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Bench-Scale Testing of a High Efficiency, Ultra-Compact Process for Pre-Combustion CO 2 Capture (Final Report)

The overarching objective of this bench-scale study is to field-validate the technical feasibility of a membrane- and adsorption-enhanced water gas shift (WGS) reaction process that employs a carbon molecular sieve (CMS) membrane reactor (MR) followed by an adsorption reactor (AR) for pre-combustion CO 2 capture while demonstrating progress towards achievement of the overall performance goals of CO 2 capture with 95% CO 2 purity at a cost of electricity (COE) of 30% less than the baseline capture approaches. The main goal of this bench-scale project is to advance the proposed MR-AR transformative CO 2 capture technology to a sufficient maturity level so that our present industrial partners (M&PT and Worley) can potentially adapt the technology for further small pilot-scale testing. The project began at TRL 4, as the system prototype had already been validated in the laboratory on simulated syngas (as part of project DE-FE0026423). The project ended at TRL 5, via scaling-up of the prototype system and testing it on actual syngas at a host site (CAER at the University of Kentucky (UKy)). The project was carried-out in two different phases: In Phase I (during BP1 of the project), the team designed, constructed, and assembled the bench-scale experimental MR-AR system, prepared the membranes, adsorbents, and catalysts, tested the bench-scale unit with simulated syngas to validate functionality, and prepared a preliminary TEA of the technology. In Phase II (during BP2 of the project), the team installed the unit at the test site at the UKy site and completed all utility connections and hookups, field-tested the novel MR-AR process in the bench-scale system using real syngas, collected and analyzed experimental data, and completed a detailed TEA of the technology. A key aim of the project was to identify and address the technical and process risks and to generate information to advance the technology to the next stage of development. All project milestones and success criteria were met. Specifically, the team: (i) designed, constructed and assembled a bench scale system that handled 1 – 5 scfm syngas and tested its functionality with simulated syngas; (ii) prepared high performance CMS membrane tubes (ID: 3.5 mm, OD: 5.7 mm, 30” long) that meet the target H 2 permeance ( > 1 m 3 /(m 2 .hr.bar) or (> 370.3 GPU)) and a target H 2 /CO selectivity >80 at the relevant temperature (up to 300oC) and pressure conditions (up to 25 bar) with a <10% decline in performance over each 250 hr. testing period (iii) procured commercial sour-shift catalyst in sufficient quantity and prepared up to 10 kg of pelletized adsorbent for use at relevant conditions (250oC 2.5wt.% and target sorbent attrition rate < 0.2; (iv) installed the bench-scale unit at the test site (UKy) and tested the MR-AR technology using real syngas for over 250 hr. for both static and flow experiments; (v) updated the TEA analysis based on bench-scale data and met CO 2 capture goals of 95% CO 2 purity at a cost of electricity 30% less than baseline capture approaches. The CMS membranes, HTC adsorbents and catalysts employed all exhibited very robust and stable performance during the long-term run (over a >250 hr live syngas run). Furthermore, the proposed MR-AR IGCC system achieved a LCOE with a N 2 sale price of $\$$ 30/ton, with N 2 compression (no N 2 compression) of 101.2 $\$$/MWh (95.3 $\$$/MWh), which represents a 29.8% (33.9%) LCOE reduction in the baseline IGCC with carbon capture of 144.2 $\$$/MWh. The proposed MR-AR IGCC delivers a CO 2 capture cost of 44.8 $\$$/tonne (39.9 $\$$/tonne) vs 98.1 $\$$/tonne of the baseline capture case and a net power production of 586 MWe (623 MWe) vs 556 MWe of the baseline capture case.

01 COAL, LIGNITE, AND PEAT↗

Potential Availability of Alternative Fuel to Supply Maritime Activities in Pacific Northwest Ports

The international shipping sector represented 3% of global greenhouse gas emissions in 2023 (Office of Energy Efficiency & Renewable Energy 2024). International shipping has been classified as a difficult-to-decarbonize industry (IRENA 2024). In an effort to drive decarbonization, the U.S. Department of Energy has partnered with Mission Innovation to co-lead the Zero-Emission Shipping Mission, which launched in 2021(Office of Energy Efficiency & Renewable Energy 2021). In addition, the U.S. Department of State partnered with Norway to launch the Green Shipping Challenge in 2022 (Office of the Spokesperson 2022b). As part of the ZESM and Green Shipping Challenge, the United States is collaborating with the Republic of Korea (ROK) to develop a green shipping corridor (U.S. Mission Korea 2023). The United States and ROK have conducted a pre-feasibility study as the first step in developing a green shipping corridor between the countries. The ports included in the study are Seattle, Tacoma, and Everette in the U.S. Pacific Northwest (PNW) and Busan, Ulsan, and Masan in ROK. The National Renewable Energy Laboratory's role in the study was to analyze the availability and technical potential of alternative marine fuels in proximity to U.S. PNW ports. The findings show most of the existing alternative fuel capacity within the region is from renewable diesel, biodiesel, and sustainable aviation fuel facilities. The largest growth in fuel capacity in the region by 2030 is projected to be in renewable diesel and hydrogen. The overall technical readiness of non-drop-in alternative fuel production and conversion technologies is more developed than alternative-fueled ships and associated fueling infrastructure. However, much of the fuel capacity in the region is comprised of drop-in fuels, making it technically possible to use existing infrastructure for transporting and bunkering to the existing fleet. Data to inform regional alternative fuel quantity estimations were collected from an extensive review of databases, reports, announcements, and other publicly available resources. A maturity index and sector competition factor were applied to announced fuel projects to determine the quantity of alternative fuel available to the marine sector in the region by 2030. Demand data were collected from fuel bunkering logs covering the PNW seaports (State of Washington 2021). Both supply and demand data were converted to very-low sulfur fuel oil gallon equivalents (VLSFO-GE) for better comparison. Qualitative data were gathered through interviews with stakeholders, project developers, and industry experts. Total alternative fuel capacity available to the marine sector in the region is estimated to be 824 million VLSFO-GE per year by 2030. This is sufficient to cover the requirements of a green shipping corridor between the United States and ROK. The findings from this report are being used to inform detailed feasibility studies for several U.S. PNW -ROK green shipping corridors. Updates from the U.S. PNW - ROK feasibility studies will continue to be published on Mission Innovation's green corridor tracking website (Zero Emission Shipping Mission, n.d.). In addition, this report has helped to inform further work on shipping decarbonization in the U.S. PNW, including the Pacific Northwest to Alaska Green Corridor focused on cruise vessels.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Sister Rod Destructive Examinations (FY22) Appendix F2: Evaluation of Fuel Rod Fatigue During Spent Fuel Transportation

This report documents work performed under the Spent Fuel and Waste Disposition’s Spent Fuel and Waste Science and Technology program for the US Department of Energy (DOE) Office of Nuclear Energy (NE). This work was performed to fulfill Level 2 Milestone M2SF-23OR010201024, “FY22 Report on ORNL Sibling Rod Testing Results,” within work package SF-23OR01020102 and is an update to the work reported in M2SF-22OR010201047, M2SF-21OR010201032, M2SF-19ORO010201026, and M2SF- 19OR010201028. As a part of DOE NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods, are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin (LT) Zirc-4, ZIRLO, and M5. The DEs are being conducted to obtain a baseline of the HBU rods’ condition before dry storage and are focused on understanding overall SNF rod strength and durability. Composite fuel and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition. This appendix documents an evaluation of the fatigue data to enhance the technical basis for post-storage transportation, handling, and subsequent disposition and to identify future testing needs for Phase 2 of the project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Sister Rod Destructive Examinations (FY22) Appendix F: Cyclic Integrated Reversible-Bending Fatigue Tests

This report documents work performed under the Spent Fuel and Waste Disposition’s Spent Fuel and Waste Science and Technology program for the US Department of Energy (DOE) Office of Nuclear Energy (NE). This work was performed to fulfill Level 3 Milestone M3SF-22OR010201046, “Draft FY2022 ORNL Report on High Burnup Sibling Pin Testing Results,” within work package SF-22OR01020104 and is an update to the work reported in M2SF-22OR010201047, M2SF-21OR010201032, M2SF-19ORO010201026 and M2SF-19OR010201028. As a part of DOE NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods, are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin (LT) Zirc-4, ZIRLO, and M5. The DEs are being conducted to obtain a baseline of the HBU rods’ condition before dry storage and are focused on understanding overall SNF rod strength and durability. Composite fuel and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition. This appendix documents the status of the ORNL Phase 1 DE activities related to tests of the sister rods using the Cyclic Integrated Reversible-Bending Fatigue Tester (CIRFT) in Phase 1 of the sister rod test program.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Modeling Cyber Supply Chain Incidents with Multilayered Graph Motifs

As noted within the literature, supply chain includes people and organizations---manufacturers, integrators, and third-party vendors---that are involved in one or more stages of a product lifecycle. Since supply chains, by definition, include organizations and people, supply chain risk management activities must consider dependencies between an organization's business processes and third-party resources. Just as adversarial tactics can be implemented via techniques implemented via networked computer systems, so can such tactics be expressed via legal business relationships. A cyber incident may have an exponential impact downstream, for example, by leveraging a product's distribution channel (e.g. malicious updates in SolarWinds, buggy updates in CrowdStrike). Similarly, legitimate and legal business relationships also affect the attack surface exposure of systems, enabling long-term persistence and/or unknown impacts to product quality that are hard to detect. This paper catalogs several recent digital supply chain incidents and applies a multilayered network formalism to develop structural indicators (graph motifs) that reflect potentially-adversarial behavior. Finally, we compare and contrast the characteristics of adversarial tactics (e.g. Loss of Availability, Data Collection) that leverage cyber-physical dependencies to those that leverage legal organizational relationships.

97 - MATHEMATICS AND COMPUTING↗

Sister Rod Destructive Examinations (FY23) Appendix I: SNF Aerosols Released During Rod Fracture

This report documents work performed under the Spent Fuel and Waste Disposition’s Spent Fuel and Waste Science and Technology program for the US Department of Energy (DOE) Office of Nuclear Energy (NE). This work was performed to fulfill Level 3 Milestone M3SF-23OR010201026, “FY23 M3 draft report on results from testing in FY23,” within work package SF-23OR01020102 and is an updated to the work reported in M2SF-23OR010201024, M2SF-22OR010201047, M2SF-21OR010201032, M2SF-19ORO010201026, and M2SF-19OR010201028. As a part of the DOE NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods, are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin Zirc-4, ZIRLO, and M5. The DEs are being conducted to obtain a baseline of the HBU rods’ condition before dry storage and are focused on understanding overall SNF rod strength and durability. Fuel rods and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition.This report documents the status of the ORNL Phase 1 DE activities related to the collection of SNF aerosol particles released during fuel rod fracture in 4-point bending in Phase 1 of the sister rod test program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Generation IV Benchmarking of TRISO Fuel Performance Models Under Accident Conditions Final Report

The Generation IV International Forum (GIF) is a co-operative international endeavor of fourteen members organized to carry out the research and development needed to establish the feasibility and performance capabilities of the next generation nuclear energy systems. GIF selected six reactor technologies, amongst which is the Very High Temperature Reactor (VHTR) that is primarily dedicated to the cogeneration of electricity and hydrogen. The technical basis for VHTR is the tristructural isotropic (TRISO)-coated particle fuel, the graphite as the core structure, helium coolant, as well as the dedicated core layout and lower power density to removal decay heat in a natural way. At the heart of safety features of the VHTR concept lie the TRISO fuel particles that are designed to keep their structural integrity and retain fission products at temperatures up to 1600°C. As part as the design and future operation of VHTRs, a key aspect is the accurate prediction of fuel performance under irradiation and accident conditions. Modeling and simulation allow prediction of TRISO fuel behavior when subject to neutron flux and in high temperature accident scenarios. The refinement of the fuel performance models and codes is performed by comparison to in-pile and out-of-pile experimental data that reproduce the expected irradiation conditions in high temperature gas-cooled reactors (HTGRs). Historically, the International Atomic Energy Agency (IAEA) developed a benchmark dedicated to the validation of predictive methods for fuel and fission product behavior through the Coordinated Research Program CRP-2 (IAEA, 1997). CRP-2 was later updated to cover fuel fabrication, quality assurance, irradiation performance, safety testing, and spent fuel. The scope of the resulting CRP-6 benchmarks focused on HTGR fuel performance and fission product release (IAEA, 2012). Taking advantage of additional TRISO fuel fabrication, irradiation, and safety testing campaigns, GIF launched a Generation IV Benchmarking of TRISO Fuel Performance Models under Accident Conditions in late 2015. This GIF benchmark is a three-year program steered by Idaho National Laboratory (INL, USA). The other participants include the Japan Atomic Energy Agency (JAEA, Japan) and the Korea Atomic Energy Research Institute (KAERI, Korea). The objectives of the benchmark are to: follow on the IAEA CRP benchmarks, (2) model fission product release under accident conditions, (3) compare results obtained by the fuel performance modeling codes of the benchmark participants, and (4) compare these code predictions to experimental data. Safety tests chosen for modeling include the first and second experiments of the Advanced Gas Reactor program (AGR-1 and AGR-2) and the High Flux Reactor (HFR) EU1bis experiment. This report presents the results obtained by the three research institutions using their respective fuel performance modeling codes. Comparisons of the corresponding fission product release predictions are made with experimental data from AGR-1, AGR-2, and HFR-EU1bis. The benchmark results show good agreements between all participants but also show a general trend of over-prediction of the experimental release data, which is mainly attributed to the use of over-estimated diffusion coefficients.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Adaptive Data-Driven Model Predictive Control for Heat Pipe Microreactors

To establish a technical basis for self-regulating microreactors, a model predictive control (MPC) system is investigated to proactively respond to anomalies and disturbances in anticipation of potential deviations from operating setpoints. Due to the difficulty of developing a physics-based surrogate model that can accurately match plant data in various operating conditions, machine learning algorithms are used in MPC, which allow for learning from both simulation and operation data, thus efficiently describing the targeted transient with arbitrary accuracy. However, one of the biggest concerns in applying ML algorithms like artificial neural networks (ANNs) is that the predictive capabilities of ANN are limited by training data. If there are gaps between the training and target domain, the accuracy of an ANN can degrade significantly when it is used to predict unseen data. To improve the predictive capability of ANN and enable a confident use of data-driven MPCs outside the training data, this study proposes an adaptive data-driven MPC framework. The system will monitor the discrepancy between plant responses and surrogate predictions, fine-tune the ANN-based surrogate when a large discrepancy is detected, and continue MPC operation with updated surrogates. The framework is demonstrated on a point kinetic model for microreactors. The hyperparameters of the update strategy, including layers to update, error thresholds, learning rate discount, and number of data points used for fine-tuning, are optimized so the simulated microreactor is able to follow changes in setpoint with the smallest of deviations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Adaptable Data Driven Model Predictive Control for Heat Pipe Microreactors

To establish a technical basis for self-regulating microreactors, a model predictive control (MPC) system is investigated to proactively respond to anomalies and disturbances in anticipation of potential deviations from operating setpoints. Due to the difficulty of developing a physics-based surrogate model that can accurately match plant data in various operating conditions, machine learning algorithms are used in MPC, which allow for learning from both simulation and operation data, thus efficiently describing the targeted transient with arbitrary accuracy. However, one of the biggest concerns in applying ML algorithms like artificial neural networks (ANNs) is that the predictive capabilities of ANN are limited by training data. If there are gaps between the training and target domain, the accuracy of an ANN can degrade significantly when it is used to predict unseen data. To improve the predictive capability of ANN and enable a confident use of data-driven MPCs outside the training data, this study proposes an adaptive data-driven MPC framework. The system will monitor the discrepancy between plant responses and surrogate predictions, fine-tune the ANN-based surrogate when a large discrepancy is detected, and continue MPC operation with updated surrogates. The framework is demonstrated on a point kinetic model for microreactors. The hyperparameters of the update strategy, including layers to update, error thresholds, learning rate discount, and number of data points used for fine-tuning, are optimized so the simulated microreactor is able to follow changes in setpoint with the smallest of deviations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

LandScan Mosaic

The LandScan program at Oak Ridge National Laboratory (ORNL), in collaboration with the National Geospatial-Intelligence Agency (NGA), continues to deliver the most accurate and up to date global, high resolution gridded population data. Additionally, the latest advancements in the LandScan HD methodology led to reduced latency in development of rapid updates for geopolitical events. With momentum towards reporting more up to date population estimates, feedback from the user community expressed interest in reporting population estimates in ranges - whether to express a level of uncertainty or confirm to leadership and stakeholders the modeled data are estimates. Building upon the need to understand uncertainty or confidence in the modeled data and report ranges at the global scale, LandScan Mosaic was developed. LandScan Mosaic represents the next generation of high-resolution population modeling, building upon the established success of previous LandScan HD iterations. While LandScan HD employed a deterministic big data fusion approach, LandScan Mosaic enhances this methodology by integrating advanced machine learning techniques to impute missing, yet crucial, population model parameters. This advancement allows for probabilistic modeling of building occupancy and population distribution, incorporating uncertainty quantification through Monte Carlo sampling methods. By combining big data fusion with machine learning-driven imputation and stochastic modeling, LandScan Mosaic provides a more comprehensive and robust representation of population dynamics. LandScan Mosaic will be following the in the footsteps of its longstanding counterpart LandScan Global and releasing a global gridded population raster, at the 3-arcsecond resolution. This technical report documents the current stage of development of LandScan Mosaic, detailing the methodologies and data sources behind the modeling. Stakeholders are encouraged to use this document as an authoritative reference for insight into Mosaic’s data development processes. However, readers should note that LandScan Mosaic remains in a late-stage research and development phase, and methodologies and data presented here are subject to refinements ahead of the anticipated global release in Summer 2025. Feedback and inquiries from users and stakeholders are welcomed as we continue to refine and enhance this important population resource.

97 MATHEMATICS AND COMPUTING↗

Sister Rod Destructive Examinations (FY23) Appendix E2: Four-Point Bend Fractured Specimen Images

This report documents work performed under the Spent Fuel and Waste Disposition’s Spent Fuel and Waste Science and Technology program for the US Department of Energy (DOE) Office of Nuclear Energy (NE). This work was performed to fulfill Level 2 Milestone M2SF-24OR010201024, “FY23 ORNL Testing on Sibling Pins,” within work package SF-24OR01020102 and is an update to the work reported in M2SF-23OR010201024, M2SF-22OR010201047, M2SF-21OR010201032, M2SF-19ORO010201026, and M2SF-19OR010201028. As a part of the DOE NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods, are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin Zirc-4, ZIRLO, and M5. The DEs are being conducted to obtain a baseline of the HBU rods’ condition before they are sent to dry storage and are focused on gaining an understanding of overall SNF rod strength and durability. Composite fuel and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition. This report documents the status of the ORNL Phase 1 DE activities related to the mechanical testing of selected sister rods in Phase 1 of the sister rod test program. This appendix provides images of the segments broken in four-point bending (4PB).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

State Technical Assistance - New Mexico Energy and Conservation Management Division Report [Slides]

The New Mexico Energy and Conservation Management Division (ECMD) sought technical assistance to enhance their ability to evaluate program impacts using the Low-Income Energy Affordability Data (LEAD) tool. NLR assisted ECMD in leveraging the LEAD tool to calculate and analyze energy burden across electric utility service areas, enabling them to assess program outcomes more effectively. To meet ECMD's goals, NLR developed a customized methodology to calculate utility-specific energy burden metrics using census tract data and available utility service area information from the Energy Information Administration (EIA). While acknowledging some limitations in the EIA dataset, NLR estimated the percentage of households within each service territory and incorporated relevant filters such as income, housing characteristics, and other demographics from the LEAD tool. The analysis provided ECMD with a new capability to evaluate program success based on energy savings, reductions in energy burden, and other performance indicators. The data and methodology also support discussions with utilities to improve the accuracy of service territory datasets. ECMD can use the outputs to track program effectiveness and plan future initiatives. NLR offered the possibility of follow-on work, including capacity-building for ECMD to repeat the analysis independently and the option to refine the analysis with updated service.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

An Overview of the State-of-the-Art Reactor Consequence Uncertainty Assessment Accident Progression Insights

The U.S. Nuclear Regulatory Commission (NRC) with Sandia National Laboratories (Sandia) have completed three uncertainty analyses (UAs) as part of the State-of-the-Art Reactor Consequence Analyses (SOARCA) program. The SOARCA UAs included an integrated evaluation of uncertainty in accident progression, radiological release, and offsite health consequence projections. The UA for Peach Bottom, a boiling-water reactor (BWR) with a Mark I containment located in the State of Pennsylvania, analyzed the unmitigated long-term station blackout SOARCA scenario. The UA for Sequoyah, a 4-loop Westinghouse pressurized-water reactor (PWR) located in the State of Tennessee, analyzed the unmitigated short-term station blackout SOARCA scenario, with a focus on issues unique to the ice condenser containment and the potential for early containment failure due to hydrogen deflagration. The UA for Surry, a 3-loop Westinghouse PWR with a sub-atmospheric large dry containment located in the State of Virginia, analyzed the unmitigated short-term station blackout SOARCA scenario including the potential for thermally-induced steam-generator tube rupture. These three UAs are currently documented in three NUREG/CR reports. This report provides input to planned NRC documentation on the insights and findings from the SOARCA UA program. The purpose of the summary report is to provide a useful reference for regulatory applications that require the evaluation of offsite consequence risk from beyond design basis event severe accidents. This report focuses on the accident progression and source term insights developed from the MELCOR analyses. MELCOR is the NRC's best-estimate, severe accident computer code used in the SOARCA UAs. In anticipation of the SOARCA UA insights work, NRC and Sandia benchmarked the response of the Peach Bottom model to selected reference calculations from the Peach Bottom SOARCA UA. Peach Bottom was the first SOARCA UA performed and was completed in 2015 using the MELCOR 1.8.6 code. The PWR SOARCA UAs evolved the original methodology and utilized the updated MELCOR 2.2 computer code. The Peach Bottom model has been systematically updated for other NRC research efforts and has been updated to MELCOR 2.2. computer code. The findings from the new reference calculations using the updated model with the MELCOR 2.2 code are also integrated into the report. A second objective is an assessment of the applicability of the results to the other nuclear reactors in the U.S. As the key findings are reviewed, judgments are presented on the applicability of the results to other U.S. nuclear power plants. An important objective of the SOARCA program relied on high- fidelity plant-specific modeling. However, the nature of the insights and conclusions allowed judgements to be made on the applicability of the various insights to the same general classification of plant (i.e., BWR or PWR) or the entire fleet of plants. Finally, the results from the SOARCA UA accident progression calculations contain a wealth of information not previously documented in the NUREG/CRs. This report includes new but related information that can be used to benchmark past or support future regulatory decisions related to severe accidents. The new work includes a benchmark of the NUREG-1465 licensing source term definitions, the variability of key accident progression events and timing to radionuclide release, and an improved understanding of the timing and source terms from consequential steam generator tube ruptures. iii ACKNOWLEDGEMENTS The Sandia authors gratefully acknowledge the significant technical and programmatic contributions from the NRC SOARCA team which are reflected throughout the report. Dr. Tina Ghosh has been involved throughout the SOARCA UAs, providing the primary managerial and technical oversight. The long lists of NRC and Sandia contributors from the SOARCA UAs are cited in the three NUREG/CRs and are also gratefully acknowledged by the small team of authors compiling the results of their efforts. Significant technical contributions, advice, and reviews were provided by Dr. Hossein Esmaili, Dr. Alfred Hathaway, and Dr. Edward Fuller (retired) of the NRC. Dr. Randal Gauntt (retired), Mr. Patrick Mattie, Mr. Joseph Jones (retired), and Dr. Doug Osborn from Sandia are recognized as the SOARCA UA managers guiding the past efforts. There is a comparable list of project managers at the NRC including Ms. Patricia Santiago, Dr. Salman Haq, and Mr. Jon Barr. Sadly, we have lost Mr. Charlie Tinkler and Mr. Robert Prato, who were important contributors to the original SOARCA project. Finally, Mr. Kyle Ross and Mr. Mark Leonard have also retired but were significant technical contributors. Mr. Kyle Ross was the technical lead on all three SOARCA UAs and the original pressurized water reactor SOARCA study. Mr. Leonard was the technical lead on the original boiling water reactor SOARCA study and a key contributor to the first Peach Bottom SOARCA UA. iv

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Electrical Energy Storage Data Submission Guidelines, Version 2

Energy storage technologies are positioned to play a substantial role in power delivery systems. They have the potential to serve as an effective new resource to maintain reliability and allow for increased penetration of renewable energy. However, because of their relative infancy, there is a lack of knowledge about how these resources truly operate over time. A data analysis can help ascertain the operational and performance characteristics of these emerging technologies. Rigorous testing and a data analysis are important for all stakeholders to ensure a safe, reliable system that performs predictably on a macro level. Standardizing testing and analysis approaches to verify the performance of energy storage devices, equipment, and systems when integrating them into the grid will improve the understanding and benefit of energy storage over time from technical and economic vantage points. Demonstrating the life-cycle value and capabilities of energy storage systems begins with the data that the provider supplies for the analysis. After a review of energy storage data received from several providers, some of these data have clearly shown to be inconsistent and incomplete, raising the question of their efficacy for a robust analysis. This report reviews and proposes general guidelines, such as sampling rates and data points, that providers must supply for a robust data analysis to take place. Consistent guidelines are the basis of a proper protocol and ensuing standards to (1) reduce the time that it takes for data to reach those who are providing the analysis; (2) allow them to better understand the energy storage installations; and (3) enable them to provide a high-quality analysis of the installations. The report is intended to serve as a starting point for what data points should be provided when monitoring. Readers are encouraged to use the guidance in the report to develop specifications for new systems, as well as enhance current efforts to ensure optimal storage performance. As battery technologies continue to advance and the industry expands, the report will be updated to remain current.

25 ENERGY STORAGE↗

Reducing Uncertainty of Fielded Photovoltaic Performance (Final Technical Report)

Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. The FY19-21 Reducing Uncertainty project leveraged data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Specifically, we accomplished: report on field performance and degradation rates for high-efficiency silicon (HJT, PERC, IBC) and more conventional technologies; developed automated analysis techniques to quantify system performance (performance ratio, energy yield) and production shortfalls (soiling, degradation, availability); refined the RdTools software toolkit to bring standard, validated analysis techniques to bear on third-party data; analyzed and reported on large datasets including Treasury data and Lawrence Berkeley National Laboratory's Utility-Scale dataset to expand the high-quality degradation-rate histogram published previously; worked with industry partners and the DuraMAT data hub to enable private parties to share and aggregate PV production data anonymously, leveraging cloud-based data analysis infrastructure and publishing on US fleet-scale performance comprising over 7GW of operating systems. (https://www.nrel.gov/pv/fleet-performance-data-initiative.html). Through our industry collaborations we have engaged in NDA-covered data transfer with twelve PV fleet owners as of January 2022, with more agreements in negotiation. Our scalable cloud-based time series database contains over 30 billion rows (20TB) of PV time series data, representing over 1700 commercial and utility-scale systems, and over 7.2 GW of DC capacity (Fig 1). Initial field performance results have been distributed in several public reports. Because our fleet composition and data quality methods are continually improving, annual updates to these results are published to our PV Fleet webpage [ https://www.nrel.gov/pv/fleet-performance-data-initiative.html ] and DuraMAT data hub [DOI: 10.21948/1842958]. Another existing dissemination channel used for observed soiling losses is a map we maintain for soiling losses. Additional products developed include a report detailing fleet-wide performance index, availability, startup loss and snow loss factors, a detailed report on the 1603 grant dataset comprising over 100,000 PV systems with failure and performance details and a utility-scale report coauthored with LBNL on 31 GW of system performance.

14 SOLAR ENERGY↗

Energetic particle physics: Chapter 7 of the special issue: on the path to tokamak burning plasma operation

We review the physics of energetic particles (EPs) in magnetically confined burning fusion plasmas with focus on advances since the last update of the ITER Physics Basis (Fasoli et al 2007 Nucl. Fusion 47 S264). Topics include basic EP physics, EP generation, diagnostics of EPs and instabilities, the interaction of EPs and thermal plasma instabilities, EP-driven instabilities, energetic particle modes (EPMs), and turbulence, linear and nonlinear stability and simulation of EP-driven instabilities and EPMs, 3D effects, scenario optimization strategies based on EP phase-space control, EPs in reduced field scenarios in ITER before DT, and the physics of runaway electrons. We describe the simulation and modeling of EPs in fusion plasmas, including instability drive and damping as well as EP transport, with a range of approaches from first-principles to reduced models, including gyrokinetic simulations, kinetic-MHD models, gyrofluid models, reduced models, and semi-analytical approaches.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Comparative Performance of Three Eye-Tracking Devices in Detection of Mild Traumatic Brain Injury in Acute Versus Chronic Subject Populations

ABSTRACT Introduction Presently, traumatic brain injury (TBI) triage in field settings relies on symptom-based screening tools such as the updated Military Acute Concussion Evaluation. Objective eye-tracking may provide an alternative means of neurotrauma screening due to sensitivity to neurotrauma brain-health changes. Previously, the US Army Medical Research and Development Command Non-Invasive NeuroAssessment Devices (NINAD) Integrated Product Team identified 3 commercially available eye-tracking devices (SyncThink EYE-SYNC, Oculogica EyeBOX, NeuroKinetics IPAS) as meeting criteria toward being operationally effective in the detection of TBI in service members. We compared these devices to assess their relative performance in the classification of mild traumatic brain injury (mTBI) subjects versus normal healthy controls. Materials and Methods Participants 18 to 45 years of age were assigned to Acute mTBI, Chronic mTBI, or Control group per study criteria. Each completed a TBI assessment protocol with all 3 devices counterbalanced across participants. Acute mTBI participants were tested within 72 hours following injury whereas time since last injury for the Chronic mTBI group ranged from months to years. Discriminant analysis was undertaken to determine device classification performance in separating TBI subjects from controls. Area Under the Curves (AUCs) were calculated and used to compare the accuracy of device performance. Device-related factors including data quality, the need to repeat tests, and technical issues experienced were aggregated for reporting. Results A total of 63 participants were recruited as Acute mTBI subjects, 34 as Chronic mTBI subjects, and 119 participants without history of TBI as controls. To maximize outcomes, poorer quality data were excluded from analysis using specific criteria where possible. Final analysis utilized 49 (43 male/6 female, mean [x̅] age = 24.3 years, SD [s] = 5.1) Acute mTBI subjects, and 34 (33 male/1 female, x̅ age = 38.8 years, s = 3.9) Chronic mTBI subjects were age- and gender-matched as closely as possible with Control subjects. AUCs obtained with 80% of total dataset ranged from 0.690 to 0.950 for the Acute Group and from 0.753 to 0.811 for the Chronic mTBI group. Validation with the remaining 20% of dataset produced AUCs ranging from 0.600 to 0.750 for Acute mTBI group and 0.490 to 0.571 for the Chronic mTBI group. Conclusions Potential eye-tracking detection of mTBI, per training model outcomes, ranged from acceptable to excellent for the Acute mTBI group; however, it was less consistent for the Chronic mTBI group. The self-imposed targeted performance (AUC of 0.850) appears achievable, but further device improvements and research are necessary. Discriminant analysis models differed for the Acute versus Chronic mTBI groups, suggesting performance differences in eye-tracking. Although eye-tracking demonstrated sensitivity in the Chronic group, a more rigorous and/or longitudinal study design is required to evaluate this observation. mTBI injuries were not controlled for this study, potentially reducing eye-tracking assessment sensitivity. Overall, these findings indicate that while eye-tracking remains a viable means of mTBI screening, device-specific variability in data quality, length of testing, and ease of use must be addressed to achieve NINAD objectives and DoD implementation.

General & Internal Medicine↗

Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO 2 capture process

In this paper, a methodology is developed for sequential design of experiments (SDoE) for process systems and applied to a solvent-based CO 2 capture system. In this approach, the prior knowledge of the system is used to prioritize process data collection at specific operating conditions. These data are then incorporated into a Bayesian inference methodology for updating a stochastic model by refining estimations of its underlying parameters, and the updated model is then used to generate the next set of test runs. Thus, the new knowledge obtained from the data is used to guide subsequent iterations of the experimental runs, ensuring that the overall data collection is maximally informative given that most experimental campaigns, especially at pilot or higher-scale plants, are costly, time-consuming, and resource-limited. The test run objective for this work was to minimize the maximum model prediction uncertainty for key output variables, but the methodology is generic and can be readily applied to other test run objectives. This methodology is applied to an aqueous monoethanolamine (MEA) pilot plant campaign at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama, USA. The SDoE framework was utilized for two iterations, while collecting 18 sets of data representing different process conditions, and this resulted in an overall average reduction in uncertainty of approximately 50% in the prediction of CO 2 capture percentage. Moreover, 11 additional data sets were obtained with variation of absorber packing height for further model validation. This work shows the capability of the SDoE framework to maximize learning given limited resources, allowing for the reduction of model uncertainty, which is of great importance for many applications including reduction of technical risk associated with scale-up and economic analysis.

20 FOSSIL-FUELED POWER PLANTS↗