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DOE OSTI2023

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction

Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Technical and Licensing Considerations for Micro-Reactors

The U.S. Nuclear Regulatory Commission (NRC) has interacted with vendors pursuing the commercialization of micro-reactors (i.e., reactors capable of producing about 1 MW(th) to 20 MW(th) of energy from nuclear fission). It is envisioned that micro-reactors could be assembled and fueled in a factory and shipped to a site. Many of the sites are expected to be remote locations requiring off-grid power or in some cases military bases. The objective of this effort is to explore the technical issues and the approach required to reach a finding of "reasonable assurance of public health and safety" for this new and different class of reactors. The analysis performed here leverages available micro-reactor design and testing data available from national laboratory experience as well as commercial design information to explore technical issues. Some factors considered include source term, accidents that would need to be analyzed, and the extent of the probabilistic risk assessment (PRA). The technical evaluation was performed within the framework of the Licensing Modernization Project (LMP) to identify licensing basis events, classification of structures, systems and components, and defense-in-depth needed to provide regulatory certainty. With this framework and technical evaluation in mind, the scope and content of a micro-reactor licensing application is discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Reactor Pressure Vessel Fracture Mechanics Development and Concrete Application Testing for Grizzly

The Grizzly code is being developed to address degradation issues in nuclear reactor structures and components. For light-water reactors, Grizzly currently has capabilities to simulate degradation processes and their effects on structural integrity in two key areas: reactor pressure vessels (RPVs) and reinforced concrete structures. This report documents improvements made to Grizzly’s ability to address both of these structural systems. For RPVs, the reduced-order models (ROMs) used in fracture mechanics calculations have been expanded to allow their application over a broader range of the parameter space than was permitted by the previous models. The ROMs currently used in Grizzly for the evaluation of flaws that are fully embedded within the RPV (as opposed to surface-breaking flaws) are based on a model that is known to be conservative, indicating higher stress intensity factors than would be obtained from direct simulations. A more accurate model that eliminates these excess conservatisms has been recently included in the American Society of Mechanical Engineers Boiler and Pressure Vessel Code but was not applicable for flaws near the RPV surface, which is where the most critical flaws are usually located. That model has recently been extended for increased applicability in this near-surface region. The ROMs for embedded flaws in the Grizzly code have been expanded to include these recent extensions, which permit their use in a much broader set of cases than previously possible. Direct 3D simulations have been used to check these ROMs and have shown good agreement in most cases, although there are still some cases that need further investigation. There are considerable benefits to using these these more accurate and less conservative ROMs for embedded flaws. On a benchmark probabilistic fracture mechanics problem tested here, the conditional probability of fracture initiation computed for a population of flaws in a single plate in an RPV decreased by over a factor of 3. To address aging in reinforced concrete structures, a capability to simulate multiple degradation mechanisms, including alkali-silica reaction and radiation-induced volumetric expansion has been developed in Grizzly over the past several years. This had previously been demonstrated on laboratory-scale specimens but not on full-scale nuclear concrete structures with reinforcement. To demonstrate the applicability of Grizzly to the analysis of large-scale structures of interest, a full 3D model of a representative reinforced concrete structure, including a complex arrangement of reinforcing bars, was developed and demonstrated in Grizzly.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗

DOE OSTI2022

Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability

This paper describes a milling stability identification approach that simultaneously considers: physics-based models for the tool tip frequency response functions and stability predictions; the binary result from a milling test (automatically labeled as stable or unstable based on frequency content); chatter frequency when an unstable result is obtained; and user risk tolerance. The algorithm applies probabilistic Bayesian machine learning with adaptive, parallelized Markov Chain Monte Carlo sampling to update the probability of stability with each milling test. Furthermore, the result is a robust solution for rapid convergence to optimized milling parameters for maximum metal removal rate using all available information.

42 ENGINEERING↗

DOE OSTI2020

A systematic approach to identify initiating events and its relationship to Probabilistic Risk Assessment: Demonstrated on the Molten Salt Reactor Experiment

Not Available

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

NRAP-Open-IAM: NRAP Open Source Integrated Assessment Model

Note: This is the last version (a2.6.1) of NRAP-Open-IAM released during NRAP Phase II in 2022. The latest version of NRAP-Open-IAM is available here: https://edx.netl.doe.gov/dataset/phase-iii-nrap-open-iam NRAP-Open-IAM is an open-source software product that enables quantification of containment effectiveness and leakage risk at storage sites in the context of system uncertainties and variability. NRAP-Open-IAM represents the next-generation in a line of systems-based computational models developed for quantitative geological carbon storage (GCS) risk assessment. The model comprises a set of reduced-order and analytical models of various components of the GCS system, potential leakage pathways, receptors of concern including impact to groundwater resources and the atmosphere, a framework to support stochastic simulation, time stepping, uncertainty quantification, other analytical functionality for scenario and risk-performance evaluation, and a basic graphical user interface to support scenario development, data input simulation definition, and basic post-processing and results display. As the NRAP Open-IAM functionality continues to evolve, we continue to add to its capability to develop quantitative, probabilistic, and time-dependent profiles of the evolution of risk at a GCS site and evaluate the influence of uncertain parameters on uncertainty in predicted risk. It can be used to quantify the dynamics of reservoir saturation plume and pressure-affected area, for evaluation of the area of potential groundwater impact (i.e., Area of Review) and monitoring requirements to support cost and regulatory analysis, and for consideration of different post-injection site care and closure scenarios. This submission contains the current version of NRAP-Open-IAM available for evaluation and testing. To use the NRAP-Open-IAM, download the source code (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/4c24a3da-3b40-4ffe-9892-c807ae9f8760) then open the NRAP-Open-IAM user's guide (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669) to read more about the tool. Installation instructions for Windows, Mac, and Linux can be found in the "installers" folder of the extracted NRAP-Open-IAM folder and describe setup of environment (e.g., Python libraries) needed for proper work of the tool. Test of installation can be done by running "python openiam_setup_tests.py" in the "setup" folder. The installation test also runs a test suite to see if the NRAP-Open-IAM has been installed correctly. To run the test suite separately, run "python iam_test.py" in the "test" folder. User's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669 Developer's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/3bc6ee7d-609d-4eb6-80ba-fa6130ee0313 Reservoir simulation data used in some examples distributed with NRAP-Open-IAM: - Kimberlina: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/eb62cece-61b2-4037-9b6d-32407dde2ab8 - Kimberlina (compartmentalized): https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/366f9530-3b32-4b84-affe-ab2df1d9a8b5 - FutureGen 2.0: https://edx.netl.doe.gov/dataset/futuregen-2-0-1008-simulation-reservoir-lookup-table NRAP-Open-IAM GitLab repository: https://gitlab.com/NRAP/OpenIAM Related publications: - Bacon, D., Yonkofski, C., Brown, C., Demirkanli, D. and Whiting, J., 2019. Risk-based post injection site care and monitoring for commercial-scale carbon storage: Reevaluation of the FutureGen 2.0 site using NRAP-Open-IAM and DREAM. International Journal of Greenhouse Gas Control 90: 102784. - Bacon, D. Demirkanli, D., and White, S., 2020. Probabilistic risk-based Area of Review (AoR) determination for a deep-saline carbon storage site. International Journal of Greenhouse Gas Control 102: 103153. - Harp, D., Oldenburg, C., and Pawar, R., 2019. A metric for evaluating conformance robustness during geologic CO2 sequestration operations. International Journal of Greenhouse Gas Control 85: 100-108. - Lackey, G., Vasylkivska, V., Huerta, N., King, S., and Dilmore, R., 2019. Managing well leakage risks at a geologic carbon storage site with many wells, International Journal of Greenhouse Gas Control, 88 :182-194. - Vasylkivska, V., Dilmore, R., Lackey, G., Zhang, Y., King, S., Bacon, D., Chen, B., Mansoor, K., and Harp, D., 2021. NRAP-Open-IAM: A flexible open-source integrated assessment model for geologic carbon storage risk assessment and management, Environmental Modelling & Software, 143: 105114. Presentations: - Chen, B., Harp, D., and Pawar, R., A data assimilation approach (ES-MDA) coupling with NRAP-Open-IAM for quantifying uncertainty reduction in geological CO2 sequestration. AGUFM 2019: T44A-02. - Chen, B., and Harp, D., Improving risk analysis precision for geologic CO2 sequestration by quantifying the uncertainty reduction before and after acquiring monitoring data. 14th Greenhouse Gas Control Technologies Conference, Melbourne, Australia, 2018, pp. 21-26. - Harp, D., National Risk Assessment Partnership Task 2: Containment Assurance. No. LA-UR-19-28654, Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2019. - Vasylkivska, V., King, S., Bacon, D., Harp, D., Chen, B., Mansoor, K., Onishi, T., Yang, Y., Zhang, Y., and Keating, E., NRAP-Open-IAM: An open-source integrated assessment model, poster, Mastering the Subsurface Through Technology Innovation, Partnerships and Collaboration: Carbon Storage and Oil and Natural Gas Technologies Review Meeting, Pittsburgh, PA, August 13-16, 2018. - Vasylkivska, V., Lackey, G., King, S., Wentworth, A., Huerta, N., Creason, C., DiGiulio, J., Yang, Y., and Dilmore, R., Long-term risk analysis of a geologic CO2 storage project during the post-injection period, SIAM Conference on Computational Science and Engineering, Spokane, WA, February 25-March 1, 2019. - Vasylkivska, V., Overview of the NRAP-Open-IAM tool for carbon storage (beta release), 2019 Annual NRAP Tool Users Meeting, Pittsburgh, PA, August 27, 2019. - Vasylkivska, V., Bacon, D., Chen, B., Dilmore, R., Harp, D., King, S., Lackey, G., Lindner, E., Liu, G., Mansoor, K. and Zhang, Y., NRAP-Open-IAM: A new, open-source code for integrated assessment of geologic carbon storage containment effectiveness and leakage risk, poster, American Geophysical Union Fall Meeting 2020 (virtual meeting), December 2020. - Vasylkivska, V., NRAP open-source integrated assessment model and relevant application, oral presentation, NRAP workshop "NRAP Tools for Geologic Carbon Storage Risk-Based Decision Making" held in conjunction with Groundwater Protection Council (GWPC) 2021 Annual Forum (virtual meeting), Salt Lake City, UT, September 2021. - Vasylkivska, V., NRAP-Open-IAM: open-source integrated assessment model, digital poster/demonstration, software demonstration session, 2022 Carbon Management Project Review Meeting, August 16, 2022

AoR↗

DOE OSTI2022

Reliability-Informed Economic and Energy Evaluation for Bi-Level Design for Remanufacturing: A Case Study of Transmission and Hydraulic Manifold

Abstract Design for remanufacturing (DfRem) is one attractive strategy that encourages the reuse of a product and extends the product's life cycle. Traditional design processes often only consider product reliability at an early design stage. However, from the perspective of environmental sustainability, it is becoming increasingly important to evaluate the long-term economic and environmental impacts of design decisions during early-stage design. We propose a bi-level DfRem framework consisting of system-level reusability allocation and component-level design tradeoff analysis, considering reliability and product warranty policy. First, a system-level reusability allocation problem aims at a theoretical exploration of the design space where all the components comprising the system are allocated certain reuse rates to achieve target energy savings with minimum cost. Following the theoretical exploration at the system level, a component-level analysis looks at practical design options for each component and trades-off between the overall cost and energy consumption for multiple remanufacturing cycles. Both levels of the framework require modeling component reuse for multiple remanufacturing cycles, which we achieve by using a branched power-law model that provides probabilistic scenarios of reusing the component or replacing it with a new part. We demonstrate the utility of this framework with the case study of an infinitely variable transmission (IVT) used by some agricultural machines manufactured by John Deere and show snapshots of a prototype software tool that we developed for easy use by designers.

Engineering↗

DOE OSTI2024

Hazards and Probabilistic Risk Assessments of Advanced Nuclear Reactors Coupled with Industrial Facilities

This report provides a roadmap and tool kit for site specific risk assessments across a broad range of industrial customers co-located with advanced nuclear power plants (ANPP) that are not currently built and operating in the U.S. This report builds upon the body of work sponsored by the Department of Energy (DOE) Integrated Energy Systems Pathway that has produced industrial requirements studies and techno-economic assessments on the topics of feasibility of ANPP supported industrial processes. This report also leverages the DOE Light Water Reactor Sustainability (LWRS) program that has presented hazards assessment and generic probabilistic risk assessments (PRAs) for the addition of a heat extraction system (HES) to light-water reactors (LWRs) co-located with hydrogen production facilities. Many of the hazard assessments and risk assessments performed for the LWRS report are agnostic to whether the nuclear reactor is an ANPP or were adapted to the ANPP focus. The report performs hazards assessments to include industrial facilities: an oil refinery, a methanol plant, a synthetic fuel (synfuel) plant, the production of synthetic gas (syngas) as part of the methanol and synfuel plants, wood pulp and paper mills, and hydrogen production. Hydrogen production facilities are assessed in depth through prior reports in the LWRS program and the results are leveraged in this report. All these facilities are specified through industrial process and requirements research performed by national laboratories, universities, and interaction with industry. Many of the processes used in this report are pre-conceptual designs to use for decarbonization of the current technology facilities. A process of failure modes and effects analysis (what can go wrong) and accidentology (what has historically gone wrong) was used to determine the hazards presented to the nuclear power plant by the addition of the HES and the industrial customer. Chemical properties of feedstocks and products are summarized as part of the hazards assessment. Example analysis procedures are provided for each of the hazard types identified. These deterministic analyses can be used to assess adherence to licensing criteria. They can also be used to meet other safety goals like protection of the public, workers, or industrial facility equipment. A modular high temperature gas-cooled reactor (MHTGR) PRA only existing on paper was modeled and verified in modern PRA software. This will provide a tool for representative ANPP probabilistic analyses for future research.

10 SYNTHETIC FUELS↗

DOE OSTI2021

An uncertainty-focused database approach to extract spatiotemporal trends from qualitative and discontinuous lake-status histories

Changes in lake status are often interpreted as palaeoclimate indicators due to their dependence on precipitation and evaporation. The Global Lake Status Database (GLSDB) has since long provided a standardised synopsis of qualitative lake status over the last 30,000 14C years. Potential sources of uncertainty however are not recorded in the GLSDB. Here we present an updated and improved relational-database framework that incorporates uncertainty in both chronology and the interpretation of palaeoenvironmental data. The database uses peer-reviewed palaeolimnological studies to produce a consensus on qualitative lake-status histories, whose chronologies are revised and standardized through the recalibration of radiocarbon dates and the application of Bayesian age-depth modelling for stratigraphic archives. Quantitative information on absolute water-level elevation is preserved if available from geomorphological sources. We also propose a new probabilistic analytical framework that accounts for these uncertainties to reconstruct synoptic, integrated environmental signals. The process is based on a Monte Carlo algorithm that iteratively samples individual lake-status histories within the limits of their uncertainties to produce many possible scenarios. We then use Recursively-Subtracted Empirical Orthogonal Function analysis to extract dominant patterns of lake-status variability from these scenarios. As a proof of concept, we apply this framework to 67 sites in eastern and southern Africa whose lake-status histories cover part of the late Pleistocene and/or Holocene. We show that, despite the sometimes large temporal and interpretation uncertainties, and the inclusion of highly discontinuous lake-status time series, identifying the major known millennial-scale climatic phases during the last 20,000 years is possible. Our framework was also able to identify an antiphased response between the lake basins in eastern and interior southern Africa to these changes. Here, we propose that our new database and methodology framework serves as a template for efficient lake-status data synthesis, encourages the incorporation of lake-status data in palaeoclimate syntheses, and expands the possibilities for the use of such data in the evaluation of climate models.

58 GEOSCIENCES↗

DOE OSTI2025

MOOSE ProbML: Parallelized probabilistic machine learning and uncertainty quantification for computational energy applications

Here, this paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source computational platform for parallel finite element and finite volume analyses. In addressing the computational expense and uncertainties inherent in complex multiphysics simulations, this paper integrates Gaussian process (GP) variants, active learning, Bayesian inverse UQ, adaptive forward UQ, Bayesian optimization, evolutionary optimization, and Markov chain Monte Carlo (MCMC) within MOOSE. It also elaborates on the interaction among key MOOSE systems — Sampler, MultiApp, Reporter, and Surrogate — in enabling these capabilities. The modularity offered by these systems enables development of a multitude of probabilistic ML and UQ algorithms in MOOSE. Example code demonstrations include parallel active learning and parallel Bayesian inference via active learning. The impact of these developments is illustrated through five applications relevant to computational energy applications: UQ of nuclear fuel fission product release, using parallel active learning Bayesian inference; very rare events analysis in nuclear microreactors using active learning; advanced manufacturing process modeling using multi-output GPs (MOGPs) and dimensionality reduction; fluid flow using deep GPs (DGPs); and tritium transport model parameter optimization for fusion energy, using batch Bayesian optimization. These capabilities are part of the MOOSE framework.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2021

GDSA PFLOTRAN Development (FY2021)

The Spent Fuel & Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for SFWST disposal R&D is to develop a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2021 advances of the PFLOTRAN Development group of the SFWST Campaign. The mission of this group is to develop a geologic disposal system modeling capability for nuclear waste that can be used to probabilistically assess the performance of generic disposal concepts. In FY 2021, development proceeded along three main thrusts: software infrastructure, code performance, and process model advancement. Software infrastructure improvements included implementing an Agile software development framework and making improvements to the QA Test Suite. Code performance improvements included development of advanced linear and nonlinear solvers as well as design of flexible smoothing algorithms for capillary pressure functions. Process modeling advancements included the addition of flexible thermal conductivity function definitions and refinement of multi-continuum reactive transport to support Sandia’s participation in DECOVALEX

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

PFLOTRAN Development FY2022

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

PFLOTRAN Development FY2021

The Spent Fuel & Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for SFWST disposal R&D is to develop a disposal system modeling and analysis capability for valuating disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2021 advances of the PFLOTRAN Development group of the SFWST Campaign. The mission of this group is to develop a geologic disposal system modeling capability for nuclear waste that can be used to probabilistically assess the performance of generic disposal concepts. In FY 2021, development proceeded along three main thrusts: software infrastructure, code performance, and process model advancement. Software infrastructure improvements included implementing an Agile software development framework and making improvements to the QA Test Suite. Code performance improvements included development of advanced linear and nonlinear solvers as well as design of flexible smoothing algorithms for capillary pressure functions. Process modeling advancements included the addition of flexible thermal conductivity function definitions and refinement of multi-continuum reactive transport to support Sandia’s participation in DECOVALEX. This report fulfills the GDSA PFLOTRAN Development Work Package Level 3 Milestone – PFLOTRAN Development, FY2021, M3SF-21SN010304072.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Multicriterion Benefit Evaluation of Deploying New Battery Technology with Increased Capacity at a Generic Nuclear Power

Nuclear power plant (NPP) safety improvements are routinely made by plant licensees and regulators. Examples of plant improvements include accident-tolerant fuel, diverse and flexible coping strategies, passive cooling systems, and increased battery capacity. A combined use of these plant improvements could lead to plant designs with enhanced resilience, allowing NPPs to better cope with both internal and external hazards and keep the plant operating safely, efficiently, and economically. This paper focuses on increased battery capacity and evaluates the potential costs and benefits of deploying batteries with increased capacities at a generic boiling water reactor (BWR) NPP. A multicriterion benefit evaluation methodology is used for the cost-benefit analysis. Ten alternatives for extending battery capacity are developed, including nine alternatives to provide additional direct-current power and one alternative to provide additional alternating-current power. Potential benefits of reducing plant risk are quantified through incorporating the alternatives into loss-of-offsite-power scenarios of the generic BWR probabilistic risk assessment model. Potential costs of implementing the alternatives are qualitatively discussed and ranked. The alternatives are then compared based on their impacts on plant risk and economics.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Multicriterion Benefit Evaluation of Deploying New Battery Technology with Increased Capacity at a Generic Nuclear Power Plant

This presentation is prepared for the DOE LWRS RISA Pathway Stakeholder Engagement Meeting on October 13-14, 2021. Nuclear power plant (NPP) safety improvements are routinely made by plant licensees and regulators. Examples of plant improvements include accident-tolerant fuel, diverse and flexible coping strategies, passive cooling systems, and increased battery capacity. A combined use of these plant improvements could lead to plant designs with enhanced resilience, allowing NPPs to better cope with both internal and external hazards and keep the plant operating safely, efficiently, and economically. This presentation focuses on increased battery capacity and evaluates the potential costs and benefits of deploying batteries with increased capacities at a generic boiling water reactor (BWR) NPP. A multicriterion benefit evaluation methodology is used for the cost-benefit analysis. Ten alternatives for extending battery capacity are developed, including eight alternatives to provide additional direct-current power and one alternative to provide additional alternating-current power. Potential benefits of reducing plant risk are quantified through incorporating the alternatives into loss-of-offsite-power scenarios of the generic BWR probabilistic risk assessment model. Potential costs of implementing the alternatives are qualitatively discussed and ranked. The alternatives are then compared based on their impacts on plant risk and economics.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Linking Extragalactic Transients and Their Host Galaxy Properties: Transient Sample, Multiwavelength Host Identification, and Database Construction

Understanding the preferences of transient types for host galaxies with certain characteristics is key to studies of transient physics and galaxy evolution, as well as to transient identification and classification in the LSST era. Here we describe a value-added database of extragalactic transients—supernovae, tidal disruption events, gamma-ray bursts, and other rare events—and their host galaxy properties. Based on reported coordinates, redshifts, and host galaxies (if known) of events, we cross-identify their host galaxies or most likely host candidates in various value-added or survey catalogs, and compile the existing photometric, spectroscopic, and derived physical properties of the host galaxies in these catalogs. This new database covers photometric measurements from the far-ultraviolet to mid-infrared. Spectroscopic measurements and derived physical properties are also available for a smaller subset of hosts. For our 36,333 unique events, we have cross-identified 13,753 host galaxies using host names, plus 4480 using host coordinates. Besides those with known hosts, there are 18,100 transients with newly identified host candidates. This large database will allow explorations of the connections of transients to their hosts, including a path toward transient alert filtering and probabilistic classification based on host properties.

79 ASTRONOMY AND ASTROPHYSICS↗