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DOE OSTI2021

U.S. Industry Opportunities for Advanced Nuclear Technology Development (Phase III)

This is the third phase of a work scope which has been focused on providing a framework for and preserving key experimental programs and experiences which are critical to the licensing basis of currently operating nuclear reactors or which could be used in the safety basis for the next generation of reactors. The first phase of this effort, documented in Reference 1, focused on compiling a list of key experimental programs through an international survey of reactor safety professionals working in licensing, design, and academia. The second phase in this effort, documented in Reference 2, focused on creating a searchable database framework in which to organize the results from the international survey and to perform detailed research on several key programs to provide the framework for how to categorize the references which could be located. The purpose of the third phase is to perform a high-level research effort on each experiment/experience and to determine if sufficient data, reports, and results have already been captured to consider the program archived for future generators of nuclear professionals.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from the scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, here we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ~35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles’ text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% accuracy on morphology prediction, 0.99 AUC on protocol identification, and up to a 0.87 F1-score on chemical entity recognition. In addition to processing articles’ text, microscopy images of nanomaterials within the articles are also automatically identified and analyzed to determine the nanomaterials’ morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design.

36 MATERIALS SCIENCE↗

NASA NTRS1996

Predictions of Control Inputs, Periodic Responses and Damping Levels of an Isolated Experimental Rotor in Trimmed Flight

Since the early 1990s the Aeroflightdynamics Directorate at the Ames Research Center has been conducting tests on isolated hingeless rotors in hover and forward flight. The primary objective is to generate a database on aeroelastic stability in trimmed flight for torsionally soft rotors at realistic tip speeds. The rotor test model has four soft inplane blades of NACA 0012 airfoil section with low torsional stiffness. The collective pitch and shaft tilt are set prior to each test run, and then the rotor is trimmed in the following sense: the longitudinal and lateral cyclic pitch controls are adjusted through a swashplate to minimize the 1/rev flapping moment at the 12 percent radial station. In hover, the database comprises lag regressive-mode damping with pitch variations. In forward flight the database comprises cyclic pitch controls, root flap moment and lag regressive-mode damping with advance ratio, shaft angle and pitch variations. This report presents the predictions and their correlation with the database. A modal analysis is used, in which nonrotating modes in flap bending, lag bending and torsion are computed from the measured blade mass and stiffness distributions. The airfoil aerodynamics is represented by the ONERA dynamic stall models of lift, drag and pitching moment, and the wake dynamics is represented by a state-space wake model. The trim analysis of finding, the cyclic controls and the corresponding, periodic responses is based on periodic shooting with damped Newton iteration; the Floquet transition matrix (FTM) comes out as a byproduct. The stabillty analysis of finding the frequencies and damping levels is based on the eigenvalue-eigenvector analysis of the FTM. All the structural and aerodynamic states are included from modeling to trim analysis. A major finding is that dynamic wake dramatically improves the correlation for the lateral cyclic pitch control. Overall, the correlation is fairly good.

Gaonkar, G. H.↗

DOE OSTI2023

High-throughput ab initio design of atomic interfaces using InterMatch

Forming a hetero-interface is a materials-design strategy that can access an astronomically large phase space. However, the immense phase space necessitates a high-throughput approach for an optimal interface design. Here we introduce a high-throughput computational framework, InterMatch, for efficiently predicting charge transfer, strain, and superlattice structure of an interface by leveraging the databases of individual bulk materials. Specifically, the algorithm reads in the lattice vectors, density of states, and the stiffness tensors for each material in their isolated form from the Materials Project. From these bulk properties, InterMatch estimates the interfacial properties. We benchmark InterMatch predictions for the charge transfer against experimental measurements and supercell density-functional theory calculations. We then use InterMatch to predict promising interface candidates for doping transition metal dichalcogenide MoSe 2 . Finally, we explain experimental observation of factor of 10 variation in the supercell periodicity within a few microns in graphene/α-RuCl 3 by exploring low energy superlattice structures as a function of twist angle using InterMatch. We anticipate our open-source InterMatch algorithm accelerating and guiding ever-growing interfacial design efforts. Moreover, the interface database resulting from the InterMatch searches presented in this paper can be readily accessed online.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Density pedestal prediction model for tokamak plasmas

Abstract A model for the pedestal density prediction based on neutral penetration combined with pedestal transport is presented. The model is tested against a pedestal database of JET-ILW Type I ELMy H-modes showing good agreement over a wide range of parameters both in standalone modelling (using the experimental temperature profile) and in full Europed modelling that predicts both density and temperature pedestals simultaneously. The model is further tested for ASDEX Upgrade and MAST-U Type I ELMy H-modes and both are found to agree with the same model parameters as for JET-ILW. The JET-ILW experiment where the isotope of the main ion is varied in a D/T scan at constant gas rate and constant β N is successfully modelled as long as the separatrix density ( n e,sep ) and pedestal transport coefficient ratio ( D / χ ) are varied in accordance with the experimentally observed variation of n e,sep and the isotope dependence of D / χ found in gyrokinetic simulations. The predictions are found to be sensitive to n e,sep which is why the model is combined with an n e,sep model to predict the pedestal for the STEP fusion reactor.

Physics↗

DOE OSTI2022

Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern

A prototype application for machine-readable literature is investigated. The program is called pyDataRecognition and serves as an example of a data-driven literature search, where the literature search query is an experimental data set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier and full reference of top-ranked papers together with a stack plot of the user data alongside the top-five database entries. The paper describes the approach and explores successes and challenges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Automatic identification and classification of Palomar Transient Factory astrophysical objects in GLADE

Palomar Transient Factory (PTF) is a comprehensive detection system for the identification and classification of transient astrophysical objects. In this paper, we make two significant contributions to the PTF pipeline. First, we present an experimental study that evaluates a novel implementation of the real-time classifier in GLADE - a parallel data processing system that combines the efficiency of a database with the extensibility of map-reduce. We show how each stage in the classifier maps optimally into GLADE tasks by taking advantage of the unique features of the system - range-based data partitioning, columnar storage, multi-query execution, and in-database support for complex aggregate computation. Second, we introduce a novel parallel similarity join algorithm for advanced transient classification. In conclusion, we implement this algorithm in GLADE and execute it on a massive supercomputer with more than 3,000 threads, achieving more than three orders of magnitude improvement over the PostgreSQL solution.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2011

An Analysis of Database Replication Technologies with Regard to Deep Space Network Application Requirements

The Deep Space Network (DSN) has three communication facilities which handle telemetry, commands, and other data relating to spacecraft missions. The network requires these three sites to share data with each other and with the Jet Propulsion Laboratory for processing and distribution. Many database management systems have replication capabilities built in, which means that data updates made at one location will be automatically propagated to other locations. This project examines multiple replication solutions, looking for stability, automation, flexibility, performance, and cost. After comparing these features, Oracle Streams is chosen for closer analysis. Two Streams environments are configured - one with a Master/Slave architecture, in which a single server is the source for all data updates, and the second with a Multi-Master architecture, in which updates originating from any of the servers will be propagated to all of the others. These environments are tested for data type support, conflict resolution, performance, changes to the data structure, and behavior during and after network or server outages. Through this experimentation, it is determined which requirements of the DSN can be met by Oracle Streams and which cannot.

Oracle Database File System↗

DOE OSTI2024

Development and Experimental Optimization of High-Temperature Modeling Tools and Methods for Concentrated Solar Power Particle - Systems

A novel, open-source radiative modeling toolset was developed to extend the functionality of particle-based modeling software (e.g. discrete element method (DEM)) to environmental conditions relevant to concentrated solar power applications. This toolset was optimized for deployment on desktop workstations instead of high-performance computing systems, to render such tools more accessible to the research community. Both particle-based modeling and radiative exchange modeling are computationally expensive and often require specialized programming expertise, making these methods cumbersome to use. Recent developments in DEM software by DCS Computing have greatly reduced these challenges, providing a graphical-user-interface based platform and modeling optimization for desktop workstations, HPCs, and cloud computing. The University of Dayton leveraged the experience of DCS Computing in developing a user-friendly, open-source radiative heat transfer expansion for DEM modeling. The University of Dayton DEM+ radiative modeling toolset was developed using a combination of fundamental experimental measurements, modeling, and simplified flow experiments over a range of temperatures and flow conditions. The toolset provides researchers with access to multiple radiative models including an accelerated Monte-Carlo Ray Tracing (application agnostic, highly computationally expensive), an expanded database of distance-based approximations (application limited, computationally light), and a weighted blending of the two methods capable of achieving over 90% reduction in computation time with equivalent accuracy compared to Monte-Carlo Ray Tracing. Through a graphical user interface, users can customize the radiative models to match their desired accuracy and available computational resources, improving access to particle based modeling for the research community. Ceramic sintered bauxite proppants were used in modeling and experimentally as a baseline. Both the radiative heat transfer and flow properties for particulate systems were investigated at elevated temperatures up to 800 °C. The major accomplishments for this work include a verified, open-source radiative modeling toolset to be distributed amongst the research community and the fabrication of three small-scale test facilities to investigate particle behavior and tune DEM flow properties for operation up to 800 °C. The findings have been shared with the research community via conference modeling workshops, deployment of the tools in DCS Computing Aspherix®, and open-source access to the developed radiative modeling tool. The development of next-generation CSP facilities and thermal energy storage systems based on ceramic particles requires providing access to computationally efficient and accurate modeling tools. Particles will experience a wide range of environments (20-800 °C) and handling conditions (dilute curtains or dense packing), requiring specially designed and optimized equipment. Optimizing solid particle physics models and establishing best-practices for particle modeling in CSP environments will assist researchers with designing optimized equipment, accelerating the deployment of more economically-competitive CSP facilities.

14 SOLAR ENERGY↗

NASA NTRS1994

Three-Dimensional Navier-Stokes Simulations with Two-Equation Turbulence Models of Intersecting Shock-Waves/Turbulent Boundary Layer at Mach 8.3

An investigation of the numerical simulation with two-equation turbulence models of a three-dimensional hypersonic intersecting (SWTBL) shock-wave/turbulent boundary layer interaction flow is presented. The flows are solved with an efficient implicit upwind flux-difference split Reynolds-averaged Navier-Stokes code. Numerical results are compared with experimental data for a flow at Mach 8.28 and Reynolds number 5.3x10(exp 6) with crossing shock-waves and expansion fans generated by two lateral 15 fins located on top of a cold-wall plate. This experiment belongs to the hypersonic database for modeling validation. Simulations show the development of two primary counter-rotating cross-flow vortices and secondary turbulent structures under the main vortices and in each corner singularity inside the turbulent boundary layer. A significant loss of total pressure is produced by the complex interaction between the main vortices and the uplifted jet stream of the boundary layer. The overall agreement between computational and experimental data is generally good. The turbulence modeling corrections show improvements in the predictions of surface heat transfer distribution and an increase in the strength of the cross-flow vortices. Accurate predictions of the outflow flowfield is found to require accurate modeling of the laminar/turbulent boundary layers on the fin walls.

Bardina, J. E.↗

DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

A database of ultrastable MOFs reassembled from stable fragments with machine learning models

High-throughput screening of hypothetical metal-organic framework (MOF) databases can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning (ML) models to identify MOFs that are thermally stable and stable upon activation. We separate these MOFs into their building blocks and recombine them to make a new hypothetical MOF database of over 50,000 structures with orders of magnitude more (1) connectivity nets and (2) inorganic building blocks than were present in prior databases. Further, this database shows a 10-fold enrichment of ultrastable MOF structures that are stable upon activation and more than 1 standard deviation more thermally stable than the average experimentally characterized MOF. For nearly 10,000 ultrastable MOFs, we compute elastic moduli to confirm that these materials have good mechanical stability, and we report methane deliverable capacities. We identify privileged metal nodes in ultrastable MOFs that optimize gas storage and mechanical stability simultaneously.

36 MATERIALS SCIENCE↗

DOE OSTI2018

A power-balance model for local helicity injection startup in a spherical tokamak

A 0D circuit model for predicting I p ( t ) in Local Helicity Injection (LHI) discharges is developed. Analytic formulas for estimating the surface flux of finite-A plasmas developed are modified and expanded to treat highly shaped, ultralow-A tokamak geometry using a database of representative equilibria. Model predictions are compared to sample LHI discharges in the A ~ 1 Pegasus spherical tokamak, and are found to agree within 15% of experimental I p ( t ). High performance LHI discharges are found to follow the Taylor relaxation current limit for approximately the first half of the current ramp, or I p ≲ 75 kA. The second half of the current ramp follows a limit imposed by power-balance as plasmas expand from high- A to ultralow- A . Here, this shape evolution generates a significant drop in external plasma inductance, effectively using the plasma’s initially high inductance to drive the current ramp and provide > 70% of the current drive V-s. Projections using this model indicate the relative influences of higher helicity input rate and injector current on the attainable total plasma current.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

Development plan for the External Hazards Experimental Group. Light Water Reactor Sustainability Program

This report describes the development plan for a new multi-partner External Hazards Experimental Group (EHEG) coordinated by Idaho National Laboratory (INL) within the Risk-Informed Safety Margin Characterization (RISMC) technical pathway of the Light Water Reactor Sustainability Program. Currently, there is limited data available for development and validation of the tools and methods being developed in the RISMC Toolkit. The EHEG is being developed to obtain high-quality, small- and large-scale experimental data validation of RISMC tools and methods in a timely and cost-effective way. The group of universities and national laboratories that will eventually form the EHEG (which is ultimately expected to include both the initial participants and other universities and national laboratories that have been identified) have the expertise and experimental capabilities needed to both obtain and compile existing data archives and perform additional seismic and flooding experiments. The data developed by EHEG will be stored in databases for use within RISMC. These databases will be used to validate the advanced external hazard tools and methods.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Utica Shale Energy and Environment Laboratory Final Scientific/Technical Report

The Ohio State University (OSU) was awarded a contract on October 1, 2014, from the Department of Energy National Energy Technology Laboratory (DOE-NETL) to develop the Utica Shale Energy and Environment Laboratory (USEEL) in the Utica-Point Pleasant shale play of the Appalachian Basin. It was designed to be an environmental and technology development lab that would enable the academic, industry, government, and non-governmental organization (NGO) research communities to better understand unconventional oil and gas (UOG) engineering practices and technology to increase production and safety, and decrease environmental effects. Political and economic consequences necessitated changes in project site location and design, from the Ohio State Eastern Agricultural Research Station (EARS) and the Muskingum Watershed Conservancy District (MWCD) in east-central Ohio to a site located at an Energy Corporation of America (ECA) Marcellus drill pad in Greene County, Pennsylvania. Although the overall project progressed little beyond planning and administration before termination on September 18, 2017, significant research and MS or PhD investigations were completed or continue today. An experimental study design for site specific and regional baseline assessments was developed utilizing ecoregions, United States Geological Survey (USGS) Hydraulic Unit Code watersheds, and GIS technology and databases. This can be utilized to build a defensible and scalable management and research framework for UOG investigations that can be extrapolated for predictive and comparative analyses. The most commonly mentioned perceived risks of shale energy development identified in a socioeconomic analysis included impacts to the environment and water resources, traffic and road deterioration, and crime. Economic benefits, such as the windfall wealth to residents, job opportunities, and the demand for hotels and restaurants emerged, as the main positive impact to the community. Preliminary results of an analysis of the location and mechanisms for extremely high pore pressures of the deep Utica-Point Pleasant formations of southwestern Pennsylvania and their possible relationship to high initial production rates indicate that the overpressure is caused by a combination disequilibrium compaction and fluid expansion. Drilling fluids research designed a proof-of-concept device to evaluate desalination and solvent separation in hydraulic fracturing flowback water that could facilitate water recycling and potentially mitigate deep-well injection. A groundwater study was devised to characterize the subsurface aquifers in terms of the geological framework and groundwater chemistry, including the chemistry of dissolved gases and salts. Brines and gases associated with hydrocarbon migration in groundwater (including noble gases and hydrocarbons) were to be specifically fingerprinted for use as tracers of fluid migration. Trace element analysis of fish otoliths, using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), is being utilized to assess surface water quality. This methodology will be used to reconstruct the exposure histories of fish from watersheds and discriminate between the signatures associated with hydraulic fracturing and coal mining activities.

03 NATURAL GAS↗

DOE OSTI2016

Plan to Verify and Validate Multi-Hazard Risk-Informed Margin Management Methods and Tools

This report describes the development plan for verification and validation (V&V) of multihazard risk informed margin management (RIMM) methods and tools. The plan will include using new INL developed tool sets coupled with the new multi-partner External Hazards Experimental Group (EHEG) coordinated by the Idaho National Laboratory (INL) within the Risk-Informed Safety Margin Characterization (RISMC) technical pathway of the Light Water Reactor Sustainability Program. The RISMC pathway is developing a suite of new tools and methods for advanced evaluation of facility risk. The RISMC toolkit, includes tools and methods focused on evaluation of risk from external hazards (e.g, seismic and flooding events), which have been shown to be dominant risk contributors in probabilistic risk assessments performed for operating nuclear power plants (NPPs). The external events activity (EEA) within the RISMC pathway is tasked with developing tools and methods focused on evaluation of multi-hazard external risk (e.g, seismic and flooding events). The EHEG will provide technical expertise and experimental large-scale testing data needed for development and validation of tools and methods in the RISMC toolkit for external hazard safety evaluations. The EEA within RISMC has two key elements: (1) an organizational and research framework provided by INL and (2) a coordinated group of university and national laboratory partners with the complementary expertise and experimental capabilities needed to conduct large-scale external hazard-focused experiments. This cooperative group will allow INL to leverage a range of existing capabilities to meet the unique needs of RISMC tool and method development. In addition, the capabilities of the EHEG could be used to address needs of other national laboratories. Currently, there is limited data available for development and validation of the tools and methods being developed in the RISMC Toolkit. The EHEG is being developed to obtain high quality, large-scale experimental data validation of RISMC tools and methods in a timely and cost-effective way. The group of universities and national laboratories that will eventually form the EHEG (which is ultimately expected to include both the initial participants and other universities and national laboratories that have been identified) have the expertise and experimental capabilities needed to both obtain and compile existing data archives and perform additional seismic and flooding experiments. The data developed by EHEG will be stored in databases within RISMC. These databases will be used to validate the advanced external hazard tools and methods. The numerical tools under development in RISMC are used to evaluate the impacts that external hazards have on nuclear power plants. These analysis tools will be used to advanced current risk calculation processes and reduce uncertainty in these calculations. To have confidence in the predictive capability of these numerical tools it is important to verify and validate them. To verify and validate numerical tools the user must understand what physics is being used to represent the problem (i.e. constitutive models used to represent the soil and structure), the numerical solver approach used (i.e. finite element), what data already exists that could be used to validate the tools, and what data is needed for validation.

31274↗

DOE OSTI2020

Enhanced Hanford Low-Activity Waste Glass Property Data Development (Phase 3)

This work was performed for the U.S. Department of Energy (DOE) Office of River Protection (ORP) to provide expert evaluation and experimental work in support of the River Protection Project vitrification technology development1. The long-term objective of this work is to expand the property-composition database for Hanford site low-activity waste (LAW) glasses and property-composition models to cover the balance of the mission for the Hanford Waste Treatment and Immobilization Plant (WTP). When this effort is complete, enhanced LAW glass property-composition models will be developed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Quality Assurance of the Irradiated EBR-II Metallic Fuels Data

The U.S. DOE-NE’s Advanced Reactor Technologies (ART) Fast Reactor Program (FRP) and the NE-4 Advanced Fuels Campaign (AFC) have jointly undertaken efforts to qualify post-irradiation examination (PIE) data for Experimental Breeder Reactor II (EBR-II) and Fast Flux Test Facility (FFTF) metallic fuels. Among the thousands of PIE datasets in the Metallic Fuels Irradiation & Physics Database (FIPD), data from selected EBR-II experiments have been prioritized as the most valuable to the U.S. nuclear industry for licensing activities related to metallic fuel-based advanced fast reactors. This report summarizes the general data qualification progress of the EBR-II and FFTF experiments, with focus on the selected EBR-II experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗