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OUTLINE • Six Fuel Cycle Topics • SRNL Process Modeling • Tritium Accountancy • Real-Time Monitoring Framework Proof-of-Concept Study • Development of the Accountancy Framework • Sensor Assessment In Relation to the DT Fuel Cycle • Summary
This article presents an overview of select advances in AREVA NP methods concerning 3 topics: Analytical fuel rod model, Transient and accident simulation, and ATWSi post dryout treatment. Concerning the first topic, the analytical fuel rod model relies heavily on first-principles closed-form solutions instead of ever finer nodalization and higher dimensions of standard numerical methods such as finite differences or finite elements. This analytical fuel rod model takes full account of the complications that made numerical analysis necessary, e.g. variation of pellet thermal conductivity with temperature and burnup, and the radial dependence of fission power deposition in pellets. The model also calculates thermo-mechanical parameters such as clad stress and strain, fission gas release, pellet sintering and swelling, and pellet-clad gap size and thermal resistance. The model is suited for innovative fuel designs such as doped pellets. Concerning the second topic, the BWR evaluation model, AURORA-B, is an advanced transient and accident simulator including SRELAP5, a two fluid thermal hydraulic code comprehensively reviewed by international safety authorities for BWR and PWR applications; RODEX4, a best estimate fuel performance code used to evaluate the local thermal-mechanical behavior of fuel rods during postulated transients and accidents; and MB2-K, a neutron kinetics extension of the steady state core simulator MICROBURN-B2. The analytical domains will include transients as well as control rod drop and loss of coolant accidents. Extension of RODEX4 have been made to accommodate analysis of chromium-doped fuel. Concerning the third issue,The new AREVA post-dryout models can accurately predict dryout/re-wet followed by an ultimate failure to re-wet under oscillatory ATWSi (Anticipated Transient Without SCRAM with core instability) conditions. These models, while not a boiling curve based method, can qualitatively reproduce the characteristics of the boiling curve.
The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.
The work is summarized from several perspectives: 1D simulation perspective: Post-shot models agree with yield data to within a factor of ~2 at low implosion velocities, but the models diverge from the data as the velocity and convergence ratio increase. 2D simulation perspective: Integrated hohlraum-capsule post-shot models agree with primary data for most implosions, but overpredict yield and DSR for a few of the highest velocity implosions. High-resolution 3D post-shot capsule-only modeling captures much of the delivered performance of the one shot currently simulated.
Abstract not provided.
Equity is core to sustainability, but current interventions to enhance sustainability often fall short in adequately addressing this linkage. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. This Perspective highlights progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions. We present a conceptual framework for equity in systems modeling, focused on its distributional, procedural, and recognitional dimensions. We discuss examples of how modelers engage with these different dimensions throughout the modeling process and from across a range of modeling approaches and topics, including water resources, energy systems, air quality, and conservation. Synthesizing across these examples, we identify significant advances in enhancing procedural and recognitional equity by reframing models as tools to explore pluralism in worldviews and knowledge systems; enabling models to better represent distributional inequity through new computational techniques and data sources; investigating the dynamics that can drive inequities by linking different modeling approaches; and developing more nuanced metrics for assessing equity outcomes. We also identify important future directions, such as an increased focus on using models to identify pathways to transform underlying conditions that lead to inequities and move toward desired futures. By looking at examples across the diverse fields within sustainability science, we argue that there are valuable opportunities for mutual learning on how to use models more effectively as tools to support sustainable and equitable futures.
The Jules Horowitz Reactor (JHR) is a Material-Testing Reactor (MTR) under construction in the south of France at CEA Cadarache (French Alternative Energies and Atomic Energy Commission). It will typically host about 20 simultaneous irradiation experiments in the core and in the beryllium reflector. These experiments will help us better understand the complex phenomena occurring during the accelerated ageing of materials and the irradiation of nuclear fuels. Gamma heating, i.e. photon energy deposition, is mainly responsible for temperature rise in non-fuelled zones of nuclear reactors, including JHR internal structures and irradiation devices. As temperature is a key parameter for physical models describing the behavior of material, accurate control of temperature, and hence gamma heating, is required in irradiation devices and samples in order to perform an advanced suitable analysis of future experimental results. From a broader point of view, JHR global attractiveness as a MTR depends on its ability to monitor experimental parameters with high accuracy, including gamma heating. Strict control of temperature levels is also necessary in terms of safety. As JHR structures are warmed up by gamma heating, they must be appropriately cooled down to prevent creep deformation or melting. Cooling-power sizing is based on calculated levels of gamma heating in the JHR. Due to these safety concerns, accurate calculation of gamma heating with well-controlled bias and associated uncertainty as low as possible is all the more important. There are two main kinds of calculation bias: bias coming from nuclear data on the one hand and bias coming from physical approximations assumed by computer codes and by general calculation route on the other hand. The former must be determined by comparison between calculation and experimental data; the latter by calculation comparisons between codes and between methodologies. In this presentation, we focus on this latter kind of bias. Nuclear heating is represented by the physical quantity called absorbed dose (energy deposition induced by particle-matter interactions, divided by mass). Its calculation with Monte Carlo codes is possible but computationally expensive as it requires transport simulation of charged particles, along with neutrons and photons. For that reason, the calculation of another physical quantity, called KERMA, is often preferred, as KERMA calculation with Monte Carlo codes only requires transport of neutral particles. However, KERMA is only an estimator of the absorbed dose and many conditions must be fulfilled for KERMA to be equal to absorbed dose, including so-called condition of electronic equilibrium. Also, Monte Carlo computations of absorbed dose still present some physical approximations, even though there is only a limited number of them. Some of these approximations are linked to the way how Monte Carlo codes apprehend the transport simulation of charged particles and the productive and destructive interactions between photons, electrons and positrons. There exists a huge variety of electromagnetic shower models which tackle this topic. Differences in the implementation of these models can lead to discrepancies in calculated values of absorbed dose between different Monte Carlo codes. The magnitude of order of such potential discrepancies should be quantified for JHR gamma-heating calculations. We consequently present a two-pronged plan. In a first phase, we intend to perform compared absorbed dose / KERMA Monte Carlo calculations in the JHR. This way, we will study the presence or absence of electronic equilibrium in the different JHR structures and experimental devices and we will give recommendations for the choice of KERMA or absorbed dose when calculating gamma heating in the JHR. In a second phase, we intend to perform compared TRIPOLI4 / MCNP absorbed dose calculations in a simplified JHR-representative geometry. For this comparison, we will use the same nuclear data library for both codes (the European library JEFF3.1.1 and photon library EPDL97) so as to isolate the effects from electromagnetic shower models on absorbed dose calculation. This way, we hope to get insightful feedback on these models and their implementation in Monte Carlo codes. (authors)
A survey of some results in the modern microscopic theory of properties of nuclear reactions with gamma rays is given. First of all, we discuss the impact of Phonon Coupling (PC) on the Photon Strength Function (PSF) because it represents the most natural physical source of additional strength found for Sn isotopes in recent experiments that could not be explained within the standard HFB + QRPA approach. The self-consistent version of the Extended Theory of Finite Fermi Systems in the Quasiparticle Time Blocking Approximation is applied. It uses the HFB mean field and includes both the QRPA and PC effects on the basis of the SLy4 Skyrme force. With our microscopic E1 PSFs, the following properties have been calculated for many stable and unstable even–even semi-magic Sn and Ni isotopes as well as for double-magic {sup 132}Sn and {sup 208}Pb using the reaction codes EMPIRE and TALYS with several Nuclear Level Density (NLD) models: (1) the neutron capture cross sections; (2) the corresponding neutron capture gamma spectra; (3) the average radiative widths of neutron resonances. In all the properties considered, the PC contribution turned out to be significant, as compared with the standard QRPA one, and necessary to explain the available experimental data. The results with the phenomenological so-called generalized superfluid NLD model turned out to be worse, on the whole, than those obtained with the microscopic HFB + combinatorial NLD model. The very topical question about the M1 resonance contribution to PSFs is also discussed.Finally, we also discuss the modern microscopic NLD models based on the self-consistent HFB method and show their relevance to explain the experimental data as compared with the phenomenological models. The use of these self-consistent microscopic approaches is of particular relevance for nuclear astrophysics, but also for the study of double-magic nuclei.
The Multi-Source Signatures for Nuclear Programs project, part of Pacific Northwest National Laboratory’s (PNNL) Signature Discovery Initiative, seeks to computationally capture expert assessment of multi-type information such as text, sensor output, imagery, or audio/video files, to assess nuclear activities through a series of Bayesian network (BN) models. These models incorporate knowledge from a diverse range of information sources in order to help assess a country’s nuclear activities. The models span engineering topic areas, state-level indicators, and facility-specific characteristics. To illustrate the development, calibration, and use of BN models for multi-source assessment, we present a model that predicts a country’s likelihood to participate in the international nuclear nonproliferation regime. We validate this model by examining the extent to which the model assists non-experts arrive at conclusions similar to those provided by nuclear proliferation experts. We also describe the PNNL-developed software used throughout the lifecycle of the Bayesian network model development.
TMEM16F is a Ca 2+ -activated phospholipid scramblase in the TMEM16 family of membrane proteins. Unlike other TMEM16s exhibiting a membrane-exposed hydrophilic groove that serves as a translocation pathway for lipids, the experimentally determined structures of TMEM16F shows the groove in a closed conformation even under conditions of maximal scramblase activity. It is currently unknown if/how TMEM16F groove can open for lipid scrambling. Here we describe the analysis of ~400 µs all-atom molecular dynamics (MD) simulations of the TMEM16F revealing an allosteric mechanism leading to an open-groove, lipid scrambling competent state of the protein. The groove opens into a continuous hydrophilic conduit that is highly similar in structure to that seen in other activated scramblases. The allosteric pathway connects this opening to an observed destabilization of the Ca 2+ ion bound at the distal site near the dimer interface, to the dynamics of specific protein regions that produces the open-groove state to scramble phospholipids.
Nuclear reactors are uniquely powerful, abundant, and flavor-pure sources of antineutrinos that continue to play a vital role in the US neutrino physics program. The US reactor antineutrino physics community is a diverse interest group encompassing many detection technologies and many particle physics topics, including Standard Model and short-baseline oscillations, BSM physics searches, and reactor flux and spectrum modeling. The community's aims offer strong complimentary with numerous aspects of the wider US neutrino program and have direct relevance to most of the topical sub-groups composing the Snowmass 2021 Neutrino Frontier. Reactor neutrino experiments also have a direct societal impact and have become a strong workforce and technology development pipeline for DOE National Laboratories and universities. This white paper, prepared as a submission to the Snowmass 2021 community organizing exercise, will survey the state of the reactor antineutrino physics field and summarize the ways in which current and future reactor antineutrino experiments can play a critical role in advancing the field of particle physics in the next decade.
LBNL researchers developed a template and companion guide for structuring distribution system plans. The template covers 10 key sections in utility distribution system plans and underlying topics: -Executive Summary -Planning Objectives -Current Distribution System -Planning Approach -Asst Management, Reliability and Resilience -O&M Expenses -Capacity Expansion Planning -Solution Identification -Cost-effectiveness -Implementation For each section in the template, the companion guide provides: a description of the topics covered and their importance; the type of content to include in the plan; an example utility approach; and references to other utility plans that serve as models for the topic.
A comprehensive way of deriving the Hamiltonian of the interacting boson model (IBM) is described. Based on the fact that the multi-nucleon induced surface deformation in finite nucleus is simulated by effective boson degrees of freedom, the potential energy surface calculated with self-consistent mean-field method employing a given energy density functional (EDF) is mapped onto the IBM analog, and thereby the excitation spectra and transition rates with good symmetry quantum numbers are calculated. Recent applications of the proposed approach are reported: (i) an alternative robust interpretation of the {gamma}-soft nuclei and (ii) shape coexistence in lead isotopes.
Massive digital disinformation is one of the main risks of modern society. Hundreds of models and linguistic analyses have been done to compare and contrast misleading and credible content online. However, most models do not remove the confounding factor of a topic or narrative when training, so the resulting models learn a clear topical separation for misleading versus credible content. We study the feasibility of using two strategies to disentangle the topic bias from the models to understand and explicitly measure linguistic and stylistic properties of content from misleading versus credible content. First, we develop conditional generative models to create news content that is characteristic of different credibility levels. We perform multi-dimensional evaluation of model performance on mimicking both the style and linguistic differences that distinguish news of different credibility using machine translation metrics and classification models. We show that even though generative models are able to imitate both the style and language of the original content, additional conditioning on both the news category and the topic leads to reduced performance. In a second approach, we perform deception style ``transfer" by translating deceptive content into the style of credible content and vice versa. Extending earlier studies, we demonstrate that, when conditioned on a topic, deceptive content is shorter, less readable, more biased, and more subjective than credible content, and transferring the style from deceptive to credible content is more challenging than the opposite direction.
Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery.
Abstract Slip flows in ducts are important in numerous engineering applications, most notably in microchannel flows. Compared to the standard no‐slip Dirichlet condition, the case of slip formulates as a Robin‐type condition for the fluid tangential velocity. Such an increase in mathematical complexity is accompanied by a more challenging numerical transcription. The present work concerns with this topic, addressing the modeling of the slip velocity boundary condition in the lattice Boltzmann method (LBM) applied to steady slow viscous flows inside ducts of nontrivial shapes. As novelty, we extend the newly revised local second‐order boundary (LSOB) Dirichlet fluid flow method [ Philos. Trans. R. Soc. A 378, 20190404 (2020)] to implement the slip velocity condition within the two‐relaxation‐time (TRT) framework. The LSOB follows an in‐node philosophy where its operation principle seeks to explicitly reconstruct the unknown boundary populations in the form of a third‐order accurate Chapman–Enskog expansion, where the wall slip condition is built‐in as a normal Taylor‐type condition. The key point of this approach is that the required first‐ and second‐order momentum derivatives, rather than computed through nonlocal finite difference approximations, are locally determined through a simple local linear algebra procedure, whose formulation is particularly aided by the TRT symmetry argument. To express the obtained derivatives, two approaches are considered, called and , which operate with node and wall variables, respectively. These two formulations are developed to prescribe the physical slip condition over plane and curved walls, including the corners. Their consistency and accuracy characteristics are examined against alternative linkwise strategies to impose the wall slip velocity, such as the kinetic‐based diffusive bounce‐back scheme, the central linear interpolation slip scheme, and the multireflection slip scheme. The several slip schemes are tested over different 3D microchannel configurations, with walls not conforming with the LBM uniform mesh. Numerical tests confirm the advanced accuracy characteristics of the proposed LSOB slip boundary scheme, revealing the added challenge of the wall slip modeling, and that parabolic accuracy is a necessary requirement to reach second‐order accuracy within this problem class.
These are slides on Generative Modeling for Machine Learning on the D-Wave. The following topics are detailed: generative models; Boltzmann machines: a generative model; restricted Boltzmann machines; learning parameters: RBM training; practical ways to train RBM; D-Wave as a Boltzmann sampler; mapping RBM onto the D-Wave; Chimera restricted RBM; mapping binary RBM to Ising model; experiments; data; D-Wave effective temperature, parameters noise, etc.; experiments: contrastive divergence (CD) 1 step; after 50 steps of CD; after 100 steps of CD; D-Wave (experiments 1, 2, 3); D-Wave observations.
The marine and hydrokinetic (MHK) industry is at an early stage of development and has the potential to play a significant role in diversifying the U.S. energy portfolio and reducing the U.S. carbon footprint. Wave energy is the largest among all the U.S. MHK energy resources, which include wave energy, ocean current, tidal-instream, ocean thermal energy conversion, and river-instream. Wave resource characterization is an essential step for regional wave energy assessments, Wave Energy Converter (WEC) project development, site selection and WEC design. The present paper provides an overview of a joint modelling effort by the Pacific Northwest National Laboratory and Sandia National Laboratories on high-resolution wave hindcasts to support the U.S. Department of Energy’s Water Power Technologies Office’s program of wave resource characterization, assessment and classifications in all US coastal regions. Topics covered include the modelling approach, model input requirements, model validation strategies, high performance computing resource requirements, model outputs and data management strategies. Examples of model setup and validation for different regions are provided along with application to development of classification systems, and analysis of regional wave climates. Lessons learned and technical challenges of the long-term, high-resolution regional wave hindcast are discussed.