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At least 127 records · Page 7

Industrial Scale Demonstration of Smart Manufacturing, Achieving Transformational Energy Productivity Gains

While many U.S. manufacturing operations utilize optimization for individual unit processes, smart manufacturing (SM) systems that integrate manufacturing intelligence in real time across an entire production operation are rare in large companies and virtually nonexistent in smaller organizations. One example of an area where SM systems can be applied is in the management of waste heat. A smart system that not only seeks to recover waste heat, but also to use energy more efficiently, is a more cost-effective approach because energy use, waste energy loss and product output are optimized together. The overall objective of this project was to develop an industry-accepted SM Platform that enables smart systems by accelerating and lowering the cost of development and implementation scales for use in diverse sets of manufacturing industry sectors, manufacturing operations and company sizes. Specifically, the project (1) designed and demonstrated the application of a prototype SM Platform for two diverse commercial test beds, (2) demonstrated at one and estimated for the other test bed the potential reduction in waste heat generation, and (3) worked with leading automation vendors to catalyze low-cost commercialization of the technology developed. The Smart Manufacturing Application and Data Platform (SM Platform) developed during this project is an innovative approach that marries IT virtualization technologies with operational data and modeling system requirements to construct a ready-to-go platform for enabling sensor – data – modeling – actuation systems, driven by real-time plant data and performance metrics. This SM Platform allows manufacturing organizations, regardless of their industry or size, to assemble new operational systems at a much lower cost, optimizing process knowledge and improving energy productivity by integrating with existing process control and automation systems. Also developed during this project is a methodology for developing energy productivity metrics that use real-time process information and provide comparisons for potential, practical, and actual performance, as well as examples and the methodology for provisioning of an Energy Dashboard that displays alternatives to optimize energy use within specific business contexts aimed at helping improve energy productivity and reduce emissions in U.S. manufacturing. The SM Platform designed utilizes current manufacturing, operational and IT industry standards. The platform can be used to expedite sensor – data – model – actuation system development and deployment in a cloud-based environment using a novel end-to-end workflow as a service capability that is already integrated with data connection, ingestion, and contextualization services. In addition, the methodology for development of energy productivity metrics and reusable application templates called Toolkits have been defined so that they can be tailored to a specific business situation. The project’s two test beds, one in a hydrogen production plant and the other in a forging, heat treatment, and machining operation, utilize sensor-driven modeling, measurement, and simulation systems. In these two test beds, the implementation of these as a comprehensive system has made it possible for energy productivity to be managed in real-time throughout the plant and enterprise using an energy dashboard. The development pathway to direct machine actuation was articulated. During the first year, the project team developed the most highly instrumented steam-methane reformer (SMR) in the U.S. using infrared cameras and thermocouples. Real-time data are streamed to high fidelity and reduced-order models for analysis. A high fidelity CFD model, parallelized in the SM Platform cloud environment, was demonstrated for operational use. This is the first real-time operational productivity system of a steam methane reformer using both high fidelity and data-driven models together in a real time situation to balance natural gas flow to the reformer tubes. An infrared camera system was developed and shown to outperform thermocouples in coverage, accuracy, reliability, and cost effectiveness for use in a high temperature, harsh environment. A forging, heat-treating, machining line operation test bed includes new instrumentation for data capture analysis, modeling, and simulation and will be integrated into SM Platform workflows so that business performance can be managed in real-time. Composable metrics were developed using the SM Platform to drive improvements in energy productivity, environmental performance, safety, asset management, costs, and overall operations. A major focus of the SM systems developed was optimization of energy productivity at the enterprise level, which resulted in reductions in waste heat generation for the two project test beds. Reductions in the waste heat generated will result in greater energy savings than will waste heat recovery due to inefficiencies in the heat transfer process. For the SMR test bed an efficiency improvement of 1 – 2% with a corresponding waste heat reduction of 5% were demonstrated. It should be noted that the test bed SMR unit had an efficiency of more than 90% as compared to 70 to 80% efficiencies of most SMR units. So implementation of similar SM systems at typical SMR units could yield more than 15 to 25% waste heat reductions. Similarly the forging, heat-treating, machining line operation test bed is a DOE AMO Platinum Level Superior Energy Performance plant with a much higher than typical overall operating efficiency. For this test bed heat integration using recuperators is estimated to result in fuel savings of 15.9% due to waste heat recovery compared to the base case without recuperators with negligible changes in part exit conditions. This results in an 18.9% energy efficiency improvement. The project team estimates that broad multi-sector adoption of the developed technology could reduce waste heat generation by an amount equivalent to 1.3% of total U.S. energy use. This waste heat reduction is associated with 69 million tons reduction in annual carbon dioxide emissions. Other potential benefits include reducing process times, solid and liquid wastes, environmental impacts, and water use. It is estimated that this investment has an anticipated payback period of less than one year in energy-intensive applications. The SM Platform could also reduce the costs of deploying SM systems by 50% in manufacturing operations. Commercialization of the SM Platform will require significant cross-industry collaboration and technology provider market changes. Steps taken toward commercialization included 1) analyzing savings from deployment of the SM Platform and 2) holding SM Platform workshops, webinars, and conferences with targeted individual industry segments, with a focus on small and medium-sized organizations. An upgraded community website has been established as a portal for accessing and distributing information. Most importantly, the SM platform enabling technology developed during this project and its commercialization will continue, evolve and expand under the Clean Energy Smart Manufacturing Innovation Institute (CESMII).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

220EV Box Truck Telemetry Dataset

This dataset contains telemetry data for the 520EV refuse trucks accessed through a Veracity data platform as provided by Peterbilt. The dataset contains driving data on both electric trucks used by SWS. Data were recorded at an hourly resolution and contain energy use data while driving and idling, distance driven (miles), and driving speed (miles per hour). This dataset also contains charging data on the fast charger in kilowatt-hours for each hour.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

520EV Refuse Truck Telemetry Dataset

This dataset contains telemetry data for the 520EV refuse trucks accessed through a Veracity data platform as provided by Peterbilt. The dataset contains driving data on both electric trucks used by SWS. Data were recorded at an hourly resolution and contain energy use data while driving and idling, distance driven (miles), and driving speed (miles per hour). This dataset also contains charging data on the fast charger in kilowatt-hours for each hour.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Data relay system specifications for ERTS image interpretation

Experiments with the Data Collection System (DCS) of the Earth Resources Technology Satellites (ERTS) have been developed to stress ERTS applications in the Earth Resources Observation Systems (EROS) Program. Active pursuit of this policy has resulted in the design of eight specific experiments requiring a total of 98 DCS ground-data platforms. Of these eight experiments, six are intended to make use of DCS data as an aid in image interpretation, while two make use of the capability to relay data from remote locations. Preliminary discussions regarding additional experiments indicate a need for at least 150 DCS platforms within the EROS Program for ERTS experimentation. Results from the experiments will be used to assess the DCS suitability for satellites providing on-line, real-time, data relay capability. The rationale of the total DCS network of ground platforms and the relationship of each experiment to that rationale are discussed.

Daniel, J. F.↗

Challenges for monitoring and data analytics in a leadership public data repository

The availability and disposition of data has assumed increasing importance in large-scale computational science. Data repositories are evolving to meet new classes of requirements: compliance with government access guidelines, support for reproducibility of experimental results, and long-term availability of data products. The Constellation public data repository at the Oak Ridge Leadership Computing Facility faces these issues while being situated in one of the most productive data centers in the world. While monitoring and operational data analysis are ingrained in the operation of the OLCF’s large-scale high performance computing platforms, data repositories do not have this history of support. Problems faced by Constellation range from data size (over 7 petabytes in current holdings) to analytic complexity (detailed curation is both absolutely necessary for many data sets and absolutely impossible for humans to accomplish in any practical manner) to deployment environment (OLCF storage resources are oriented toward the needs of the compute platforms). In this paper we describe some of the challenges for collecting monitoring and analytic data from a leadership public data repository. We also discuss various strategies we are pursuing in order to address these challenges, from manual data collection to plans for introducing machine learning-based curatorial techniques.

Widener, Patrick [ORNL] (ORCID:0000000258820816)↗

Accelerated data-driven materials science with the Materials Project

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

Horton, Matthew K↗

Automation of Data Collection Methods for Online Monitoring of Nuclear Power Plants

Maintenance activities in the United States nuclear power industry are mainly centered on preventive and corrective maintenance. Predictive maintenance, which is a variable time basis maintenance strategy that is dependent on the condition of the equipment, is less utilized but potentially more effective. In the current maintenance practices, the nuclear power plants rely on manual processes to supply information to a human decision making process. The plants have several processes that generate information that is not typically used beyond the intended target for collecting that information. This information is a source of data that is manually collected by labor intensive tasks. The data is therefore expensive to collect, yet limited in use. The industry has realized the opportunity present in reducing the labor intensive tasks by automating them and increasing the uses of the data collected by either developing advanced predictive maintenance methods using data-driven decision making that could span from trending to machine learning and artificial intelligence. This shift in maintenance strategy may result in high cost saving impact. In addition to reducing labor requirements and frequency of maintenance activities, this shift in strategy will reduce the materials portion of the operations and maintenance cost as well. The aim of this specific effort is to focus on the automation of data collection process. It is part of a series of planned efforts to target multiple elements of migrating the current preventive maintenance approach in nuclear power plants to a data-driven approach. These elements include data collection, management, analysis, visualization, in addition to value analysis and change enabling. The other five elements of migration were not considered because they are planned to be explicitly studied in multiple parallel efforts. Challenges including regulations requirements or limitations are planned in a future effort that focuses on change enabling. The risk and benefit/cost of the approach shift is planned in another effort targeted towards value. The storage, communication, computational power, data architecture, and protection are part of the information infrastructure and are planned in dedicated data management effort. The best means to present the data for assisted human decision making is planned in a data visualization effort. The data interpretation and patterns recognition for machine decision making is planned through multiple efforts in data analytics over the following years. This work identified seventeen data sources collection methods in nuclear power plants that could be automated for an integrated data platform that enables comprehensive and informed decision making. For each of the identified seventeen data sources, the base, modern, and state of the art states were described. The current state of data collection used by most utilities was identified as the "base state". The current achievable state was identified as the "modern state", a state that has either been achieved by at least some US utilities or in other industries. The future state that requires research and development to reach was identified as the "state of the art" state. The described states used an out-of-the-box thinking approach. This effort found that there is a hug potential for task automation and data fidelity improvements by evolving the current industry data collection methods. The majority of the processes analyzed were found to be manual and labor intensive and fell in the base state. As a result, the modern state often targeted reducing the manual logging efforts by using electronic and semi-intelligent means or complementing the work with sensors and mobile or fixed technologies that can reduce the demand of labor activities. The state of the art state involved applications advanced methods of data correlation, data mining, and machine learning along with increasing the spatial distribution and fidelity of data, coupling multiple data sources, and enabling the use of sophisticated mobile devices and machine comprehension methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Enabling Data Exchange and Data Integration with the Common Information Model: An Introduction for Power Systems Engineers and Application Developers

The Common Information Model (CIM) is an open-source information model that is used to model an electrical network and the various equipment used on the network. CIM is widely used for data exchange of bulk transmission power systems and is finding increasing use for distribution systems. Use of a non-proprietary information model (such as CIM) that has been agreed upon and adopted by numerous utilities, vendors, and researchers allows significant reduction in the effort and cost of data integration. Likewise, adoption of open data platforms built around the CIM increases available functionalities for managing and optimizing the smart grid of the future. This report is intended as an introduction to CIM for utility engineers, power systems researchers, and application developers, providing a broad view of the CIM and how particular profiles can be adapted for various use cases. Unlike most other CIM introduction documents and the International Electrotechnical Commission (IEC) standards (which are mostly targeted to an audience of data scientists, enterprise database managers, and platform developers), this report is intended for users of traditional power systems analysis software and other readers without any prior experience with canonical information models, data profiles, or UML modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Open Source Platform for Live Processing of High Data Rate Electron Microscopy (SBIR Phase I Award Topic 15, Subtopic B, Final Scientific / Technical Report)

4D scanning transmission electron microscopy (STEM) is experiencing a revolution both in terms of data size, and data veracity, as are many other areas of experimental data acquisition. This presents enormous opportunities to make new scientific discoveries, but is also hamstrung by aging software that is no longer fit for purpose. Experimental data acquisition platforms must make the same investments in software infrastructure that the simulation community made decades ago to take advantage of high-performance computing (HPC) if they hope to realize the full potential of the latest advances made in detector technology. This requires rethinking the design and deployment of hardware, moving away from small, fixed commodity computing capabilities with closed interfaces to open, modular, scalable systems designed for parallelism and throughput. The technical approach will leverage best-in-class open source technologies, and develop new approaches strategically to build an open source platform for experimental data acquisition targeting high data rate electron microscopy. The project will develop a software platform that uses the power and performance of modern HPC resources to ingest the large volume of data produced by electron microscopes using direct electron detectors. A set of configurable pipelines will be used to enable the processing to be tailored to the problem being investigated with builtin visualization of data at interactive rates. This project will leverage investments made by the DOE and other agencies to provide a powerful software platform for the experimental data community working with most powerful electron microscopes, such as 4D STEM detectors. It will create a platform that serves as a reference for the community. Permissive open source licensing will make the platform available to all, with a simple deployment strategy offering the ability to keep data close to where it is generated. This advanced software platform will require customization for each deployment, creating opportunities for software services that are typically offered to extend open source software platforms.

97 MATHEMATICS AND COMPUTING↗

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗

Advancing Open Science Through Innovative Data System Solutions: The Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)'s Data Ecosystem

Collaborative open science practices are changing the way research is conducted. These changes affect how scientists work together on data, code and information. Data systems enhance open science by offering forward thinking technological solutions, such as providing data and computation on the cloud, to enable collaboration, sharing and analysis. In this paper, we present our vision for a conceptual data system on the cloud that enables open science. We also present our work on the Multi-Mission Algorithm and Analysis Platform (MAAP)which has served as a pathfinder data system for this conceptual approach.

Kaylin Bugbee↗

Open and Extensible Control & Analytics Platform for Synchrophasor Data (Final Report)

The objective of the Open and Extensible Control and Analytics (openECA) Platform for Phasor Data project was to develop an open source software platform that significantly accelerates the production, use, and ongoing development of real-time decision support tools, automated control systems, and off-line planning systems that (1) incorporate synchrophasor data and (2) enhance system reliability while enabling the North American Electric Reliability Corporation (NERC) operating functions of reliability coordinator, transmission operator, and/or balancing authority to be executed more effectively.

99 GENERAL AND MISCELLANEOUS↗

Advanced study of global oceanographic requirements for EOS A/B: Technical volume

Characteristics of the ocean are considered in terms of U.S. social, scientific and ecomomic priorities and in terms of the measurements that can best be made from a spacecraft. The kinds of information needed to advance the basic ocean sciences, to improve marine transportation and fisheries operations, and to provide information for pollution control are discussed. These information needs were related to sensor concepts and an optimum sensor complement is presented, together with orbital considerations. The data-gathering capabilities of an oceanographic spacecraft were considered in relation to those of terrestrial oceanographic programs, using airborne, surface, and submarine platforms. Data management problems are discussed and are considered to be solvable with current technology.

Source record↗

Using KBase to Assemble and Annotate Prokaryotic Genomes

The DOE Systems Biology Knowledgebase is an open–access bioinformatics software and data platform for analyzing plants, microbes, and their communities. KBase enables scientists to create, execute, collaborate on, and share reproducible analyses of their biological data in the context of public data and private collaborator data. For microbiologists researching prokaryotes, KBase offers analysis tools for performing quality control and assessment of Next–Generation Sequencing reads, de novo assembly, genome annotation, and tools for analyzing structural and functional features of genomes. As a result, this unit demonstrates an example workflow for taking a comparative and iterative approach to assembly and annotation of prokaryotic genomes using KBase that can be used by microbiologists seeking to perform isolate analysis in a rapid and reproducible fashion.

59 BASIC BIOLOGICAL SCIENCES↗

Equitable Energy Metrics for Integration into Building Performance Standard Tracking Platforms: Preprint

Building Performance Standards (BPS) are being adopted globally and in the United States of America, where 14 different states and jurisdictions have a policy in place and many others are under development (Department of Energy (DOE) 2023). Accurate and equitable data sources are essential to make informed decisions about focusing investment on upgrading buildings to meet jurisdictional goals. There have been multiple new tools developed related to Energy Equity and Environmental Justice (EEEJ) and the resulting datasets need to be integrated into large building port-folios for quick access and better scalability. Integrating EEEJ data in a user-friendly format can help decision makers more quickly assess impacts and analyze the multitude of potentially significant metrics for which there is not yet consensus. In the U.S. and Canada, many BPS ordinances rely primarily on ENERGY STAR Portfolio Manager (ESPM) to capture building characteristics and energy and water consumption data. These datasets can then be imported into city-specific building tracking tools like the Standard Energy Efficiency Data Platform (SEED). Crucially, BPS decision makers require an efficient means of identifying buildings in priority communities to effectively allocate resources and funding. This process must integrate seamlessly with existing jurisdictional toolsets for optimal utility. This paper will demonstrate, for the case of Washington D.C.'s (the District) data, a workflow that provides actionable data for building upgrade investment prioritization in disadvantaged communities.

BPS↗

Advanced data analysis in inertial confinement fusion and high energy density physics

Bayesian analysis enables flexible and rigorous definition of statistical model assumptions with well-characterized propagation of uncertainties and resulting inferences for single-shot, repeated, or even cross-platform data. This approach has a strong history of application to a variety of problems in physical sciences ranging from inference of particle mass from multi-source high-energy particle data to analysis of black-hole characteristics from gravitational wave observations. The recent adoption of Bayesian statistics for analysis and design of high-energy density physics (HEDP) and inertial confinement fusion (ICF) experiments has provided invaluable gains in expert understanding and experiment performance. In this Review, we discuss the basic theory and practical application of the Bayesian statistics framework. We highlight a variety of studies from the HEDP and ICF literature, demonstrating the power of this technique. Due to the computational complexity of multi-physics models needed to analyze HEDP and ICF experiments, Bayesian inference is often not computationally tractable. Two sections are devoted to a review of statistical approximations, efficient inference algorithms, and data-driven methods, such as deep-learning and dimensionality reduction, which play a significant role in enabling use of the Bayesian framework. We provide additional discussion of various applications of Bayesian and machine learning methods that appear to be sparse in the HEDP and ICF literature constituting possible next steps for the community. We conclude by highlighting community needs, the resolution of which will improve trust in data-driven methods that have proven critical for accelerating the design and discovery cycle in many application areas.

47 OTHER INSTRUMENTATION↗

The Role of Mobility Data Hubs in an Integrated Decarbonized Transportation Future

The landscape for connected mobility ecosystems is evolving rapidly as information and communication technologies lower the cost and complexity of connecting people to places, integrating transportation modes and collecting data regarding such movements. These developments have been key to unlocking new business opportunities, particularly through mobility services. While the mechanisms for data collection, processing, and transfer have made significant advances in the past decade, the broader landscape of mobility data architectures and data users remains largely unresolved. It is unclear as to whether the result will converge towards a framework that resembles a coherent quilt or a disjointed patchwork of competing visions. Initial approaches to mobility data collection and provisioning have been largely siloed - by mode or software - or held for exclusive use, however several key players are quickly realizing the need and opportunities enabled through integrated mobility data eco-systems, or mobility data hubs as referred to in this paper. As the business case for hosting mobility data hubs evolves, there is great uncertainty regarding their impact to either advance or exacerbate sustainable mobility (e.g., seamless connectivity across modes, decreased energy consumption and greenhouse gas emissions, etc.). Groups such as the United Nations and World Bank have identified data platforms as a key enabler of realizing environmental and social benefits. If designed with decarbonization in mind, we hypothesize that enhanced observability provided by these ever-expanding mobility data hubs can facilitate energy and emissions reductions that are otherwise limited by transactional barriers and knowledge asymmetry that is inherent to a more siloed approach. In this sense, integration of mobility data can help to create a competitive playing field where value is not determined by exclusivity of data, but rather the quality and uniqueness of a given service. The goals of this paper are to 1) identify key players and data architectures that are emerging in a service-based mobility market, 2) explore several use cases where mobility data hubs have enabled greater sustainability outcomes, and 3) discuss key issues that will need to be resolved to fully leverage emerging mobility data hubs towards a sustainable transportation future.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

Data mining the missing ordered phases of Li/Na metal oxides

Data-driven discovery of Li-ion and Na-ion battery materials has been pioneered by generic materials data platforms such as the Materials Project. After decades of progress, it is timely to ask whether there remain underexplored compositional spaces. Here, in this work, we present a systematic data-mining effort to uncover missing ordered binary, ternary and quaternary Li/Na-containing metal oxides using high-throughput density functional theory (DFT). Building on 19,120 stable and metastable oxides entries from the Materials Project, we performed 13,245 additional calculations through isovalent substitutions of known ground states, experimentally reported compounds, and specific prototype structures. Our study identifies 36 new ground states within the GGA/GGA + U convex hull and 45 within the r 2 SCAN convex hull. Additionally, we identified 840 metastable compounds from GGA/GGA + U and 979 from r 2 SCAN that are absent in the present Materials Project databases. Moreover, we have tripled the metastable materials in compositional spaces with a molar ratio of cation/anion >1, highlighting the overlooked opportunities in this compositional space.

25 ENERGY STORAGE↗