Sandia Proprietary Information (and Third Party Proprietary Information) & Personally Identifiable Information (PII).
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Elliott, Russell D.↗
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IM3 Projected US Data Center Locations
IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830
Mongird, Kendall (ORCID:0000000328077088)↗
IM3 Projected US Data Center Locations
IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830
Mongird, Kendall (ORCID:0000000328077088)↗
Overview Report: Normal and Emergency Operation Visualization
This is an overview report to document and illustrate methods used in a project entitled “Normal and Emergency Operations Visualization” for a utility company, conducted in 2009-2010 timeframe with funding from the utility company and the U.S. Department of Energy. The original final report (about 180 pages) for the project is not available for distribution because it alludes to findings that assessed the design of an operational system that contained proprietary information; this abridged version contains descriptions of methods and some findings to illustrate the approach used, while avoiding discussion of sensitive or proprietary information. The client has approved this abridged version of the report for unlimited distribution to give researchers and collaborators the benefit of reviewing the research concepts and methods that were applied in this study.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Exploring the Possible Use of Information Barriers for future Biological Weapons Verification Regimes
This report describes a path forward for implementing information barriers in a future generic biological arms-control verification regime. Information barriers have become a staple of discussion in the area of arms control verification approaches for nuclear weapons and components. Information barriers when used with a measurement system allow for the determination that an item has sensitive characteristics without releasing any of the sensitive information. Over the last 15 years the United States (with the Russian Federation) has led on the development of information barriers in the area of the verification of nuclear weapons and nuclear components. The work of the US and the Russian Federation has prompted other states (e.g., UK and Norway) to consider the merits of information barriers for possible verification regimes. In the context of a biological weapons control verification regime, the dual-use nature of the biotechnology will require protection of sensitive information while allowing for the verification of treaty commitments. A major question that has arisen is whether - in a biological weapons verification regime - the presence or absence of a weapon pathogen can be determined without revealing any information about possible sensitive or proprietary information contained in the genetic materials being declared under a verification regime. This study indicates that a verification regime could be constructed using a small number of pathogens that spans the range of known biological weapons agents. Since the number of possible pathogens is small it is possible and prudent to treat these pathogens as analogies to attributes in a nuclear verification regime. This study has determined that there may be some information that needs to be protected in a biological weapons control verification regime. To protect this information, the study concludes that the Lawrence Livermore Microbial Detection Array may be a suitable technology for the detection of the genetic information associated with the various pathogens. In addition, it has been determined that a suitable information barrier could be applied to this technology when the verification regime has been defined. Finally, the report posits a path forward for additional development of information barriers in a biological weapons verification regime. This path forward has shown that a new analysis approach coined as Information Loss Analysis might need to be pursued so that a numerical understanding of how information can be lost in specific measurement systems can be achieved.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Comprehensive GOM Federal Waters Platform, Incident, Metocean, and Geohazard Dataset
The dataset contains integrated data from an array of disparate data sources, all spatially and temporally linked to platforms in the federal waters of the Gulf of Mexico (platform data from BSEE, 2020). Integrated data includes past reported incidents dating back to 1956 (BSEE, BOEM, MMS), metocean data (see Nelson et al. in review for source information), and geohazard data (see Nelson et al. in review for source information). Proprietary well production information was redacted from this dataset, but was used in resulting analytics.
Full System↗
Preventing Reverse Engineering of Critical Industrial Data with DIOD
Business analytics augmented by artificial intelligence and machine learning (AI/ML) have revolutionized the role of data in the modern world. In recent years, businesses have incorporated data into their decision-making process for better prediction, risk-assessment, content creation, etc. While such businesses often seek to leverage the full use of their data through third-party AI/ML services, they are often hampered by the risks of data leaks, reverse-engineering, stolen technology, etc. that often have disastrous consequences for businesses and their stakeholders alike. Thus, there arises a need for data masking prior to its transmission that obfuscates proprietary information while preserving the information relevant for AI/ML applications. In order to meet the needs of industrial data which are significantly different from those of data warehouses, previous work proposed an efficient time and space-scalable data masking paradigm known as the deceptive infusion of data (DIOD) methodology. The present work expands upon this work by leveraging existing reverse-engineering capabilities to facilitate the decomposition of industrial data into its proprietary and AI/ML-relevant parts, referred to as fundamental and inference metadata respectively. Both sets of metadata are further obfuscated in accordance with the DIOD methodology to create the DIOD rendition of the industrial data, which is rendered immune to reverse-engineering by discarding proprietary information and only preserving AI/ML-relevant information. Additionally, constraints of the original DIOD manuscript are relaxed using mutual information by configuring the methodology to the target AI/ML application to unlock the full potential of the DIOD methodology. As an example, data from a nuclear reactor is transformed into that from a nonlinear spring-mass system with different levels of data masking as required by the generic system and the target application.
97 MATHEMATICS AND COMPUTING↗
Assessment of Industrial Load for Demand Response across U.S. Regions of the Western Interconnect
Demand response (DR) has the ability to both increase power grid reliability and potentially reduce operating system costs. Understanding the role of demand response in grid modeling has been difficult due to complex nature of the load characteristics compared to the modeled generation and the variation in load types. This is particularly true of industrial loads, where hundreds of different industries exist with varying availability for demand response. We present a framework considering industrial loads for the development of availability profiles that can provide more regional understanding and can be inserted into analysis software for further study. The developed framework utilizes a number of different informational resources, algorithms, and real-world measurements to perform a bottom-up approach in the development of a new database with representation of the potential demand response resource in the industrial sector across the U.S. This tool houses statistical values of energy and demand response (DR) potential by industrial plant and geospatially locates the information for aggregation for different territories without proprietary information. This report will discuss this framework and the analyzed quantities of demand response for Western Interconnect (WI) in support of evaluation of the cost production modeling with power grid modeling efforts of demand response.
24 POWER TRANSMISSION AND DISTRIBUTION↗
The Feasibility of Moving PMU Data in the Future Power Grid
The power grid is a complex network connecting electricity providers with their consumers. With an increasing consumer base requiring more resources and the requirement to integrate significant renewable sources, maintaining the network requires new and innovative management solutions. To manage this complexity and provide precise, real-time views of the grid, Phasor Measurement Units (PMUs) are undergoing widespread deployment. These units provide measurements as often as 60 times per second, with an accurate time identifier attached to each reading, enabling real-time monitoring of the network. However, this new capability generates much more data than the current infrastructure is designed to handle. With the expectation that there will eventually be tens of thousands of PMUs monitoring the transmission lines, the power community is looking towards accumulating multiple terabytes of data per day - several orders of magnitude beyond current data acquisition rates. This has led to questions being raised in the power community about whether or not a significant research effort is required to effectively transfer the volume of information generated by these new data streams. This paper answers that question by comparing a worst-case data generation scenario with several alternative networking protocols and historical trends in protocol advancement. Based on this analysis we are able to conclude that transferring the information between the PMUs and the resulting data repositories is feasible. We recognize that there are issues beyond transferring the data that need to be addressed such as effective access to historical data, data transfer latency, cyber security, and data analysis. There is also an extensive engineering trade-off that the power companies will need to make to decide the best mix of networking protocols for their particular PMU deployments, since that requires significant assumptions about proprietary information including deployment cost, deployment schedules, PMU locations, and available resources.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Technical Note: Confirming the prescribed angle of CT localizer radiographs and c-arm projection acquisitions
Purpose: Accurate CT radiograph angle is not usually important in diagnostic CT. However, there are applications in radiation oncology and interventional radiology in which the orientation of the x-ray source and detector with respect to the patient is clinically important. The authors present a method for measuring the accuracy of the tube/detector assembly with respect to the prescribed tube/detector position for CT localizer, fluoroscopic, and general radiograph imaging using diagnostic, mobile, and c-arm based CT systems. Methods: A mathematical expression relating the x-ray projection of two metal BBs is related to gantry angle. Measurement of the BBs at a prescribed gantry (i.e., c-arm) angle can be obtained and using this relation the prescribed versus actual gantry angle compared. No special service mode or proprietary information is required, only access to projection images is required. Projection images are available in CT via CT localizer radiographs and in the interventional setting via fluorography. Results: The technique was demonstrated on two systems, a mobile CT scanner and a diagnostic CT scanner. The results confirmed a known issue with the mobile scanner and accurately described the CT localizer angle of the diagnostic system tested. Conclusions: This method can be used to quantify gantry angle, which is important when projection images are used for procedure guidance, such as in brachytherapy and interventional radiology applications.
61 RADIATION PROTECTION AND DOSIMETRY↗
Assessment of Technical Information Needs and Considerations for Front-End Activities for Molten Salt Fuel Types
This report details an assessment of the hazards and safety impacts related to front-end operations of molten salt reactors (MSR) as well as an assessment of existing safety guidance. Publicly available information was evaluated to make recommendations to the U.S. Nuclear Regulatory Commission (NRC) for guidance development and information gathering needs. Five different MSR technologies were evaluated along with information from 1960’s about the Atomic Energy Commission programs for the Aircraft Reactor Experiment and Molten Salt Reactor Experiment. Primary areas of evaluation include fuel cycle facilities, transportation and at reactor fuel mixing operations up to reactor startup. In general, there was difficulty synthesizing publicly available information due to the early stage of the MSR development and proprietary information associated with the reactor technology companies analyzed. There are a few specific recommendations from this report. One overarching paradigm shift that the NRC needs to evaluate is the possibility of collocating fuel cycle facilities with operating MSRs. Although there is no specific prohibition to this in the NRC regulations, the collocation of the fuel cycle facilities with the operating MSR presents a novel approach compared to the light water reactor baseline. More guidance and research into thorium operations and regulation is needed. The hazards associated with the stability of stored fuel constituents need to be evaluated. Some reactor fuel cycles use beryllium, and its use and the associated hazards need further analysis. NRC-approved designs for transportation packages for shipping molten salt fuels and factory-built and fueled reactors are not available.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
AGR-2 Irradiated Test Train Preliminary Inspection and Disassembly First Look
The AGR 2 irradiation experiment began in June 2010 and was completed in October 2013. The test train was shipped to the Materials and Fuels Complex in July 2014 for post-irradiation examination (PIE). The first PIE activities included nondestructive examination of the test train, followed by disassembly of the test train and individual capsules and detailed inspection of the capsule contents, including the fuel compacts and their graphite fuel holders. Dimensional metrology was then performed on the compacts, graphite holders, and steel capsule shells. AGR 2 disassembly and metrology were performed with the same equipment used successfully on AGR 1 test train components. Gamma spectrometry of the intact test train gave a preliminary look at the condition of the interior components. No evidence of damage to compacts or graphite components was evident from the isotopic and gross gamma scans. Disassembly of the AGR 2 test train and its capsules was conducted rapidly and efficiently by employing techniques refined during the AGR 1 disassembly campaign. Only one major difficulty was encountered while separating the test train into capsules when thermocouples (of larger diameter than used in AGR 1) and gas lines jammed inside the through tubes of the upper capsules, which required new tooling for extraction. Disassembly of individual capsules was straightforward with only a few minor complications. On the whole, AGR 2 capsule structural components appeared less embrittled than their AGR 1 counterparts. Compacts from AGR 2 Capsules 2, 3, 5, and 6 were in very good condition upon removal. Only relatively minor damage or markings were visible using high resolution photographic inspection. Compact dimensional measurements indicated radial shrinkage between 0.8 to 1.7%, with the greatest shrinkage observed on Capsule 2 compacts that were irradiated at higher temperature. Length shrinkage ranged from 0.1 to 0.9%, with by far the lowest axial shrinkage on Capsule 3 compacts—possibly as a consequence of lower packing fraction or larger particle size. Differences in fast neutron fluence among compacts from these four capsules had no obvious effect on radial and axial shrinkage. (The AGR 2 experiment included Capsule 1 containing French compacts and Capsule 4 with compacts made at Oak Ridge National Laboratory using South African fuel particles. Information on these two batches of AGR 2 fuel compacts is confined to restricted Appendices A and B because of proprietary information limitations.)
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
United States–Brazil Joint Study: A Preliminary Assessment of Opportunities and Challenges for Small Modular Reactors in Brazil
This report supplies the technical information to enable a broader understanding of different technology choices for nuclear reactors in Brazil. The assessment characteristics of this process include reactor technology, economic costs for deployment, and regulatory analyses of small modular reactors, advanced reactors, and microreactors. The analysis documented in this report provides economic and technological information to assist Brazil, whose official indicative scenarios show an increase in operating nuclear power from 2 Gigawatt in 2022 to 8–10 Gigawatt by 2050, in policy making and long-term energy planning. To protect company proprietary information, the information in this report was limited to publicly available information and therefore can only inform preliminary reactor technology assessments. This report does not recommend or endorse any specific reactor developer or technology.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Fuel Cell Technology Status Analysis Project: Partnership Opportunities (Fact Sheet)
This fact sheet describes National Renewable Energy Laboratory's (NREL's) Fuel Cell Technology Status Analysis Project. NREL is seeking fuel cell industry partners from the United States and abroad to participate in an objective and credible analysis of commercially available fuel cell products to benchmark the current state of the technology and support industry growth. Participating fuel cell developers share price information about their fuel cell products and/or raw fuel cell test data related to operations, maintenance, and safety with NREL via the Hydrogen Secure Data Center (HSDC). The limited-access, off-network HSDC houses the data and analysis tools to protect proprietary information. NREL shares individualized data analysis results as detailed data products (DDPs) with the partners who supplied the data. Aggregated results are published as composite data products (CDPs), which show the technology status without identifying individual companies. The CDPs are a primary benchmarking tool for the U.S. Department of Energy and other stakeholders interested in tracking the status of fuel cell technologies. They highlight durability advancements, identify areas for continued development, and help set realistic price expectations at small-volume production.
08 HYDROGEN↗
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↗
Technical Specification Surveillance Interval Extension Using Self-Diagnostics
As part of the Light Water Reactor Sustainability program, an ongoing research effort is being conducted on technical specifications surveillance interval extension of digital equipment in nuclear power plants. The research team is led by Idaho National Laboratory and includes Pacific Northwest National Laboratory, Technology Resources, and Oak Ridge National Laboratory. This research focuses on developing methods for applying the U.S. Nuclear Regulatory Commission (NRC)–approved guidance to implement a licensee-controlled, risk-informed surveillance frequency change program in digital instrumentation and control (I&C) systems that include self-diagnostics and online monitoring (OLM) capabilities. Although approved methods exist for extending technical specifications (TS) surveillance test intervals (STIs) for general equipment, including analog I&C equipment, gaps remain in technology and guidance on crediting newer digital equipment’s internal self-diagnostics and OLM characteristics. Previous research described a general methodology for crediting internal self-diagnostics for extending surveillance test intervals. The methodology used self-diagnostics to detect—and credited recovery from—failure. Self-diagnostics were also applied for performance monitoring during the extended surveillance interval. This report discusses the status of recent activities to evaluate the previously developed methodology using a pilot study. Although both a utility partner for a pilot study and a specific digital asset were identified in FY2020, delays in obtaining proprietary information resulted in a limited ability to fully evaluate the methodology, and further interactions were complicated by the COVID pandemic. Therefore, at that time, the use of public-domain information—along with current processes for surveillance interval extension through a surveillance frequency control program—identified the need to fully assess diagnostic coverage as part of the pilot study. Furthermore, self-diagnostics were also identified as a potential option to replace the drift analyses conducted as part of current STI extension procedures. In FY2022, the project was reconstituted with the industry partner, and information and data were made available by the industry partner to the research team for review. The shared information included failure event descriptions and data for a digital I&C system since its implementation, as well as recent STI extension interval reports developed by the utility partner on that digital I&C system. This report presents an evaluation of this information and data and describes an application of the proposed methodology cited above. The methodology seeks to take advantage of the self-diagnostics and OLM capabilities to reduce risk or reduce the level of qualitative monitoring assessment needed to perform a risk-informed STI extension using existing NRC approved guidance or both. Addressing these issues of STI extension by crediting self-diagnostics is likely to result in benefits for current and future nuclear power plant (NPP) operations, including lowering the barriers to adoption of digital I&C systems and increasing cost savings by deferring or eliminating unneeded preventive maintenance (tasks or checks or activities). Specifically, self-diagnostic and OLM capabilities of newer digital equipment being installed in non-safety and safety applications are designed to detect failures, provide early warning of potential failures, and notify plant operators to take appropriate action to reduce out of service (OOS) time thus protecting safety margins. Moreover, the equipment is expected to provide information that time-related operational degradation is identified early to ensure timely and planned corrective actions instead of a reactive and unplanned approach ahead of an extended-surveillance interval.
42 ENGINEERING↗
Deceptive Infusion of Data: A Novel Data Masking Paradigm for High-Valued Systems
This work addresses how analysts of a high-valued system (e.g., nuclear reactor, aircraft turbine designs) can extract findable, accessible, interoperable, and reusable scientific data for public dissemination to artificial intelligence and machine-learning (AI/ML) researchers in a manner that cannot be reverse-engineered, potentially compromising sensitive or proprietary information. State-of-the-art methods address this problem through data masking techniques, which allow access to a subset of the information while obfuscating private and potentially identifying information (e.g., personally identifying medical data). These methods are unsuitable for industrial engineering processes, where AI/ML tools need explicit access to all the data available to draw the best inference about the system to help optimize its performance and identify its vulnerabilities, etc. Our novel deceptive infusion of data paradigm provides a solution to this conundrum by developing a mathematical approach capable of concealing the identity of the system while providing full access to all the features employed by AI/ML tools to ensure their optimal performance.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗