also resulted in a patent disclosure.
This presentation discusses two efforts of the NARI AI/ML Intern team during the Fall 2021 OSTEM Internship term. For Letters of Agreement (LoA), we have studied how LoAs are structured and explored the question ‘What is an LoA constraint?’ To do this, our approach is data-driven, iterative, and assisted by machine learning when available. In this presentation, we will walk through our tasks of manually scanning through documents, performing a preliminary entity labelling task, and our unsupervised analysis on LoA procedures sections. After this research phase, we define the smallest constraint unit in an LoA, and start to perform entity extraction. Looking towards constraint extraction, we are also exploring the use of a one-class support vector machine (OneClassSVM) model to identify patterns within the data. The second effort of our team this term is focused on Air Traffic Control System Command Center (ATCSCC) advisory meetings, and the subsequent advisory documents that get published from their content. These advisory documents are important to give readily accessible summaries of daily operations, so that data centers, airline officials, and other stakeholders can easily understand the context of these meetings in real time. In applying machine learning to this scenario, two natural language processing tasks are used. First is developing machine learning models to convert the meeting speech data into text. With this text, use of extractive and abstractive text summarization models are used to automatically generate preliminary versions of the advisory documents.
As atmospheric greenhouse gas concentrations and the resulting social costs rise, it is increasingly important to conduct research on technical approaches to remove carbon dioxide from the atmosphere at the gigaton scale. One highly uncertain but perhaps plausible solution is ocean carbon dioxide (CO2) removal (CDR), which encompasses a suite of proposed techniques for increasing the net flux of CO2 from the atmosphere to the ocean. As reflected in a 2022 National Academy of Sciences report, interest in ocean CDR is rapidly growing, and new stakeholders from a wide range of fields want access to air-sea CO2 flux and ocean carbon information to understand the potential risks and benefits of ocean CDR. Moreover, there is an urgent need for research to develop a scientific basis to support these societal needs. At the same time, there is a growing need to vision a global carbon monitoring system that can quantify natural and anthropogenic perturbations in the air-sea CO2 flux to quantify and attribute the impacts of ocean CDR efforts on the ocean carbon sink. Here, we propose to present nascent efforts at the Ames Research Center to leverage NASA's unique remote sensing and computational capabilities and expertise in basic and applied Earth science to meet the needs of the ocean CDR stakeholder community as represented by the OceanVisions network. Specifically we aim to develop a global ocean CDR scenario explorer with two objectives: 1) to make quantitative estimates of the air-sea CO2 flux and its uncertainty more accessible to a wide range of users, and 2) to enable these diverse users to interactively explore the uncertain impacts on air-sea CO2 fluxes in a wide range of ocean CDR scenarios. The information would be provided to users via a public graphical user interface, and the results would be based on a new synthesis of scientific observations and knowledge in a global ocean mixed layer inverse model run on supercomputers. The proposed tool aims to fill a unique and valuable niche for users and the scientific community by balancing tradeoffs between user interests, scientific knowledge, and technical capabilities. We will conclude by visioning the potential implications for a global carbon monitoring system in a world with gigaton scale ocean CDR.