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Shear Enhanced Eccentric Roiling (ShEER) is a novel sheet manufacturing technique that aims to superimpose and control hydrostatic and deviatoric stress paths during rolling. In this research, we attempt to use ShEER to form and enhance the properties of low-ductility highly anisotropic materials such as magnesium.
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A set of novel fuel concepts has been proposed for use in advanced low-enriched uranium (LEU) systems that utilize powder metallurgical methods for fabrication of the fuel forms. Preliminary tests demonstrated the ability to produce powder but did not yield the desired quality to be used as feedstock in these applications. This study seeks to establish an improved powder production method for uranium-based alloys and evaluate the parameters required to refine the particles produced by size and morphology. Powder samples were fabricated via atomization and analyzed for particle morphology utilizing sieving methods and scanning electron microscopy (SEM). Surrogate testing displayed improved performance in producing small (<250 µm) spherical particles, and initial tests demonstrated the initial capability to atomize uranium. Further work is required to refine the atomization process to produce high quality uranium microspheres.
Environmentally acceptable lubricants (EALs) are defined as lubricants that meet U.S. Environmental Protection Agency (EPA) standards for “biodegradability, toxicity, and bioaccumulation potential that minimize their likely adverse consequences in the aquatic environment, compared to conventional lubricants.” Restrictions from state, local, and federal agencies, as well as a growing awareness of the environmental risks associated with petroleumbased lubricants, have shifted government and industry to seek out high-performing EALs. The U.S. Department of Energy (DOE) is working to reduce the environmental impacts of hydropower development and operation by investing in tools and methods to develop, demonstrate, and validate EAL materials.
Critical infrastructure stakeholders need to baseline their systems to understand expected protocol communications.Baseline behaviors may vary based on operational context.Expected operations during a maintenance window, for example, may be different from normal operations.Furthermore, constructing system baselines for Industrial Control Systems (ICS) is difficult and time-consuming.ICS processes generate artifacts expressed across heterogeneous data sources such as network and device logs. There needs to be a corpus of data in order to develop and compare methods that evaluate the feasibility, performance, and generality of approaches to construct baselines for ICS events. Standalone repositories of network packet captures are insufficient to develop methods to classify or recognize operational events expressed across multiple data sources. Moreover, static data corpora do not enable researchers to compare the impact of changing the underlying system for which a baseline is being constructed and this limits the ability to evaluate the performance of system baselines given system changes (e.g. patches, configuration, maintenance events). In order to address these limitations within the community, this talk intends to promote discussion about the state of the practice of constructing baselines. In this manner, we can continue to understand requirements within industry that are not being met by current approaches to baseline construction. This talk builds on two previous talks on the topic of system baselines for OT environments. First, Weaver co-presented at the RSA Conference ICS Sandbox with Dan Gunter. The talk confirmed the need within industry to construct baselines across multiple types of data sources relative to the semantics of specific business processes. Second, Weaver presented at IEEE Security and Privacy Workshop on Language-Theoretic Security.
Advanced analysis methods were developed to determine the electron temperature and density evolution of targets disassembly by intense electron beams at DARHT based on shearing interferometer and shadowgraph measurements. New methods have been developed to spatially and temporally resolve electron velocity and temperature, expanding on recently published first results of Aluminum target disassembly. A surrogate model has been created for use in developing Adaptive Machine Learning analysis techniques. The results will also be used to guide the development of the system and experimental approach to future measurements on DARHT Axis-I and a new system to be deployed on DARHT Axis-II.
This is the final technical report DE-SC0018439: "Towards The Development of Rare Event Simulation Methods For Improved Mechanistic Understanding of Mineral Surface Reactivity". Under this effort we have devised a Forward Flux Sampling algorithm that utilizes time as a reaction progress coordinate.
Collection and analysis of environmental samples is commonly used by a range of stakeholders in nuclear safeguards and security contexts. While the ubiquity of samples and their transport in the environment allow regular collection, developing and demonstrating methods for analyzing these samples is difficult. In this work, an environmental sample consists of a set of one or more individual particles. Recent advances in reactor simulation have allowed us to generate data that are more representative of real-world environmental samples, enabling statistically defensible method development and testing. The most notable of these advances is a drastic increase in the number of material depletion regions, which allows our simulations to capture the variation in isotopic composition seen at length scales consistent with environmental samples. Traditional approaches for handling multiparticle samples treat each particle in the sample individually, estimating the quantity of interest (e.g., core-average burnup) resulting from measurement and analysis of signatures (e.g., nuclide assays) from each individual particle. Individual estimates are then averaged to generate a single estimate of the quantity of interest over the entire sample. In this presentation, we introduce two novel approaches for interpreting environmental samples that comprise of multiple particles: (1) the Quantile-Quantile Comparator, which uses a multivariate generalization of quantile-quantile plots for comparing unknown statistical distributions, and (2) the Set Transformer, an attention-based neural network module designed to model interactions among elements (particles) in the input set (sample). Statistically representative sampling cannot be guaranteed as samples are passively collected and are beholden to what particles are available in the environment. These new analysis methods for set-input problems are expected to be more robust than traditional approaches to issues of sampling bias where particles are not uniformly distributed throughout regions of interest, as well as generally outperform traditional approaches by jointly considering all elements in the set. We will present results comparing the performance of traditional single particle approaches and the novel Quantile-Quantile Comparator and Set Transformer for interpretation of simulated environmental samples.
Hybrid method was developed based on a need to generate accurate sensitivities for specified systems with CLUTCH. This new method provides improved sensitivities with HMF-028-001, specifically with 238 U in the large reflector region. With the importance of each voxel predetermined with the adjoint flux, the hybrid method is able to generate more accurate sensitivities. More testing is needed for other types of systems and materials (i.e., thermal and intermediate energy ranges and different moderators and reflectors). Initial results are very promising and continual development of the new hybrid method is currently in progress.
We develop a method for the determination of the top quark mass using the distribution of the decay length of the B -hadrons originating from its decay. This technique is based on our earlier observation regarding the location of the peak of the b quark energy distribution. Such “energy-peak” methods enjoy a greater degree of model-independence with respect to the kinematics of top quark production compared to earlier proposals. The CMS experiment has implemented the energy-peak method using associated b -jet energy as an approximation for b quark energy. The new method uses B -hadron decay lengths, which are related to b quark energies by convolution. The advantage of the new decay length method is that it can be applied in a way that evades jet-energy scale (JES) uncertainties. Indeed, CMS has measured the top quark mass using B -hadron decay lengths, but they did not incorporate the energy-peak method. Therefore, mismodeling of top quark transverse momentum remains a large uncertainty in their result. We demonstrate that, using energy-peak methods, this systematic uncertainty can become negligible. We show that with the current LHC data set, a sub-GeV statistical uncertainty on the top quark mass can be attained with this method. To achieve a comparable systematic uncertainty as is true for many methods based on exclusive or semi-inclusive observables using hadrons, we find that the quark-hadron transition needs to be described significantly better than is the case with current fragmentation functions and hadronization models.
In the Underground Nuclear Explosions Signatures Experiment (UNESE) radioactive 37 Ar and 127 Xe were used as tracers in subsurface migration experiments. As part of the experiment, methods were developed to quantify 127 Xe via β-γ coincidence spectroscopy. Later examination of the results highlighted a weakness of this analysis method in samples with no 127 Xe present, so a reanalysis of samples was performed to identify those which were falsely identified as having 127 Xe present. Ongoing work to develop a new analysis method with targeted regions of interest is also described. Measurements were also performed to quantify the concentration of 127 Xe and 37 Ar which were injected as part of UNESE Phase 2. A best value for the concentration of 37 Ar and 127 Xe was determined and reported here for use in future analyses of the UNESE Phase 2 results.
The NA-22 Intentional Forensics Venture is developing a system for tagging nuclear fuel using various methods of information encoding. One of the main methods under development is the insertion of isotopically enriched tracers into the fuel. In order to aid in the understanding of the neutronic performance of these taggants, we assess the quality of the nuclear data underpinning simulations, which are driven by the neutron-capture cross sections. We present these cross sections of naturally occurring isotopes of the elements provided in the neutron sublibrary of the planned ENDF/B-VIII.1 Feb. 2023 library release. We make this assessment using a rubric designed for this effort, which quantifies orthogonal features related to the overall quality. The quality metric highlights 6 aspects: experimental data, resonance evaluations, integral metrics, covariances, fission products, and documentation. We focus on energy ranges relevant for reactor applications. We also discuss additional sources for new, high-quality cross-section data that may be utilized on the time scale of the venture, including existing global data, new experiments, and computational methods. Finally, overall outlook is presented with conclusions.
This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.
Contact phenomena are crucial for understanding the behavior of mechanical systems. However, existing computational approaches for simulating mechanical contact often face numerical challenges, such as inaccurate physical predictions, energy conservation errors, and unwanted oscillations. Here, we introduce an alternative technique for simulating dynamic contact based on the non‐overlapping Schwarz alternating method, originally developed for domain decomposition. In multibody contact scenarios, this method treats each body as a separate, non‐overlapping domain and prevents interpenetration using an alternating Dirichlet–Neumann iterative process. This approach has a strong theoretical foundation, eliminates the need for contact constraints, and offers flexibility, making it ideal for multiscale and multiphysics applications. We conducted a numerical comparison between the Schwarz method and traditional methods, such as the Lagrange multiplier and penalty methods, focusing on a benchmark impact problem. Our results indicate that the Schwarz alternating method outperforms traditional methods in several key areas: it provides more accurate predictions for various measurable quantities and demonstrates exceptional energy conservation capabilities. To address unwanted oscillations in contact velocities and forces, we explored various algorithms and stabilization techniques, ultimately opting for the naïve‐stabilized Newmark scheme for its simplicity and effectiveness. Additionally, we validated the efficiency of the Schwarz method in a three‐dimensional impact problem, highlighting its inherent capacity to accommodate different mesh topologies, time‐integration schemes, and time steps for each interacting body.
Accurate and realistic geological modeling is the core of oil and gas development and production. In recent years, process-based methods are developed to produce highly realistic geological models by simulating the physical processes that reproduce the sedimentary events and develop the geometry. However, the complex dynamic processes are extremely expensive to simulate, making process-based models difficult to be conditioned to field data. In this work, we propose a comprehensive generative adversarial network framework as a machine-learning-assisted approach for mimicking the outputs of process-based geological models with fast generation. The main objective of our work is to obtain a continuous parametrization of the highly realistic process-based geological models which enables us to calibrate the models and condition the models to data. Numerical results are presented to illustrate the capability of our proposed methodology.
This chapter provides an overview of the drivers for developing correlation coefficients, status on efforts to develop partial correlation coefficients, and recommended next steps. Mathematical methods for developing and using correlation coefficients were previously documented by the FCO EWG in Chapters 6 and 7 of [Ganda 2014].
Proteins interact to form protein complex to carry out biological functions such as catalytic chemical reaction. Therefore, it is important to develop computational methods to predict protein-protein interaction and the structures of protein complexes to study and enhance protein function. In this project, we successfully developed several deep learning methods to predict inter-protein contacts and the reinforcement learning and optimization methods to reconstruct protein complex structures from predicted inter-chain contacts. The methods were integrated with the MULTICOM protein complex structure prediction system and applied to predict the complex structures of biomass production-related proteins of green algae. During the two and a half years of research and development, all the specific milestones of the project were achieved successfully. 16 publications/manuscripts were produced. 10 software tools were developed. A patent application was submitted. Our MULTICOM predictors leveraging some tools developed in this project were ranked among the top predictors in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022.
We report that for efficient core analyses of coupled fast-thermal reactors, a new deterministic method has been developed based on the variational nodal transport method of VARIANT. A new multigroup cross section generation procedure was devised by combining Monte Carlo lattice calculations for thermal assemblies and the two-step procedure of the MC 2 -3 code for fast assemblies. To reduce assembly homogenization errors in nodal transport calculations, a new nodal equivalence method was developed based on the partial current discontinuity factor (PCDF) and incorporated in VARIANT. A practical procedure to calculate PCDFs with fixed source supercell calculations was also devised. The performance of the proposed method was investigated using a test problem derived from the versatile coupled test reactor (VCTR) design. The new procedure produced multigroup cross sections accurately for all assemblies. VARIANT transport calculations with PCDFs produced accurate multiplication factor and power distribution compared to reference Serpent-2 Monte Carlo solutions.