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DOE OSTI2016

Simplifying the Tracking of Waste Disposal Inventories: Process Improvement for Rapid Data Interpretation and Reduced Errors - 16143

Tracking the inventory of low-level radioactive waste at an operating disposal facility offers unique challenges. At the Savannah River Site's Saltstone Disposal Facility (SDF) the inventory used in Performance Assessment modeling is determined based on radionuclide concentrations from the aqueous waste in a feed tank and the volume of the waste that is transferred from the feed tank to the Saltstone Production Facility (SPF). These inputs are reported quarterly. As such, the disposal inventories are also updated quarterly; however, due to decay and ingrowth, the inventory values for some radionuclides can be misrepresented if the data is not tracked appropriately. Additionally, it is not feasible to analyze the feed tank samples for every possible radionuclide; therefore, a series of adjustments are necessary to estimate the concentrations of radionuclides that are not explicitly measured but still pose a potential dose risk. Prior to 2015, the SDF disposal inventories were updated using a series of spreadsheets that required users to perform a significant number of steps (including manually transferring data back and forth between a separate decay and ingrowth calculation software). Despite this labor intensive approach, the process only provided single point-in-time results. With multiple steps, minimal automation, and limited result data, this approach to inventory tracking was not ideal and minor mistakes were often identified during the equally cumbersome review process. As a process improvement, the simplified SDF Waste Inventory Disposed Estimator (SDF-WIDE) Model was developed using GoldSim software. The software allows users to identify specific dates to define the duration of the simulation, from the beginning of operations until the time of future facility closure, and provides graphical interfaces for displaying data over time. Further, GoldSim's Contaminant Transport Module is used to perform the decay and ingrowth calculations, making the model completely self-contained (i.e., there is no need to transfer intermediate data back and forth between different programs). GoldSim also includes model 'versioning' features that significantly reduce the time needed to perform the appropriate quality assurance reviews as required for each quarterly inventory update. By transitioning from the complex spreadsheets to the simplified SDFWIDE model, the time needed to perform updates to the SDF inventory has been reduced from days to hours, the risk of making human errors has been greatly mitigated, and users are able to quickly observe trends in the data over time. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition

Neodymium-iron-boron (NdFeB) magnets are the most powerful magnets per unit volume sold in the commercial market. Despite the increasing demand for clean energy applications such as electric vehicles and wind turbines, disruptive events including the COVID-19 pandemic have caused significant uncertainties in the supply and demand for NdFeB magnets. Therefore, this study aims to alleviate the risk of supply shortage for NdFeB magnets and the containing critical materials, rare-earth elements (REEs), through the development of a resilient reverse supply chain and logistics network design. We develop scenarios to model the unique impact of the COVID-19 pandemic on the proposed business, incorporating both disruption intensity and recovery rate. We formulate a chance-constrained two-stage stochastic programming model to maximize the profit while guaranteeing the network resiliency against disruption risks. To solve the problem in large-scale instances, we develop an efficient Benders decomposition algorithm that reduces the computational time by 98.5% on average compared to the default CPLEX algorithm. When applied to the United States, the model suggests the optimal facility locations, processing capacities, inventory levels, and material flows for NdFeB magnet recyclers that could meet 99.7% of the demand. To the best of our knowledge, this study is the first to incorporate the impacts of the COVID-19 pandemic to design a resilient NdFeB magnet recycling supply chain and logistics network, leveraging risk-averse stochastic programming.

42 ENGINEERING↗

DOE OSTI2025

SiC cladding R&D execution plan

To advance the development of accident-tolerant fuel cladding for light-water reactors, the Advanced Fuels Campaign actively addresses the R&D gap between the specialized technological progress in SiC-based composite materials, led by industrial partners, and the fundamental scientific understanding of material behavior under reactor operating conditions. The laboratory activities are systematically organized based on the Development Strategy document, which outlines critical barriers to the use of SiC composites in nuclear energy applications for the coming decade and beyond. To complement the strategy, this execution plan specifies a detailed year-by-year work package and establishes key milestones for assessing progress in technological advancement. This plan has been formulated in collaboration with principal investigators at national laboratories, stakeholders, and industry partners with the goal of closing technology gaps and readying SiC-based cladding technologies for full-scale commercial testing. The research conducted at the national laboratories is categorized into three primary subjects: (1) addressing challenges associated with the performance of SiC cladding under normal operating environments, (2) identifying the failure limits of SiC cladding under accident conditions, and (3) modeling fuel performance for SiC cladding systems. Comprehensive laboratory activities integrate the development of advanced modeling tools for SiC-based cladding systems with experimental validation via integrated testing of multiphysics phenomena. This document lays out the laboratory plan to support the development of SiC-based technologies, which have the potential for significant operational and safety benefits for the U.S. nuclear industry. This plan aligns with industry developer needs, relevant Nuclear Energy University Programs, and Nuclear Energy Advanced Modeling and Simulation Programs to ensure SiC cladding technologies are matured as efficiently as possible and will be maintained and updated to accommodate the dynamically evolving progress of development, capability needs, and resource availability.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2018

Prediction and Computation of Corrosion Rates of A36 Mild Steel in Oilfield Seawater

The parameters which primarily control the corrosion rate and life of steel structures are several and they vary across the different ocean and seawater as well as along the depth. While the effect of single parameter on corrosion behavior is known, the conjoint effects of multiple parameters and the interrelationship among the variables are complex. Millions sets of experiments are required to understand the mechanism of corrosion failure. Statistical modeling such as ANN is one solution that can reduce the number of experimentation. ANN model was developed using 170 sets of experimental data of A35 mild steel in simulated seawater, varying the corrosion influencing parameters SO{sub 4}{sup 2−}, Cl{sup −}, HCO{sub 3}{sup −},CO{sub 3}{sup 2−}, CO{sub 2}, O{sub 2}, pH and temperature as input and the corrosion current as output. About 60% of experimental data were used to train the model, 20% for testing and 20% for validation. The model was developed by programming in Matlab. 80% of the validated data could predict the corrosion rate correctly. Corrosion rates predicted by the ANN model are displayed in 3D graphics which show many interesting phenomenon of the conjoint effects of multiple variables that might throw new ideas of mitigation of corrosion by simply modifying the chemistry of the constituents. The model could predict the corrosion rates of some real systems.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Off-Gas Adsorption Model Capabilities and Recommendations

Off-gas treatment is required to reduce emissions from aqueous fuel reprocessing. Evaluating the products of innovative gas adsorption research requires increased computational simulation capability to more effectively transition from fundamental research to operational design. Early modeling efforts produced the Off-Gas SeParation and REcoverY (OSPREY) model that, while efficient in terms of computation time, was of limited value for complex systems. However, the computational and programming lessons learned in development of the initial model were used to develop Discontinuous Galerkin OSPREY (DGOSPREY), a more effective model. Initial comparisons between OSPREY and DGOSPREY show that, while OSPREY does reasonably well to capture the initial breakthrough time, it displays far too much numerical dispersion to accurately capture the real shape of the breakthrough curves. DGOSPREY is a much better tool as it utilizes a more stable set of numerical methods. In addition, DGOSPREY has shown the capability to capture complex, multispecies adsorption behavior, while OSPREY currently only works for a single adsorbing species. This capability makes DGOSPREY ultimately a more practical tool for real world simulations involving many different gas species. While DGOSPREY has initially performed very well, there is still need for improvement. The current state of DGOSPREY does not include any micro-scale adsorption kinetics and therefore assumes instantaneous adsorption. This is a major source of error in predicting water vapor breakthrough because the kinetics of that adsorption mechanism is particularly slow. However, this deficiency can be remedied by building kinetic kernels into DGOSPREY. Another source of error in DGOSPREY stems from data gaps in single species, such as Kr and Xe, isotherms. Since isotherm data for each gas is currently available at a single temperature, the model is unable to predict adsorption at temperatures outside of the set of data currently available. Thus, in order to improve the predictive capabilities of the model, there is a need for more single-species adsorption isotherms at different temperatures, in addition to extending the model to include adsorption kinetics. This report provides background information about the modeling process and a path forward for further model improvement in terms of accuracy and user interface.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

3D CFD Model Validation Using Benchmark Data of 1/16th Scaled VHTR Upper Plenum and Development of Wall Heat-Transfer Correlation For Laminar Flow

With support from the U.S. Department of Energy-Office of Nuclear Energy’s (DOE-NE’s) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, an effort has been pursued to support high-temperature gas-cooled reactor (HTGR) technology development and its modeling and simulation needs. There is a particular need for advanced modeling and simulation tools to predict thermal-fluid behavior in the nuclear reactor primary system, especially in the core and the lower and upper plena, during safety-related transients. In this report, two main such activities are presented relevant to the HTGRs: (1) three-dimensional (3D) computational fluid dynamics (CFD) validation using benchmark data from the upper plenum of Texas A&M University’s 1/16th scaled very-high-temperature gas-cooled reactor (VHTR), and (2) development of wall heat-transfer correlation for laminar flow in a wall-heated pipe. The CFD tool validation exercises can be helpful to choose the models and CFD tools to simulate and design specific components of the HTRGs such as upper plenum where jet mixing is a complex phenomenon. In a loss of forced circulation event, the laminar flow can be observed during the development of natural circulation flow. This work includes the development and validation of heat transfer correlations for laminar flow using the Nek5000 CFD code due to limited available experimental data for laminar flow conditions to guide low-order models (1D). In this report, the flow characteristics of a single isothermal jet discharging into the upper plenum was investigated using the Nek5000 Large-Eddy Simulation (LES) CFD tool. Several numerical simulations were performed for various jet-discharged Reynolds numbers ranging from 3,413 to 12,819. A grid-independent study was performed. The numerical results of mean velocity, root-mean-square fluctuating velocity, and Reynolds stress were compared against the benchmark data. Good agreement was obtained between simulated and measured data for axial mean velocities, except near the upper plenum hemisphere. The maximum predicted errors for axial mean velocities at various normalized coolant channel diameter heights of 1, 5, and 10 are 1.56%, 1.88%, and 3.82%, respectively. In addition, the predicted root-mean-square fluctuating velocity and Reynolds stress are qualitatively in agreement with the experimental data. The Nek5000 code was used to develop wall-heat transfer correlation for laminar flow in a cylindrical tube. Several simulations were performed for various Reynolds flow and wall-heat fluxes. A new heat transfer correlation was developed using data from Nek5000 simulation results and regression functions in Matlab. The developed heat transfer correlation is valid for various Reynolds flows from 200 to 2000. The predicted R² value for model fit was 0.875, which ensures that 87.5% of the model data lies on the Nek5000 data. Moreover, a machine learning (ML) tool was used to train and test the Nek5000 data. A good fit of the ML-based model was observed with the test data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Collaborative Research: Advancing Arctic Climate Projection Capability at Seasonal to Decadal Scales (Final Technical Report)

The Regional Arctic System Model (RASM) at process resolving configurations has been used to (i) advance understanding of physical processes and feedbacks involved in Arctic amplification and (ii) understand and potentially reduce uncertainty in prediction of arctic climate change at seasonal to decadal scales. RASM consists the atmosphere (Weather and Research Forecasting model, WRF), ocean (Parallel Ocean Program, POP), sea ice (CICE), land hydrology (Variable Infiltration Capacity model, VIC), river routing scheme (RVIC), marine biogeochemistry components and the coupling framework (CPL7). Its domain is pan-Arctic, with the atmosphere and land components configured on a 50-km or 25-km grid and four configurations of the ocean and sea ice components: 1/12°(~9.3km) or 1/48°(~2.4km) and 45 or 60 vertical layers. These RASM configurations have been motivated by the emerging exascale capability for high performance computing to improve model fidelity. The dynamical downscaling of reanalysis allows comparison of RASM results with observations in place and time to: (i) advance system level understanding of physical processes and coupling involved in an event, (ii) optimize model parameter space, (iii) diagnose and reduce model biases and (iv) produce realistic and consistent across all the components initial conditions for predictions and predictability studies, which are all unique capabilities not available in global Earth System Models (ESMs). An evaluation of RASM 1.0 (Cassano et al. 2017) revealed that it had a cold bias over the oceans and a warm bias over land areas due largely to cloud and radiation biases in the model, with too little cloud cover simulated over land and too much cloud cover simulated over sub-polar oceans. This study has motivated an upgrade to WRF version 3.7.1 in RASM and allowed for the inclusion of the radiative impact of convective clouds. A variety of atmospheric physics parameterizations were evaluated against observations (e.g. data from the Arctic Clouds in Summer Experiment (ACSE); Sedlar et al. 2020) to identify an optimal suite of WRF physics options in RASM. The RASM with the optimized WRF physics were used to study the impact of strong mesoscale winds over the ocean around the southern tip of Greenland (DuVivier and Cassano 2016) and their impact on oceanic convection (DuVivier et al. 2017a). Data from the PolarWinds field campaign were used to evaluate WRF boundary layer physics and resolution impacts on the simulation of a Greenland barrier wind event (DuVivier et al. 2017b). The RVIC streamflow routing model has been implemented in RASM to realistically represent high-resolution streamflow processes (Hamman et al. 2017) and to couple the land buoyancy fluxes to the ocean. The RASM-RVIC high-resolution data set of all coastal freshwater fluxes for the Arctic drainage basin and surrounding areas for 1979-2014 was published as a separate product (https://doi.org/10.5281/zenodo.293037). The fidelity of atmospheric momentum transfer to and the response of polar marine Ekman layer in RASM and Community Earth System Model (CESM) was investigated by Roberts et al. (2015). The increased frequency of oceanic flux exchange in CESM, following the RASM guidance, caused a considerable increase in the median inertial ice speed across the Southern Ocean and parts of the Arctic. A comprehensive evaluation of the RASM1.0 atmosphere-ocean-sea ice-land interface was completed by Brunke et al. (2018). RASM was also demonstrated for its capability to simulate extreme events in agreement with observations in space and time (Lee et al. submitted). In particular, the development of three open water events, known as polynyas, have been simulated north of Greenland in February of 2011, 2017 and 2018, in agreement with satellite observations for the past four decades. The optimized RASM sea ice results have been favorably evaluated against satellite observations and a subset of eleven CMIP6 models (Watts et al. submitted). In a complementary project, Jin et al. (2018) have shown that RASM with higher-resolution and new sea-ice processes contributed to lower model errors in sea-ice conditions, concentrations of nutrients and ice algae, in comparison to results from the coarse-resolution (1°) CESM. In two other complementary studies, RASM results were used (i) to explain areas of concentrated use by bowhead whales, the seasonal progression in the use, and the physical environment within those areas (Citta et al. 2015) and (ii) for a synthesis of fall bowhead whales distribution and migration in the Bering-Chukchi-Beaufort (BCB) Sea to investigate whale movements and feeding to the local ocean hydrography and currents (Citta et al. 2018). However, the multi-decadal output from the CESM Large Ensemble yielded unrealistic forcing. Instead, the shorter NCEP CFSv2 9-month forecasts were successfully tested and afforded an increased ensemble size (~30) to demonstrate gains of dynamical downscaling at sub-seasonal to intra-annual time scales. The improved model physics and coupling among RASM model components have yielded more realistic representation of the sea ice cover and consistent across all model components initial conditions. Consequently, RASM demonstrates significant gains compared to simulation of sea ice in the NCEP reanalysis. In addition, RASM 6-month ensemble forecasts yield very realistic sea ice distribution, which demonstrates both significant gains of dynamical downscaling and the continued impact of the initial conditions on forecasts out to 6 months (https://nps.edu/web/rasm/predictions). A follow up study using RASM for dynamical downscaling of the more realistic CESM initialized Decadal Prediction Large Ensemble output is currently ongoing as part of the DOE RGMA HiLAT-RASM project.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

An Overview of the Molten Salt Thermal Properties Database–Thermophysical, Version 4.0 (MSTDB-TP V.4.0)

A central repository of thermophysical and thermochemical properties of molten salt compositions of relevance to molten salt reactors (MSRs) is vital in supporting the broad community of MSR developers, who are at various stages of developing and deploying their reactor designs. In general, these MSR designs differ significantly from developer to developer (e.g., with respect to the hardness of the neutron spectra, level of fissile loading, target multicomponent temperatures and power levels, and moderating capabilities). Therefore, the fuel and coolant salts being considered vary greatly: they may be chlorides or fluorides, they utilize different actinides at different ratios, and the cations in the melt are selected based on perceived advantages and disadvantages. Considering the general need for thermal properties, and the vastness of the array of potential candidate salt mixtures, the Molten Salt Thermal Properties Database (MSTDB) was initiated in 2018 with the goal of providing thermophysical and thermochemical characterization of key molten salt compounds and mixtures across their temperature and compositional domains. The MSTDB is thus divided into the thermophysical arm (MSTDB-TP) and the thermochemical arm (MSTDB-TC). The MSTDB is an effort funded by the Department of Energy, Office of Nuclear Energy (DOE-NE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, and the MSR Campaign. This report provides an overview of the MSTDB-TP v4.0 in terms of the data contained within, the state of the tools used to access the data, the availability of predictive models that leverage the raw data in the database, the preliminary status of developmental efforts that are currently underway, and an account of future goals for MSTDB-TP. The primary goal for the update from MSTDB-TP v.3.1 to v4.0 was the incorporation of surface tension data into the database; this property is important for thermal hydraulics modeling and species transport in other tools that have been developed under the NEAMS program. A breakdown of the surface tension data that have been added into MSTDB-TP v4.0 is provided herein, and the manner in which the quality of the data has been assessed is also documented. For MSTDB-TP v4.0, newly published thermophysical property data—primarily from collaborative experimental efforts under the MSR Campaign—have been incorporated into the database, and the resulting expansion is documented here. Because of the size to which MSTDB-TP has grown, the raw data format has now been recast into JavaScript Object Notation (JSON) format for easier connection with the MSTDB-TP application programming interface (API). Saline; the pre-existing comma-separated value (CSV) format has been deprecated but is still maintained, accessible, and up to date. As a final effort in packaging the MSTDB-TP v4.0 update, the graphical user interface (GUI) for MSTDB has been updated to allow full accessibility to the density and viscosity predictive models, which are based on Redlich-Kister expansions of MSTDB-TP raw data. Some other major aspects of this report, in terms of preliminary and future work, include: (1) documentation of the formalism and preliminary testing of a kinetic theory model that may act as a predictive model for thermal conductivity; (2) documentation of the candidate predictive models that may be considered in the future for surface tension, making use of the surface tension data now in MSTDB-TP v4.0; (3) a preliminary account of a data collection process that will enable the filling of additional gaps within MSTDB-TP, namely with data which have been collected computationally (e.g., through ab initio molecular dynamics).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications

Run to run variability in parallel programs caused by floating-point non-associativity has been known to significantly affect reproducibility in iterative algorithms, due to accumulating errors. Non-reproducibility can critically affect the efficiency and effectiveness of correctness testing for stochastic programs. Recently, the sensitivity of deep learning training and inference pipelines to floating-point non-associativity has been found to sometimes be extreme. It can prevent certification for commercial applications, accurate assessment of robustness and sensitivity, and bug detection. New approaches in scientific computing applications have coupled deep learning models with high-performance computing, leading to an aggravation of debugging and testing challenges. Here we perform an investigation of the statistical properties of floating-point non-associativity within modern parallel programming models, and analyze performance and productivity impacts of replacing atomic operations with deterministic alternatives on GPUs. We examine the recently-added deterministic options in PyTorch within the context of GPU deployment for deep learning, uncovering and quantifying the impacts of input parameters triggering run to run variability and reporting on the reliability and completeness of the documentation. Finally, we evaluate the strategy of exploiting automatic determinism that could be provided by deterministic hardware, using the Groq LPUTM accelerator for inference portions of the deep learning pipeline. We demonstrate the benefits that a hardware-based strategy can provide within reproducibility and correctness efforts.

Shanmugavelu, Sanjif↗

DOE OSTI2017

Optimal Allocation of Static Var Compensator via Mixed Integer Conic Programming

Shunt FACTS devices, such as, a Static Var Compensator (SVC), are capable of providing local reactive power compensation. They are widely used in the network to reduce the real power loss and improve the voltage profile. This paper proposes a planning model based on mixed integer conic programming (MICP) to optimally allocate SVCs in the transmission network considering load uncertainty. The load uncertainties are represented by a number of scenarios. Reformulation and linearization techniques are utilized to transform the original non-convex model into a convex second order cone programming (SOCP) model. Numerical case studies based on the IEEE 30-bus system demonstrate the effectiveness of the proposed planning model.

Zhang, Xiaohu↗

DOE OSTI2016

Calcium Phosphate Constraints in HLW Glasses: Phosphate Liquidus Model

About 50 million gallons of high-level mixed waste is currently stored in underground tanks at the United States Department of Energy’s (DOE’s) Hanford site in the State of Washington. The Hanford Tank Waste Treatment and Immobilization Plant (WTP) will provide DOE’s Office of River Protection (ORP) with a means of treating this waste by vitrification for subsequent disposal. The tank waste will be separated into low- and high-activity waste fractions, which will then be vitrified respectively into Immobilized Low Activity Waste (ILAW) and Immobilized High Level Waste (IHLW) products. The ILAW product will be disposed in an engineered facility on the Hanford site while the IHLW product is designed for acceptance into a national geological disposal facility for high-level nuclear waste. The ILAW and IHLW products must meet a variety of requirements before they can be accepted for disposal. Glass property-composition models will be used to control the vitrification process at the WTP to ensure that the glass meets product quality and processability requirements. The quality and the validity ranges of the property-composition models and the supporting database will greatly influence waste treatment operations, including the waste loading that is achievable in the glass for each waste composition. The current baseline models for operation of the WTP were developed under the Bechtel National, Inc. (BNI) WTP contract. These models were based on LAW and HLW glass property data over a range of compositions that was guided primarily by the WTP contract and the set of tanks specified therein. The HLW testing was directed towards waste streams from the then-planned early feed tanks for the WTP (i.e., AZ-101, AZ-102, C-106/AY-102, and C-104/AY-101). These wastes are high in iron (AZ-101, AZ-102, and C-106/AY-102) or thorium (C-104/AY-101). While the baseline models may be adequate for commissioning of the facility and processing of the specified tanks, refinements and extensions or augmentations of these models will be required to process the balance of the tank waste inventory. The overall mission of the WTP will require the immobilization of tank waste compositions that are variously dominated by mixtures of aluminum, chromium, bismuth, iron, phosphorous, zirconium, and sulfur compounds as waste-loading-limiting components. Further, the WTP contract includes minimum waste loading requirements that are generally rather modest while loadings significantly higher than those in the current baseline are likely achievable. In order to achieve these higher waste loadings and to expand the applicable composition regions, new models will be needed to support the optimized operation of the WTP. Since the development of the baseline WTP models, ORP directed a program of work at the Vitreous State Laboratory (VSL) to examine various approaches for optimization of the performance of the WTP, including increasing the waste loadings in the LAW and HLW glass products. That work involved HLW rich in Al, Al+Na, Cr, Bi, S, Zr, Sr, P and Fe, in contrast to old baseline models that were based mostly on HLW tanks that were high in Fe, with one other that was also high in Th. Glass property data collected under the ORP program were compiled and combined with data collected to develop the WTP models. The combined databases have been used to update pre-existing HLW composition models or to develop new models for properties such as Product Consistency Test (PCT) responses, spinel crystallization (one-percent crystal fraction temperature, T 1% ), melt viscosity, melt electrical conductivity, nepheline formation upon slow cooling (i.e., Canister Centerline Cooling [CCC]), and Toxicity Characteristic Leach Procedure (TCLP) Cd response. While spinel is typically the most commonly occurring phase in HLW glasses, other phases can be limiting depending on the waste and glass composition. For example, various phosphate phases can become important as the phosphorus content increases. Lithium phosphate crystallization can impact PCT releases of HLW glasses, while calcium phosphate phases forming in the cold-cap can deleteriously impact feed processing in the melter. At present, however, no models exist that relate glass composition and crystallization of phosphate phases in general and calcium phosphate in particular. The present work was therefore directed towards the initial development of such models. Future work may be directed at studying amorphous phase separation in high phosphate HLW glasses. Within the River Protection Project, feed compositions are projected using the Hanford Tank Waste Operations Simulator (HTWOS), a dynamic computer model that tracks the waste through storage, retrieval, feed staging, and treatment processes. A review of the HTWOS data shows that WTP will process 4106 batches of HLW glass for the overall mission (System Plan Rev. 6, ORP-11242), each with its own waste feed and glass composition. Some of the projected HTWOS feed batches are expected to have relatively high concentrations of phosphorous compared to typical HLW streams. In the 4106 batches of HLW for the overall WTP mission P 2 O 5 varies from 0.59 wt% to 13.00 wt%, with 95 batches containing at least 10 wt% P 2 O 5 . Occurrences of phosphate crystals have commonly been noted in glasses with high bismuth and phosphorus contents. In addition, recently collected data from a set of statistically designed HLW matrix glasses prepared and characterized to support enhancement of the HLW glass property-composition models identified apatite in a number of glass samples. However, the current HLW model for crystallization (one-percent crystal fraction temperature) is applicable only to spinel crystallization in glasses. Previous projections of waste loadings and glass volume (such as those in System Plan Rev. 6, ORP-11242) have used preliminary constraints for calcium phosphate crystallization that are based on the limited available data. Additional data and improved constraints or, preferably, models that relate glass composition to crystallization of phosphate-containing phases, are therefore needed to support HLW vitrification at the WTP. The present study focuses on the development of such a model based on data from previously characterized glasses and a set of newly fabricated glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Status of the NEAMS and ARC neutronic fast reactor tools integration to the NEAMS Workbench

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Implementation of a Drift-Flux Model in SAM for Modeling of Passively Transported GAS in Molten Salt Reactors

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is further developing the Multiphysics Object Oriented Simulation Environment (MOOSE) system thermal hydraulics (T/H) code, System Analysis Module (SAM), to include modeling of molten salt reactor (MSR) designs. MSR designs in which the fuel is dissolved in the salt coolant poses unique modeling and simulation (M&S) challenges because fission products—some of which will be noncondensable gasses—travel with the coolant, thus allowing for deposition and interaction throughout the entire system flow loop. This behavior must be modeled to successfully understand MSR system behavior during normal and off-normal operation events. In past work, SAM has been extended to include a passive species transport capability for modeling of MSR fission products that travel with the salt coolant. This work extends the model to include a drift-flux model to capture the tendency of the gas phase to move at a different velocity than the bulk liquid. Additional constitutive models have also been added for calculation of gas volume fraction, bubble radius, and interfacial area, which will be important for calculation of species phase migration in future coupling activities. Testing has been performed to ensure that the new model agrees with expected values for several simple models, but additional future work will be required to test the model for more complex geometries that will be needed for full-scale MSR simulations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Status of the NEAMS and ARC fast reactor tools integration to the NEAMS Workbench

The Workbench initiative was launched in FY-2017 within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to facilitate the transition from conventional tools to high-fidelity tools. The NEAMS Workbench provides a common user interface for model creation, real-time validation, execution, output processing, and visualization for integrated codes. The integration of the Argonne Reactor Computation (ARC) suite of codes into the NEAMS Workbench through the PyARC module was initiated in FY-2017. The ARC codes, which focus on fast reactor multiphysics analysis, contain both legacy codes like DIF3D and REBUS-3 that were developed with over 30 years of experience, and newer NEAMS additions like MC 2 -3, PERSENT, and PROTEUS. Recent work expended this integration to other tools to support the U.S. fast reactor community such as DASSH for sub-channel thermal-hydraulics, Griffin for high-fidelity deterministic transport calculations, and OpenMC for Monte Carlo simulations (with Shift integration planned for FY-2023). The integration of the “extended ARC” suite of codes into the NEAMS Workbench interface relies on the PyARC and PyGriffin modules to handles the pre- and post-processing of these codes input, and the runtime environment. The PyARC module together with the NEAMS Workbench interface are both released under Open Source Software licenses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Supporting cost-effective watershed management strategies for Chesapeake Bay using a modeling and optimization framework

Extensive efforts to adaptively manage nutrient pollution rely on Chesapeake Bay Program’s (Phase 6) Watershed Model, called Chesapeake Assessment Scenario Tool (CAST), which helps decision-makers plan and track implementation of Best Management Practices (BMPs). We describe mathematical characteristics of CAST and develop a constrained nonlinear BMP-subset model, software, and visualization framework. This represents the first publicly available optimization framework for exploring least-cost strategies of pollutant load control for the United States’ largest estuary. The optimization identifies implementation options for a BMP subset modeled with load reduction effectiveness factors, and the web interface facilitates interactive exploration of >30,000 solutions organized by objective, nutrient control level, and for ~200 counties. We assess framework performance and demonstrate modeled cost improvements when comparing optimization-suggested proposals with proposals inspired by jurisdiction plans. Stakeholder feedback highlights the framework’s current utility for investigating cost-effective tradeoffs and its usefulness as a foundation for future analysis of restoration strategies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

A Kinematic Rupture Model Generator Using Irikura's Recipe

In this project we developed GEN_SRF4 a computer program for generating kinematic rupture models, compatible with the SRF format, using Irikura and Miyake (2011) asperity-based earthquake rupture model (IM2011, hereafter). IM2011, also known as Irkura’s recipe, has been widely used to model and simulate ground motion from earthquakes in Japan. An essential part of the method is its kinematic rupture generation technique, which is based on a deterministic rupture asperity modeling approach. The source model simplicity and efficiency of IM2011 at reproducing ground motion from earthquakes recorded in Japan makes it attractive to developers and users of the Southern California Earthquake Center Broadband Platform (SCEC BB platform). Besides writing the code the objective of our study was to test the transportability of IM2011 to broadband simulation methods used by the SCEC BB platform. Here we test it using the Graves and Pitarka (2010) method, implemented in the platform. We performed broadband (0.1- -10 Hz) ground motion simulations for a M6.7 scenario earthquake using rupture models produced with both GEN_SRF4 and rupture generator of Graves and Pitarka (2016), (GP2016 hereafter). In the simulations we used the same Green’s functions, and same high frequency approach for calculating the low-frequency and high-frequency parts of ground motion, respectively.

58 GEOSCIENCES↗

DOE OSTI2024

Probabilistic Programming for Transportable Source Characterization and Uncertainty Quantification of the North Korean Nuclear Tests 2006–2017

Here, we introduce a transportable technique to determine the yield and depth of burial (DOB) from seismic source spectra of underground nuclear explosions. We demonstrate this technique on the six declared North Korean nuclear tests. This approach derives source spectra in absolute units from regional phase (Pg) amplitudes by correcting the observations for geometric spreading, attenuation, and site amplification. We couple the source spectra and explosion source models with a probabilistic programming framework that integrates deep learning techniques and Bayesian modeling. This approach permits the exchange of information across various data categories to quantify both the data and model uncertainty. This technique stands out as an innovative use of broad‐area propagation models, making it transportable across various geologic settings. This method proves to be effective in scenarios with diverse and/or limited observational data, even when the source depth is unknown. We present new independent estimates of absolute yield and DOB that are consistent with the prior assessments, underscoring the potential of this method in enhancing transportable nuclear explosion monitoring capabilities.

58 GEOSCIENCES↗

DOE OSTI2016

A customizable model for chronic disease coordination: Lessons learned from the coordinated chronic disease program

In 2012, the Centers for Disease Control and Prevention provided funding and technical assistance to all states and territories to implement the Coordinated Chronic Disease Program, marking the first time that all state health departments had federal resources to coordinate chronic disease prevention and control programs. This article describes lessons learned from this initiative and identifies key elements of a coordinated approach. We analyzed 80 programmatic documents from 21 states and conducted semistructured interviews with 7 chronic disease directors. Six overarching themes emerged: 1) focused agenda, 2) identification of functions, 3) comprehensive planning, 4) collaborative leadership and expertise, 5) managed resources, and 6) relationship building. Furthermore, these elements supported 4 essential activities: 1) evidence-based interventions, 2) strategic use of staff, 3) consistent communication, and 4) strong program infrastructure. On the basis of these elements and activities, we propose a conceptual model that frames overarching concepts, skills, and strategies needed to coordinate state chronic disease prevention and control programs.

60 APPLIED LIFE SCIENCES↗