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This presentation covers recent developments in the R-Matrix Code SAMMY. The presentation starts with a discussion around SAMMY releases. Secondly it provides updates in the program flow. The presentation concludes with goals and achievements in the modernization efforts.
Thermochemical energy storage (TCES) for concentrating solar power (CSP) systems provides higher energy density than sensible energy storage systems. An ammonia-based TCES system dissociates endothermically into hydrogen and nitrogen. The stored energy is released when supercritical hydrogen and nitrogen react exothermically to synthesize ammonia. Prior ammonia synthesis systems are unable to produce temperatures consistent with modern power blocks requiring a working fluid, for example steam or carbon dioxide, to be heated to greater than 600° C., for example about 650° C. An ammonia synthesis system heats steam from, for example 350° C. to 650° C. under pressure of about 26 MPa. The hydrogen and nitrogen are preheated with a flow of supercritical fluid prior to the synthesis step to provide reaction rates sufficient to heat power block working fluid to the desired temperature.
Within the current United States (U.S.) nuclear power fleet, main control room modernization (CRM) is an important step towards cost savings. In recent decades, plants have been engaged in upgrades to varying degrees. This process requires a nuanced, balanced, and timely approach that ensures continued safety and long-term sustainability. In 2012 a survey was issued to individuals from the nuclear industry to learn their perspectives on a range of CRM issues. The survey targeted the benefits and challenges for utilities undertaking this process, including the main drivers and barriers to technology upgrades, regulatory compliance, and the effects these factors have on concepts of operations, strategic approaches, and staffing. In 2022, the survey was issued again to understand whether CRM perceptions had changed in the last 10 years. Our findings identify changes in industry thinking from a decade ago. Here we reveal perspective shifts that represent increased optimism and, in some instances, increased doubt regarding the opportunities and challenges inherent in CRM and implementation. We also report nuanced differences in CRM perspectives between utility and surrounding nuclear industry respondents.
Per- and polyfluoroalkyl substances (PFAS) are a broad class of synthetic chemicals used in a variety of modern technologies and consumer products. PFAS have an alkyl backbone with all or most of the hydrogen (H) atoms replaced with fluorine (F) atoms. PFAS generally fall into one of two categories, they can be non-polymeric with relatively low molecular weights, or long-chain polymers. PFAS are a diverse group of organic compounds that can be solids, liquids, dispersions, or gases. Their mobility and toxicity are influenced by their chain lengths and functional groups. Non-polymeric PFAS are common building blocks for the manufacturing of fluoropolymers. Non-polymeric PFAS are known to be mobile in the environment and some are considered contaminants of emerging concern. Polymeric PFAS, or fluoropolymers, are typically thought to be of low concern, however few systematic investigations into their toxicity have been completed. PFAS, including fluoropolymers and microplastics, have been found in pristine environments and animals located far from site of origin including the arctic and deep ocean.
Abstract The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors, including the retardation of the interaction and the Coulomb pseudopotential, to predict the critical temperature T c . McMillan, Allen, and Dynes derived approximate closed-form expressions for the critical temperature within this theory, which depends on the electron–phonon spectral function α 2 F ( ω ). Here we show that modern machine-learning techniques can substantially improve these formulae, accounting for more general shapes of the α 2 F function. Using symbolic regression and the SISSO framework, together with a database of artificially generated α 2 F functions and numerical solutions of the Eliashberg equations, we derive a formula for T c that performs as well as Allen–Dynes for low- T c superconductors and substantially better for higher- T c ones. This corrects the systematic underestimation of T c while reproducing the physical constraints originally outlined by Allen and Dynes. This equation should replace the Allen–Dynes formula for the prediction of higher-temperature superconductors.
Isotopes in the region of the nuclear chart below 68 Ni have been the subject of intense experimental and theoretical effort due to the potential onset of a new “island of inversion” when crossing the harmonic oscillator subshell closure at 𝑁=40. Here, we have measured the masses of 64−68 Mn using TITAN's multiple-reflection time-of-flight mass spectrometer, resulting in the first precision mass measurements of 67 Mn and 68 Mn. These results are compared to ab initio calculations and modern shell model calculations and show an increase in collectivity approaching 𝑁=40.
We present hipBone, an open-source performance-portable proxy application for the Nek5000 (and NekRS) computational fluid dynamics applications. HipBone is a fully GPU-accelerated C++ implementation of the original NekBone CPU proxy application with several novel algorithmic and implementation improvements which optimize its performance on modern fine-grain parallel GPU accelerators. Our optimizations include a conversion to store the degrees of freedom of the problem in assembled form in order to reduce the amount of data moved during the main iteration and a portable implementation of the main Poisson operator kernel. We demonstrate near-roofline performance of the operator kernel on three different modern GPU accelerators from two different vendors. We present a novel algorithm for splitting the application of the Poisson operator on GPUs which aggressively hides MPI communication required for both halo exchange and assembly. Our implementation of nearest-neighbor MPI communication then leverages several different routing algorithms and GPU-Direct RDMA capabilities, when available, which improves scalability of the benchmark. We demonstrate the performance of hipBone on three different clusters housed at Oak Ridge National Laboratory, namely, the Summit supercomputer and the Frontier early-access clusters, Spock and Crusher. Our tests demonstrate both portability across different clusters and very good scaling efficiency, especially on large problems.
The primary goal of this research was to develop an innovative approach to provide a significantly improved level of thermal protection for hot-section components, such as turbine airfoils, in modern and future gas turbines. Current and future designs for “air breathing” turbine systems are targeting turbine inlet temperatures of 1700°C or higher, which is well beyond the substrate melting temperature, and a combined cycle energy efficiency of 65%. Meanwhile, targeted cycle efficiency calls for a reduction of the amount of coolant air significantly below the current level. Hence the development of novel internal airfoil designs that provide a satisfactory level of component durability with affordable coolant management is critical to the further advances of today’s turbine technology. This project was a response to this grand challenge with the research efforts primarily focused on the development of integrating transpiration cooling with lattice enhanced internal cooling. The research on these advanced cooling technologies was accompanied with development of anti-oxidation material in the form of oxide dispersion strengthened (ODS) alloy which also possessed enhanced high temperature strength. A novel approach of fabricating the ODS alloy as powder with high compatibility with state-of-the-art additive manufacturing (AM) process was also developed.
The R-Matrix code SAMMY is a widely used nuclear data evaluation code focused on the resolved range, which includes corrections for experimental effects. The code is still mostly written in Fortran 77, and uses a memory management system suitable for the time of its initial writing (1984). A modernization effort is under way to bring the code in-line with modern software development practices. A continuous-integration testing framework was added, automating the large existing set of test cases. It is run on every commit. The memory management was updated to current standard practices suitable for modern software analysis tools. The code can be obtained from https://code.ornl.gov/RNSD/SAMMY. The resonance parameters and covariance information are now stored in C++ objects shared by SAMMY and AMPX, the processing code that generates nuclear data libraries for SCALE. This allows for easier maintenance and access to the resonance parameters inside and outside of SAMMY. This feature is already used by accessing and changing parameters in memory in the Bayesian Monte Carlo Evaluation Framework for Cross Sections Nuclear Data and Integral Benchmark Experiments project, Further plans include the switch to the ENDF reading and writing routines in AMPX, as these routines are more robust, easier to maintain, and support more features. Of note here is support for the new GNDS format. Previously it wasn’t easy to share the full covariance matrix for evaluations containing more than one isotope due to limitations on the ENDF format; this is now supported in GNDS. The data are currently available in a binary SAMMY format and can be exported to GNDS to make them more widely available and sharable. The next step will be to use the same resonance processing code at 0K in AMPX and SAMMY as one of the available Reich-Moore R-Matrix formalism. The first step toward this goal is to isolate the reconstruction into a module that takes resonance parameters as its input and does not depend on SAMMY global parameters. This goal has been achieved and it should now be possible to more easily change the resonance formalism and add enhancements as the Phenomenological R-Matrix parameterization of direct, doorway, and compound nuclear reactions discussed elsewhere on this conference. This concerted modernization and enhancement effort provides multiple advantages to the nuclear data community. It will allow parameter optimization using enhanced formalisms, including experimental effects, that better match complex experimental data. Then those evaluated parameters can immediately be passed off to AMPX to be reconstructed with the exact same cross section model and be put into a data library for subsequent testing using SCALE and the Valid Benchmark suite or other suitable benchmark suites.
This report provides an end-of-year summary that reflects the progress and status of Idaho National Laboratory’s (INL) activities concerning the development of advanced reactor (AR) regulatory framework and its implementation in the United States (U.S.). The report also provides recommendations for work to be performed in Fiscal Year 2025 (FY-25) and beyond. This work was completed in Fiscal Year 2024 (FY-24) and was supported by the U.S. Department of Energy (DOE) Regulatory Development sub-program. These activities are managed by INL on behalf of DOE.
Chemical looping is a promising approach for improving the energy efficiency of many industrial chemical processes. However, a major limitation of modern chemical looping technologies is the lack of suitable active materials to mediate the involved subreactions. Identification of suitable materials has been historically limited by the scarcity of high-temperature (>600 °C) thermochemical data to evaluate candidate materials. An accuratethermodynamic approach is demonstrated here to rapidly identify active materials which is applicable to a wide variety of chemical looping chemistries. Application of this analysis to chemical looping combustion correctly classifies 17/17 experimentally studied redox materials by their viability and identifies over 1300 promising yet previously unstudied active materials. This approach is further demonstrated by analyzing redox pairs for mediating a novel chemical looping process for producing pure SO2 from raw sulfur and air which could provide a more efficient and lower emission route to sulfuric acid. 12 promising redox materials for this process are identified, two of which are supported by previous experimental studies of their individual oxidation and reduction reactions. This approach provides the necessary foundation for connecting process design with high-throughput material discovery to accelerate the innovation and development of a wide range of chemical looping technologies.
Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.
The iron and steel industry is critical for the modern world but is also very energy intensive and is a low margin industry. Hot rolling requires reheating steel slabs to ~1250 °C for hot rolling and involves yield loss due to scale formation. Electrification of this process using resistive heating could improve yield but is currently not possible due to the poor creep resistance of MoSi2 heating elements, which need to be operated well below their maximum temperatures when mounted horizontally. To solve this problem, novel MoSi2 composite heating elements with SiBCN ceramic reinforcements are being developed using high energy ball milling and spark plasma sintering. The creep performance of different heating elements and their corresponding microstructures and electrical properties will be presented.
LiNiO 2 (LNO) is a high-capacity and model cathode first discovered in the 1980s that fell out of favor due to its intrinsic instabilities. However, research activities toward LNO are once again on the rise as the push for higher-energy-density cells marches on. Here we demonstrate here that with appropriate modern electrolytes, major performance improvements can be achieved with LNO with no additional modifications. Cells with a localized high concentration electrolyte (LHCE) deliver 92% capacity retention after 200 cycles compared with 56% capacity retention in a baseline carbonate electrolyte, maintain 94% capacity after high-voltage storage compared with 77% capacity, and display a higher onset temperature of thermal runaway of 244 °C compared with 188 °C. These improvements are attributed to the LHCE’s high oxidative stability and its formation of fluorine-rich interphases. Although further characterization of this new class of electrolyte is necessary, this work demonstrates that modern electrolytes can be drop-in enablers of high-capacity, long-cycle-life cells.
Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. Furthermore, this study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.
This report describes the extended capabilities of the NEML2 constitutive modeling library, including a flexible and efficient work dispatching system designed to leverage both CPU and GPU resources. This enhancement addresses one of the primary computational challenges in large-scale simulations: the ability to distribute and execute batches of material model evaluations across heterogeneous computing devices. The new dispatch system introduces a modular set of dispatcher and scheduler classes that coordinate the flow of data and execution between devices. The dispatcher is responsible for efficiently packaging work, managing device-specific memory operations, and synchronizing results. This modularity allows for extensibility, making it straightforward to integrate additional computing backends in the future. From an implementation standpoint, the dispatcher system interfaces seamlessly with NEML2's existing models. They handle device-aware tensor operations, optimize memory transfers, and support asynchronous execution when applicable. This design ensures that batches of material points can be evaluated concurrently, substantially improving throughput compared to previous single-device or serial implementations. These improvements not only enhance the raw performance of NEML2 but also improve its usability in multiscale and high-fidelity simulations, where the simultaneous evaluation of large material point batches is critical. Benchmarks included in the report demonstrate the system’s scalability, highlighting its effectiveness when leveraging modern GPU architectures.
The behavior of the Greenland Ice Sheet during the Pleistocene remains uncertain due to the paucity of evidence predating the Last Glacial Maximum. In this study, we employ a novel approach, cosmogenic nuclide analysis of individual subglacially-derived cobbles, which allows us to make inferences about ice sheet processes and subglacial erosion. From three locations in western Greenland, we collected 86 cobbles from the current ice sheet margin and nine cobbles exposed on the modern proglacial land surface. We measured the concentration of in situ 10 Be in all cobbles (n = 95) and 26 Al and 14 C in a subset (n = 14). Cobbles deposited during Holocene retreat have 10 Be exposure ages generally consistent with the timing of ice retreat determined by other methods. Conversely, most of the 86 subglacial cobbles contain very low concentrations of 10 Be (median 1.0×10 3 atoms g –1 ), although several have ~10 4 and one has ~10 5 atoms g –1 . The low concentrations of 10 Be in most subglacial cobbles imply that their source areas under the Greenland Ice Sheet are deeply eroded, preserving minimal evidence of surface or near-surface exposure. The presence of measurable 14 C in ten of the cobbles requires that they experienced cosmogenic nuclide production within the past ~30 ka; however, 14 C/ 10 Be ratios of ~6 suggest that nuclide production occurred during shielding by overlying material. Only two of the 86 subglacial cobbles definitively have cosmogenic nuclide concentrations consistent with prior surface exposure. Overall, isotopic analysis of subglacial cobbles indicates that much of western Greenland's subglacial landscape is characterized by deep erosion and minimal subaerial exposure.
The Advanced Gas Reactor Fuel Qualification and Development (AGR) program has focused on qualification of UCO kernel tristructural-isotropic (TRISO) particle fuel relative to UO₂ kernel TRISO particle fuel. However, a UO₂ kernel variant was included in the second AGR irradiation experiment (AGR-2) for comparison and to connect to historic fuel irradiation data. The development of a multiscale, post-irradiation examination (PIE) analysis approach through the AGR Program has allowed for a comprehensive understanding of individual particle failure. This approach has been applied to gain an understanding of SiC layer failure in UO₂ kernel TRISO fuel from AGR-2 after safety testing at 1600–1700 °C. Particle failure by intergranular CO corrosion, facilitated by a compromised inner pyrolytic carbon layer, has been confirmed through the combined application of modern x-ray computed tomography and electron microscopy techniques. In addition, a relationship between grain boundary character and CO corrosion has been identified. This finding provides an opportunity to develop mitigating strategies to improve the resilience of the SiC layer to CO corrosion in UO₂ TRISO fuel.