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DOE OSTI2018

Cold Commissioning Simulant Selection for the Hanford Tank Waste Treatment Plant LAW Facility - 18465

Commissioning the Low-Activity Waste (LAW) Facility of the Hanford Tank Waste Treatment and Immobilization Plant (WTP) will require large volumes (∼2,000,000 liters) of a non-radioactive, chemically analogous waste simulant. Cold commissioning will provide for operator training in an integrated facility, demonstration of mechanical process equipment capacity, environmental performance demonstration of emissions control systems, and process control systems shakedown and tuning. Selection of the cold commissioning simulant is following a structured four-phase process to arrive at the preferred composition and recipe. Phases I, II and III are complete. Phase I laid the framework for decision making with preparation of a Project Management Plan, an update to the Simulant Development, Approval, Validation, and Documentation procedure, and a kickoff meeting to gather stakeholder input into the simulant definition and selection criteria. Phase II consisted of a comprehensive literature search and compilation of LAW simulant recipes, followed by initial screening against the selection criteria identified in Phase I. This process identified 72 candidate simulant recipes and down-selected 9 for further study. Phase III studies involved a) statistical comparisons of the similarity of candidate simulant compositions to the tank waste feed vector, b) process modelling of the commissioning sequence with each simulant to estimate glass former and simulant material consumption, offgas and effluent volumes and compositions, and product glass volumes and compositions, and c) evaluation of simulants against the immobilized LAW glass composition algorithm to determine allo able waste loading to ensure compliance with product specifications and process constraints. The results of these studies identified two of the candidate recipes to be carried forward to full-scale testing. Phase IV consists of testing these two recipes at vendor facilities to determine scalability, fabricability, and stability of the simulants when produced at industrial scale. Scalability is the ability to take a recipe from bench scale (1 to 40 liters) batch size to full scale (>35,000 liters) batch size. Fabricability is the ability of a recipe to be prepared in large batches without the need for complex or expensive mechanical equipment, heating, or cooling systems. Stability is the ability of a simulant to withstand long term storage and variations in environmental conditions without unacceptable changes in key characteristics (the set of chemical, physical, and rheological parameters that the simulant is intended to exhibit during cold commissioning tests). Simulants will also be tested for compatibility with other startup material (i.e., frit), tuning simulants, and environmental performance demonstration test spiking solutions to ensure against unexpected situations. Using a structured process is essential when making high impact decisions such as waste simulant selection. Proper planning and a clear explanation of how the decision process will unfold is key to obtaining stakeholder buy-in as more information is gathered. The documentation resulting from this structured selection process will be compiled and summarized in a Cold Commissioning Simulant Definition deliverable due to the Department of Energy 24 months before the start of Cold Commissioning. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2013

Development of a tool dedicated to the evaluation of hydrogen term source for technological Wastes: assumptions, physical models, and validation

In radioactive waste packages hydrogen is generated, in one hand, from the radiolysis of wastes (mainly organic materials) and, in the other hand, from the radiolysis of water content in the cement matrix. In order to assess hydrogen generation 2 tools based on operational models have been developed. One is dedicated to the determination of the hydrogen source term issues from the radiolysis of the wastes: the STORAGE tool (Simulation Tool Of Emission Radiolysis Gas), the other deals with the hydrogen source term gas, produced by radiolysis of the cement matrices (the Damar tool). The approach used by the STORAGE tool for assessing the production rate of radiolysis gases is divided into five steps: 1) Specification of the data packages, in particular, inventories and radiological materials defined for a package medium; 2) Determination of radiochemical yields for the different constituents and the laws of behavior associated, this determination of radiochemical yields is made from the PRELOG database in which radiochemical yields in different irradiation conditions have been compiled; 3) Definition of hypothesis concerning the composition and the distribution of contamination inside the package to allow assessment of the power absorbed by the constituents; 4) Sum-up of all the contributions; And finally, 5) validation calculations by comparison with a reduced sampling of packages. Comparisons with measured values confirm the conservative character of the methodology and give confidence in the safety margins for safety analysis report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Expanded analysis of machine learning models for nuclear transient identification using TPOT

Industries around the world are becoming more and more data driven. The nuclear field is no exception with several different applications being proposed. One popular area of research is the use of machine learning in transient detection. This paper seeks to build upon a previous study which made use of the AutoML package TPOT to train traditional machine learning models to classify transient events occurring with a reactor. Synthetic data was once again collected using a GPWR reactor simulator. Data on 12 different events was collected using 15 different initial conditions. Here, a dataset consisting of over 100,000 data points was compiled and used to train 7 different machine learning models using a pre-defined TPOT dictionary with 12 different preprocessing techniques. Three of the trained models were able to produce validation results in the 90s with the expanded dataset. Once the models were trained, it was possible to look into where during the simulation, misclassifications occurred. Using these three models, analysis was done to determine if TPOT could be used to train models that were effective if important features were missing. The results from this were positive with the newly trained models scoring close to the original models. Finally, to conclude this study, the three high performing models were retrained using different random states to see if there was any major variation when different states were used.

42 ENGINEERING↗

DOE OSTI2024

Idaho National Laboratory Integrated Multisite SSHAC Level 3: Probabilistic Volcanic Hazards Assessment

The Idaho National Laboratory (INL) resides on the eastern Snake River Plain (ESRP), part of the Snake River Plain (SRP) with a complex origin and geologic history of volcanism. Much of the Quaternary (last 2.58 million years) volcanism within 400 km of INL is genetically associated with a major thermal anomaly referred to as the Yellowstone hotspot, which is currently located more than 180 km northeast of INL. Near INL, local volcanic sources include silicic domes near its southern border, and numerous dike-fed basaltic vents of the ESRP, some of which are near or within the INL boundaries. Quantitative probabilistic assessments of screened-in volcanic hazardous phenomena for nine different facility complexes at the INL are presented for a Senior Seismic Hazard Analysis Committee (SSHAC) Level 3 (SL3) study. The comprehensive, integrated multisite SSHAC study consists of a single regional Probabilistic Volcanic Hazards Assessment (PVHA) that pertains to all nine INL facility complexes, with site-specific information developed at each respective area of interest (AOI), referred to as the "INL facility AOI" (Figure ES-1). Hazard products are generated for specified INL facility AOIs for use by multiple stakeholders from different agencies in risk-informed decision-making regarding site selection, operations, and design of nuclear facilities at INL, consistent with U.S. Department of Energy (DOE) and U.S. Nuclear Regulatory Commission (NRC) regulatory guidance. The study also serves as the basis for future periodic safety assessments required for existing DOE facilities, such as 10-year evaluations of natural-phenomena hazards. Elements of the INL PVHA including initial characterization, screening, quantitative assessments of eruption and hazard potential, and consideration of facility needs are conducted using the three phases of the SSHAC process: evaluation, integration, and documentation. As per regulatory guidance, the SSHAC framework provided the necessary processes and procedures for the PVHA Technical Integration (TI) team, at three workshops, five formal working meetings and many TI team remote meetings, to conduct initial characterization, screen the volcanic hazards, create PVHA model inputs, exercise those models in the PVHA, consider facility-specific volcanic hazard needs, and perform final hazard calculations. The Participatory Peer Review Panel (PPRP) provided independent oversight and performed process and technical reviews of the PVHA throughout its duration. The study included an extensive New Data Collection and Analyses (NDCA) program developed by the PVHA TI team to reduce uncertainties in hazard-significant elements in the PVHA model. NDCA activities generated 20 reports providing important contributory datasets and results to the project database for characterizing the ESRP. For example, a report compiling the dimensions of ESRP shield volcanoes and lava fields was used to construct volcanic footprints (areas of impact), was compared with data from INL subsurface cores, and was used to validate the results of lava-flow inundation modeling on the contemporary terrain. Another example is the acquisition of aeromagnetic data over INL and its surrounding area, with maps of buried magmatic features (e.g., subsurface volcanoes and swarms of feeder dikes) that informed the PVHA conceptual model of volcanism. The SSHAC evaluation process was used to conduct all elements of the PVHA including initial characterization and screening. Existing data and NDCA activities provided the foundation to develop the tectonomagmatic conceptual model of volcanism for the region of geographical interest in the SRP and Yellowstone hotspot volcanic system, and for considering volcanoes in the western US. Considering Quaternary volcanic sources active during this period, the screening approach identified and evaluated magma compositions, types of eruptive and intrusive phenomena, types of hazardous phenomena, and proximity of sources to INL. The PVHA TI team evaluated 18 types of magmatic sources in terms of 20 potentially hazardous phenomena, resulting in 360 screening decisions. The screening process resulted in 140 screened-in hazardous volcanic phenomena for Quaternary volcanic sources 1) proximal to INL facility complexes in the ESRP (64 basaltic and 54 silicic), 2) regional sources associated with the Yellowstone caldera system and Blackfoot Reservoir volcanic field (7), and 3) more distal sources from thirteen Cascade volcanoes and two volcanoes at Long Valley caldera (CA).

58 GEOSCIENCES↗

DOE OSTI2014

Toward Understanding the Effect of Low-Activity Waste Glass Composition on Sulfur Solubility

The concentration of sulfur in nuclear waste glass melter feed must be maintained below the point where salt accumulates on the melt surface. The allowable concentrations may range from 0.37 to over 2.05 weight percent (of SO3 on a calcined oxide basis). If the amount of sulfur exceeds its tolerance level a molten salt will accumulate and upset melter operations and potentially shorten melter useful life. Therefore relatively conservative limits have been placed on sulfur loading in melter feed which in-turn significantly impacts the amount of glass that will be produced, in particular at the Hanford site. Crucible-scale sulfur solubility data and scaled melter sulfur tolerance data have been collected on simulated Hanford waste glasses over the last 15 years. These data were compiled and analyzed. A model was developed to predict the solubility of SO3 in glass based on 312 individual glass compositions. This model was shown to well represent the data, accounting for over 80% of the variation in data and was well validated. The model was also found to accurately predict the tolerance for sulfur in melter feed based on 19 scaled melter tests. The model is appropriate for control of waste glass processing which includes uncertainty quantification. The model also gives quantitative estimates of component concentration effects on sulfur solubility. The components that most increase sulfur solubility are Li2O > V2O5 ≈ TiO2 < CaO < P2O5 ≈ ZnO. The components that most decrease sulfur solubility are Cl > Cr2O3 > SiO2 ≈ ZrO2 > Al2O3.

Borosilicate↗

DOE OSTI2018

Development and Rigorous Validation of Modules for Predicting Organic Aerosol CCN Activity from Molecular Properties

The indirect interaction of anthropogenic aerosols with clouds (aerosol indirect forcing) represents one of the main uncertainties in climate projections, and reducing this uncertainty requires improved understanding of the aerosol life cycle, including atmospheric aging and the ability to nucleate cloud droplets or crystals. A key parameter that links the chemical properties of aerosol to its nucleating properties, and ultimately to aerosol indirect forcing, is its hygroscopicity (ability to uptake water). One way to compare and systematize the ability of various chemical compounds or particle types to nucleate cloud drops is via the hygroscopicity parameter κ, which can be used directly in global models to simulate aerosol indirect forcing. In this project we further investigated the relationships between the chemical composition of organic aerosol particles, which are known to be important components of atmospheric particulate matter, and their ability to serve as cloud condensation nuclei (CCN). In particular, we isolated the role of viscosity on κ by applying methods similar to those we used previously in our DOE/ASR research. Our motivation for the focus on viscosity was through its link to particle phase state. Particle phase affects water uptake on timescales relevant to the laboratory and also likely relevant to atmospheric processes, with reduced apparent hygroscopicities for semisolid and solid particles, compared with liquid particles. Water uptake itself affects viscosity, an important feedback that we explored in this project. The objectives of the project were as follows: (1) measure the effects of molecular structure, temperature, and relative humidity on the viscosity of organic aerosol formed via controlled laboratory experiments, (2) measure the effects of organic aerosol viscosity on water uptake and CCN activity above and below water saturation, (3) further develop the κ parameterization of organic aerosol hygroscopicity to include viscosity/phase, and (4) experimentally validate the κ parameterization for organic aerosol with chemical composition ranging from relatively simple to complex. It was proposed that particle viscosity exerts a kinetic control on equilibrium water uptake. High viscosity was expected to delay water uptake based on diffusional limitations. This would manifest itself in reduced observed CCN activity or κ. Understanding the interplay between water content and viscosity (composition control on particle viscosity) and between viscosity and equilibrium water content (diffusional control on water uptake) were the overall science objectives of this grant. Targeted experiments were carried out to test the kinetic control hypothesis. These experiments included studies that explicitly measured the equilibration time scale of water uptake, and experiments that tested the role of viscous particle coatings on the observed CCN activity. From the combined work, it is concluded that mass transfer limitations by glassy organic particles are unlikely to affect cloud droplet activation near laboratory temperatures. This implies that viscosity does not need to be included in parameterizations that link particle chemical composition and κ. A parameterization to predict κ through the UNIFAC group contribution method was developed with previous ASR funding. As planned, this computational module was further developed and validated here through targeted laboratory studies. The computational module has been made available as free software under the GNU public license. A database of κ-values to aid model development has been compiled and is available online via an open access model. In conducting this work, significant effort was made to understand how aerosol composition, including the associated aerosol water content, affects viscosity. This included a study detailing the influence of functional group composition on organic aerosol viscosity. We identified a strong link between volatility and viscosity. Functional groups responsible for low vapor pressure also promote high viscosity. Specifically, carboxylic acid and hydroxyl groups were identified as major contributors to high viscosity. Several studies were carried out to characterize the link between composition and the glass-to-liquid phase transition. Compositions tested include models of organic/inorganic particles and models of complex organic aerosol formed through secondary processes. A simple computational model to predict the amorphous phase diagram was developed and tested. Excellent agreement between model and measurements was obtained for organic/inorganic mixtures. The computational model is simple enough to be included in global model simulations. However, constraints of input parameters such as the Gordon-Taylor constant for binary mixtures for a wide range of complex mixtures have not yet been fully evaluated. Combined, the performed work provides new constraints on the amorphous phase state of organic aerosols.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Building a genome-based understanding of bacterial pH preferences

The environmental preferences of many microbes remain undetermined. This is the case for bacterial pH preferences, which can be difficult to predict a priori despite the importance of pH as a factor structuring bacterial communities in many systems. We compiled data on bacterial distributions from five datasets spanning pH gradients in soil and freshwater systems (1470 samples), quantified the pH preferences of bacterial taxa across these datasets, and compiled genomic data from representative bacterial taxa. While taxonomic and phylogenetic information were generally poor predictors of bacterial pH preferences, we identified genes consistently associated with pH preference across environments. We then developed and validated a machine learning model to estimate bacterial pH preferences from genomic information alone, a model that could aid in the selection of microbial inoculants, improve species distribution models, or help design effective cultivation strategies. More generally, we demonstrate the value of combining biogeographic and genomic data to infer and predict the environmental preferences of diverse bacterial taxa.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Probing the distance-duality relation with high- z data

Measurements of strong gravitational lensing jointly with type Ia supernovae (SNe Ia) observations have been used to test the validity of the cosmic distance duality relation (CDDR), D{sub L}( z )/[(1+ z ){sup 2D{sub A}}( z )]=η=1, where D{sub L}(z) and D{sub A}(z) are the luminosity and the angular diameter distances to a given redshift z , respectively. However, several lensing systems lie in the interval 1.4 ≤ z ≤ 3.6 i.e., beyond the redshift range of current SNe Ia compilations ( z ≈ 1.50), which prevents this kind of test to be fully explored. In this paper, we circumvent this problem by testing the CDDR considering observations of strong gravitational lensing along with SNe Ia and (a subsample from) the latest gamma-ray burst distance modulus data, whose redshift range is 0.033 ≤ z ≤ 9.3. We parameterize their luminosity distances with a second degree polynomial function and search for possible deviations from the CDDR validity by using four different η( z ) functions: η( z )=1+η{sub 0z}, η( z )=1+η{sub 0z}/(1+ z ), η( z )=(1+ z ){sup η{sub 0}} and η( z )=1+η{sub 0ln}(1+ z ). Unlike previous tests done at redshifts lower than 1.50, the likelihood for η{sub 0} depends strongly on the η( z ) function considered, but we find no significant deviation from the CDDR validity (η{sub 0}=0). However, our analyses also point to the fact that caution is needed when one fits data in higher redshifts to test the CDDR as well as a better understanding of the mass distribution of lenses also is required for more accurate results.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Supporting multiple hardware architectures at CMS: the integration and validation of POWER9

Computing resources in the Worldwide LHC Computing Grid (WLCG) have been based entirely on the x86 architecture for more than two decades. In the near future, however, heterogeneous non-x86 resources, such as ARM, POWER and Risc-V, will become a substantial fraction of the resources that will be provided to the LHC experiments, due to their presence in existing and planned world-class HPC installations. The CMS experiment, one of the four large detectors at the LHC, has started to prepare for this situation, with the CMS software stack (CMSSW) already compiled for multiple architectures. In order to allow for a production use, the tools for workload management and job distribution need to be extended to be able to exploit heterogeneous architectures. Profiting from the opportunity to exploit the first sizable IBM Power9 allocation available on Marconi100 HPC system at CINECA, CMS developed all the needed modifications to the CMS workload management system. After a successful proof of concept, a full physics validation has been performed in order to bring the system in production. The experiences are of very high value, when it comes to commissioning of the similar (even larger) Summit HPC system at Oak Ridge, where CMS is also expecting a resource allocation. Moreover the compute power of those systems is being provided also via GPUs and this represents an extremely valuable opportunity to exploit the offloading capability already implemented in CMSSW. The status of the current integration including the exploitation of the GPUs, the results of the validation as well as the future plans will be shown and discussed.

Boccali, Tommaso [INFN, Pisa]↗

DOE OSTI2017

Battery Testing FY 2017 Annual Progress Report

Abuse tests are designed to determine the safe operating limits of HEV\PHEV energy storage devices. Testing is intended to achieve certain worst-case scenarios to yield quantitative data on cell\module\pack response, allowing for failure mode determination and guiding developers toward improved materials and designs. Standard abuse tests with defined start and end conditions are performed on all devices to provide comparison between technologies. New tests and protocols are developed and evaluated to more closely simulate real-world failure conditions. When scaling from cell to the battery level, a detailed understanding of cell interactions provides insight on safety performance. Single point failures from a cell or group of cells can be initiated by a number of triggers including internal short circuit, misuse or abuse, or component failure at the battery or system level. Propagation of a single failure event (regardless of the initiation trigger) through an entire battery, system, or vehicle is an unacceptable outcome with regards to EV battery safety. In this FY, our work has focused on evaluating the propagation of a single cell thermal runaway event through a battery using a variety of design considerations with an emphasis on passive thermal management impacts. This has been coupled with thermal modeling by NREL for these testing conditions. In addition, alternative failure initiation methods have been evaluated to provide direct comparisons of possible energy injection between modes. This data was compiled to better identify what propagation test method is appropriate given certain battery designs. Expanding the analysis of short circuit current during failure propagation has been done for EV relevant chemistries. Ongoing test development and validation to obtain these values has been achieved. While robust mechanical models for vehicles and vehicle components exist, there is a gap for mechanical modeling of EV batteries. The challenge with developing a mechanical model for a battery is the heterogeneous nature of the materials and components (polymers, metals, metal oxides, liquids). Our work will provide empirical data on the mechanical behavior of batteries under compressive load to understand how a battery may behave in a vehicle crash scenario. This work is performed in collaboration with the U.S. Council for Automotive Research (USCAR) and Computer Aided Engineering of Batteries (CAEBAT). These programs have supported the design and development of a drop tower testing apparatus to close the gap between cell/string level testing and full scale crash testing with true dynamic rate effects.

25 ENERGY STORAGE↗

DOE OSTI2014

Enhanced HLW Glass Property-Composition Models – Phase 1 (Final Report)

In the present work, the previously compiled glass property-composition database for WTP HLW glasses has been updated with newly available data. The earlier evaluation of the baseline WTP models against the larger data sets, in terms of range of validity and prediction performance, has been used together with a review of the HLW glass data to identify gaps in the composition regions that need to be filled to support the development of expanded models. Subsequently, statistically-designed compositional matrices were developed to begin to fill these composition gaps. Crucible melts have been prepared for the matrix glasses and glass properties have been collected. Finally, preliminary models have been developed that are applicable to the expanded dataset. Additionally, the performance of the preliminary models have been reviewed to assess the relative advantages of developing one set of models for the entire HLW glass composition space or to developing separate models for discrete composition spaces. The longer-term objective of this multi-year task is to develop new glass property-composition models for PCT release, spinel crystallization (T 1% ), nepheline formation on CCC, TCLP Cd response, viscosity, and electrical conductivity to control glass compositions for HLW vitrification at Hanford.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Testing ΛCDM at the lowest redshifts with SN Ia and galaxy velocities

Peculiar velocities of objects in the nearby universe are correlated due to the gravitational pull of large-scale structure. By measuring these velocities, we have a unique opportunity to test the cosmological model at the lowest redshifts. We perform this test, using current data to constrain the amplitude of the "signal" covariance matrix describing the velocities and their correlations. We consider a new, well-calibrated "Supercal" set of low-redshift SNe Ia as well as a set of distances derived from the fundamental plane relation of 6dFGS galaxies. Here, analyzing the SN and galaxy data separately, both results are consistent with the peculiar velocity signal of our fiducial ΛCDM model, ruling out the noise-only model with zero peculiar velocities at greater than 7σ (SNe) and 8σ (galaxies). When the two data sets are combined appropriately, the precision of the test increases slightly, resulting in a constraint on the signal amplitude of A = 1.05 -0.21 +0.25 where A = 1 corresponds to our fiducial model. Equivalently, we report an 11% measurement of the product of the growth rate and amplitude of mass fluctuations evaluated at z eff = 0.02, f σ 8 = 0.428 -0.045 +0.048 , valid for our fiducial ΛCDM model. Here, we explore the robustness of the results to a number of conceivable variations in the analysis and find that individual variations shift the preferred signal amplitude by less than ~0.5σ. We briefly discuss our Supercal SN Ia results in comparison with our previous results using the JLA compilation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Data Collection Methods for Validation of Advanced Multi-Resolution Fast Reactor Simulations

In pool-type Sodium Fast Reactors (SFR) the regions most susceptible to thermal striping are the upper instrumentation structure (UIS) and the intermediate heat exchanger (IHX). This project experimentally and computationally (CFD) investigated the thermal mixing in the region exiting the reactor core to the UIS. The thermal mixing phenomenon was simulated using two vertical jets at different velocities and temperatures as prototypic of two adjacent channels out of the core. Thermal jet mixing of anticipated flows at different temperatures and velocities were investigated. Velocity profiles are measured throughout the flow region using Ultrasonic Doppler Velocimetry (UDV), and temperatures along the geometric centerline between the jets were recorded using a thermocouple array. CFD simulations, using COMSOL, were used to initially understand the flow, then to design the experimental apparatus and finally to compare simulation results and measurements characterizing the flows. The experimental results and CFD simulations show that the flow field is characterized into three regions with respective transitions, namely, convective mixing, (flow direction) transitional, and post-mixing. Both experiments and CFD simulations support this observation. For the anticipated SFR conditions the flow is momentum dominated and thus thermal mixing is limited due to the short flow length associated from the exit of the core to the bottom of the UIS. This means that there will be thermal striping at any surface where poorly mixed streams impinge; rather unless lateral mixing is ‘actively promoted out of the core, thermal striping will prevail. Furthermore we note that CFD can be considered a ‘separate effects (computational) test’ and is recommended as part of any integral analysis. To this effect, poorly mixed streams then have potential impact on the rest of the SFR design and scaling, especially placement of internal components, such as the IHX that may see poorly mixed streams. Finally, due to lack or infrastructural support for carrying out sodium experiments, only water experiments and CFD studies were realized in, an otherwise sodium approved facility.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

EMMA: an adaptive mesh refinement cosmological simulation code with radiative transfer

EMMA is a cosmological simulation code aimed at investigating the reionization epoch. It handles simultaneously collisionless and gas dynamics, as well as radiative transfer physics using a moment-based description with the M1 approximation. Field quantities are stored and computed on an adaptive three-dimensional mesh and the spatial resolution can be dynamically modified based on physically motivated criteria. Physical processes can be coupled at all spatial and temporal scales. We also introduce a new and optional approximation to handle radiation: the light is transported at the resolution of the non-refined grid and only once the dynamics has been fully updated, whereas thermo-chemical processes are still tracked on the refined elements. Such an approximation reduces the overheads induced by the treatment of radiation physics. A suite of standard tests are presented and passed by EMMA, providing a validation for its future use in studies of the reionization epoch. The code is parallel and is able to use graphics processing units (GPUs) to accelerate hydrodynamics and radiative transfer calculations. Depending on the optimizations and the compilers used to generate the CPU reference, global GPU acceleration factors between ×3.9 and ×16.9 can be obtained. As a result, vectorization and transfer operations currently prevent better GPU performance and we expect that future optimizations and hardware evolution will lead to greater accelerations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

ATR NEXSHARE Fact Sheet

This fact sheet will provide information for the database of experimental facilities supported by the IAEA as outlined below: NEXSHARE: As part of the Nuclear Harmonization and Standardization Initiative (NHSI), the IAEA proposes to establish a Network for global cooperation and resource sharing for experiments and code validation between experimental facilities, SMRs design organizations, International Organizations and Technical Support Organizations (TSOs). The proposed Network, NEXSHARE, will be done in collaboration with the OECD/NEA. EXPERIMENTAL FACILITY DATABASE: As part of this activity, the IAEA is compiling a database of experimental facilities applicable to SMRs (including water cooled, high temperature gas cooled, molten salt and fast neutron spectrum reactors). This database will also form part of NEXSHARE and will be documented in an IAEA publication.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data

Understanding rare‐earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional‐materials fabrication—as a surrogate for studying mineralization with data‐driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine‐learning models—K‐nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB‐based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy‐REE systems. Overall, the study shows that functional‐materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less‐studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.

feature importance analysis↗

DOE OSTI2014

Performance and Architecture Lab Modeling Tool

Analytical application performance models are critical for diagnosing performance-limiting resources, optimizing systems, and designing machines. Creating models, however, is difficult. Furthermore, models are frequently expressed in forms that are hard to distribute and validate. The Performance and Architecture Lab Modeling tool, or Palm, is a modeling tool designed to make application modeling easier. Palm provides a source code modeling annotation language. Not only does the modeling language divide the modeling task into sub problems, it formally links an application's source code with its model. This link is important because a model's purpose is to capture application behavior. Furthermore, this link makes it possible to define rules for generating models according to source code organization. Palm generates hierarchical models according to well-defined rules. Given an application, a set of annotations, and a representative execution environment, Palm will generate the same model. A generated model is a an executable program whose constituent parts directly correspond to the modeled application. Palm generates models by combining top-down (human-provided) semantic insight with bottom-up static and dynamic analysis. A model's hierarchy is defined by static and dynamic source code structure. Because Palm coordinates models and source code, Palm's models are 'first-class' and reproducible. Palm automates common modeling tasks. For instance, Palm incorporates measurements to focus attention, represent constant behavior, and validate models. Palm's workflow is as follows. The workflow's input is source code annotated with Palm modeling annotations. The most important annotation models an instance of a block of code. Given annotated source code, the Palm Compiler produces executables and the Palm Monitor collects a representative performance profile. The Palm Generator synthesizes a model based on the static and dynamic mapping of annotations to program behavior. The model -- an executable program -- is a hierarchical composition of annotation functions, synthesized functions, statistics for runtime values, and performance measurements.

Tallent, Nathan↗

DOE OSTI2017

Current Efforts in Immobilized Low-Activity Waste Glass Testing to Support the Hanford Site Integrated Disposal Facility - 17119

Cleanup plans for Hanford site high-level radioactive mixed tank waste calls for separating the waste into high-level waste (HLW) and low-activity waste (LAW) fractions. The fractions will be vitrified separately at the Hanford Tank Waste Treatment and Immobilization Plant (WTP). The HLW glass will be sent to an offsite geological disposal facility while the immobilized low-activity waste (ILAW) glass will be disposed at the onsite Integrated Disposal Facility (IDF). In order to inform models used to predict the performance of ILAW glass at IDF, it is necessary to understand the corrosion behavior of the glass. In this paper, we discuss tests that account for the effects of 1) solution chemistry, 2) the ion exchange reaction, and 3) secondary phase formation on the glass dissolution rate. To quantify the effects of solution chemistry, specifically changes in pH, T, and Si concentration, the single-pass flow-through (SPFT) test method is applied. A designed set of these tests results in a set of kinetic rate law parameters that are used in a glass dissolution rate law based on transition-state theory. Furthermore, for SPFT tests with relatively high concentrations of Si, an ion exchange rate is provided. Additionally, through tests such as the pressurized unsaturated flow (PUF) test method, a suite of secondary phases can be compiled. The collection of kinetic rate law parameters, the ion exchange term, and the suite of secondary phases are provided to IDF performance assessment (PA) modelers. Lastly, we present a description of the efforts made to verify and validate the use of these parameters and secondary phases through the use of a field lysimeter present at the Hanford site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗