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DOE OSTI2019

Systematic Technology Evaluation Program for SiC-based BWR Channel Box

Silicon carbide (SiC) –based accident-tolerant fuel (ATF) cladding of light water reactors (LWRs) has become a topic of great interest in nuclear material development since the Fukushima Dai-ichi Nuclear Power Plant accident. Extensive development of SiC cladding has identified benefits of the concept, including its inherent accident resistance and neutronic performance. Advancements achieved during the development of SiC-based ATF cladding have made it attractive for use in accident-tolerant cores of LWRs. In this document, we consider the use of SiC-based materials to replace the zirconium alloy used for the channel boxes of boiling water reactors. Both accident resistance and prevention of the bowing observed in current grades of channel boxes are needs driving the exploration of a new channel box system. To provide a pathway for channel box development, this document lays out (1) a current technology overview, (2) the technology gap, and (3) a systematic technology evaluation plan. The objectives of the technology evaluation plan are to address key feasibility issues revealed by the knowledge gap analysis and to contribute to advancing the technology readiness level. Further development will be required in the technology implementation phase. The technology evaluations include understanding the behavior of channel boxes under both normal and off-normal conditions, identifying potential mitigation strategies for the issues if necessary, and improving channel box performance based on the knowledge obtained. The structure of the technology evaluation plan and each task are shown in Figure S.1. The knowledge gap analysis identified key research areas, including irradiation effects, hydrothermal corrosion, accident behavior, and design and manufacture. These are common issues between SiC-based channel boxes and SiC-based fuel cladding, although technical evaluations specific for the channel box components are required. This document also describes details of the technical feasibility evaluation methods and plans.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

Contrasting Public Opinion Dynamics and Emotional Response during Crisis

We propose an approach for contrasting spatiotemporal dynamics of public opinions expressed toward targeted entities, also known as stance detection task, in Russia and Ukraine during crisis. Our analysis relies on a novel corpus constructed from posts on the VKontakte social network, centered on local public opinion of the ongoing Russian-Ukrainian crisis, along with newly annotated resources for predicting expressions of fine-grained emotions including joy, sadness, disgust, anger, surprise and fear. Akin to prior work on sentiment analysis we align traditional public opinion polls with aggregated automatic predictions of sentiments for contrastive geo-locations. We report interesting observations on emotional response and stance variations across geo-locations. Some of our findings contradict stereotypical misconceptions imposed by media, for example, we found posts from Ukraine that do not support Euromaidan but support Putin, and posts from Russia that are against Putin but in favor USA. Furthermore, we are the first to demonstrate contrastive stance variations over time across geo-locations using storyline visualization technique.

emotion analysis↗

DOE OSTI2016

Emulation of dynamic simulators with application to hydrology

Many simulation-intensive tasks in the applied sciences, such as sensitivity analysis, parameter inference or real time control, are hampered by slow simulators. Emulators provide the opportunity of speeding up simulations at the cost of introducing some inaccuracy. An emulator is a fast approximation to a simulator that interpolates between design input–output pairs of the simulator. Increasing the number of design data sets is a computationally demanding way of improving the accuracy of emulation. We investigate the complementary approach of increasing emulation accuracy by including knowledge about the mechanisms of the simulator into the formulation of the emulator. To approximately reproduce the output of dynamic simulators, we consider emulators that are based on a system of linear, ordinary or partial stochastic differential equations with a noise term formulated as a Gaussian process of the parameters to be emulated. This stochastic model is then conditioned to the design data so that it mimics the behavior of the nonlinear simulator as a function of the parameters. The drift terms of the linear model are designed to provide a simplified description of the simulator as a function of its key parameters so that the required corrections by the conditioned Gaussian process noise are as small as possible. The goal of this paper is to compare the gain in accuracy of these emulators by enlarging the design data set and by varying the degree of simplification of the linear model. We apply this framework to a simulator for the shallow water equations in a channel and compare emulation accuracy for emulators based on different spatial discretization levels of the channel and for a standard non-mechanistic emulator. Our results indicate that we have a large gain in accuracy already when using the simplest mechanistic description by a single linear reservoir to formulate the drift term of the linear model. Adding some more reservoirs does not lead to a significant improvement in accuracy. However, the transition to a spatially continuous linear model leads again to a similarly large gain in accuracy as the transition from the non-mechanistic emulator to that based on one reservoir.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Dense autoencoders, clustering techniques, and semi-supervised learning for HPGe $γ$-spectra

Classifying high-resolution gamma spectra by their isotopic content is an essential task in nuclear forensics and other applications. Traditional analysis methods are often time-intensive, but machine learning (ML) may help analysts quickly process many spectra. Such methods tend to rely on abundant, well-labeled data for training. Historical gamma data exists in various fields but is not uniformly useful for supervised ML due to inconsistent labeling. Here, to address some of these challenges, we present a method to classify and organize unlabeled data from high-purity germanium detectors using an autoencoding neural network (autoencoder). We trained dense autoencoders to compress gamma data into latent representations that enable efficient data characterization. By clustering the encoded spectra or lower-dimensional mappings of them, we identified and removed portions of over-abundant data categories, resulting in a more balanced dataset and improved autoencoder performance. This encoding and clustering pipeline also enabled the organization of spectra into self-consistent categories. Finally, we found that encoded representations showed potential as inputs for semi-supervised learning of nuclide identification (NID) labels, achieving an average F1 score of 0.85 ± 0.03 when mapping encodings to a set of 65 isotope labels.

Autoencoders↗

DOE OSTI2025

Preparation of the Multi-Site Data Processing at the Vera C. Rubin Observatory

The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) Camera is scheduled to start taking data in the summer of 2025. The Data Release Production will run the LSST Science Pipe software at data facilities in the US, France and the UK. The LSST Science Pipeline consists of complex directed acyclic graphs (DAGs) of tasks. Rubin will use the Production and Distributed Analysis (PanDA) workflow and workload management system to orchestrate this complex workflow and the distribution of workloads to the data facilities. When run end-to-end by a team of data production staﬀ, this processing (the Science Pipelines, distributed by the workﬂow and workload management system) is referred to as a 'campaign'. This paper describes the central services and data facility specific services that support this multi-site data process model, including the service deployment infrastructure, the workload and workflow system, the Campaign Management tools, and connection to Rubin Data Management. This paper will also mention the experience of processing the Rubin Commissioning Camera data. All these are part of the effort to scale up the processing capabilities for the expected very large data volume from the LSST Camera.

Yang, Wei [SLAC]↗

DOE OSTI2023

A Review of the Mechanical Properties of Materials Used in Nb 3 Sn Magnets for Particle Accelerators

Superconducting magnets experience significant thermo-mechanical loads throughout their life cycle. These are introduced by the electro-magnetic forces during powering, but also by the prestress applied in many magnet designs. Further to this, the large thermal excursion that components of different materials experience can generate significant internal forces. The loads are also experienced by the superconducting coils, whose critical current can decrease as a consequence of the applied strain. It is then crucial to predict the overall mechanical behavior and conservatively design a magnet, avoiding failure of the mechanical components and of the superconducting coils. Finite Element Analysis (FEA) is generally used to perform these tasks, but its results rely heavily on the material properties and models used. This is in particular true for the coil composite, which is simplified to allow reasonable model sizes in full magnet models. Here we present the state-of-art knowledge of the mechanical properties of the materials mostly used in superconducting magnet construction. We review elastic and plastic properties at room and cryogenic temperature, thermal contraction, and summarize the state-of-art failure criteria for these materials. Finally, the paper summarizes the present understanding of the mechanical behavior and limits of Nb3Sn coils. For the first time, an orthotropic failure criteria is proposed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

MedNAS: Multiscale Training-Free Neural Architecture Search for Medical Image Analysis

Deep neural networks have demonstrated impressive results in medical image analysis, but designing suitable architectures for each specific task is expertise dependent and time consuming. Neural architecture search (NAS) offers an effective means of discovering architectures. It has been highly successful in numerous applications, particularly in natural image classification. Yet, medical images possess unique characteristics, such as small regions and a wide variety of lesion sizes, that differentiate them from natural images. Furthermore, most current NAS methods struggle with high computational costs, especially when dealing with high-resolution image datasets. In this article, we present a novel evolutionary NAS method called multiscale training-free neural architecture search (MSTF-NAS) to address these challenges. Specifically, to accommodate the broad range of lesion region sizes in disease diagnosis, we develop a new reduction cell search space that enables the search algorithm to explicitly identify the optimal scale combination for multiscale feature extraction. Further, to overcome the issue of high computational costs, we utilize training-free indicators as performance measures for candidate architectures, which allows us to search for the optimal architecture more efficiently. More specifically, by considering the capability and simplicity of various networks, we formulate a multiobjective optimization problem that involves two training-free indicators and model complexity for candidate architectures. Extensive experiments on a large medical image benchmark and a publicly available breast cancer detection dataset are conducted. The empirical results demonstrate that our MSTF-NAS outperforms both human-designed architectures and current state-of-the-art NAS algorithms on both datasets, indicating the effectiveness of our proposed method.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.

chemical space exploration↗

DOE OSTI2024

Approach for energy efficient building design during early phase of design process

Energy consumption in the building sector is about 40% of total energy consumed globally and is trending upwards, along with its contribution to greenhouse gas (GHG) emissions. Given the adverse impacts of GHG emissions, it is crucial to integrate energy efficiency into building designs. The most significant opportunities for enhancing energy performance are present during the initial phases of building design, when there is less impact of other design constraints. Various tools exist for simulating different design options and providing feedback in terms of energy consumption and comfort parameters. These simulation outputs must then be analyzed to derive design solutions. This paper presents an innovative approach that utilizes user input parameters, processes them through cloud computing, and outputs easily understandable strategies for energy-efficient building design. The methodology employs Asynchronous Distributed Task Queues (DTQ) - a more scalable and reliable alternative to conventional speedup techniques-for conducting parametric energy simulations in the cloud. The goal of this approach is to assist design teams in identifying, visualizing, and prioritizing energy-saving design strategies from a range of possible solutions for each project. Furthermore, a tool ‘eDOT’ has been developed utilizing the discussed methodology. Unlike existing tools, eDOT leverages artificial intelligence to dynamically generate and provide design strategies during the early phases of design process. By simplifying the simulation process, eDOT enables design teams to make informed, data-driven decisions without needing to interpret complex simulation outputs. A case study simulated for two locations is provided in this paper to demonstrate the effectiveness of eDOT, further underscoring its practical impact on energy-efficient building design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2012

Hydraulic Conductivity of Saltstone Formulated Using 1Q11, 2Q11 and 3Q11 Tank 50 Slurry Samples

As part of the Saltstone formulation work requested by Waste Solidification Engineering (WSE), Savannah River National Laboratory (SRNL) was tasked with preparing Saltstone samples for fresh property analysis and hydraulic conductivity measurements using actual Tank 50 salt solution rather than simulated salt solution. Samples of low level waste salt solution collected from Tank 50H during the first, second, and third quarters of 2011 were used to formulate the Saltstone samples. The salt solution was mixed with premix (45 wt % slag, 45 wt % fly ash, and 10 wt % cement), in a ratio consistent with facility operating conditions during the quarter of interest. The fresh properties (gel, set, bleed) of each mix were evaluated and compared to the recommended acceptance criteria for the Saltstone Production Facility. ASTM D5084-03, Method C was used to measure the hydraulic conductivity of the Saltstone samples. The hydraulic conductivity of Saltstone samples prepared from 1Q11 and 2Q11 samples of Tank 50H is 4.2E-9 cm/sec and 2.6E-9 cm/sec, respectively. Two additional 2Q11 and one 3Q11 sample were not successfully tested due to the inability to achieve stable readings during saturation and testing. The hydraulic conductivity of the samples made from Tank 50H salt solution compare well to samples prepared with simulated salt solution and cured under similar conditions (1.4E-9 - 4.9E-8 cm/sec).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2013

Beam-based alignment in RHIC

A new measurement procedure and a new data analysis algorithm have been introduced to alleviate the difficulties faced by beam-based alignment (BBA) tasks in RHIC. New codes were also developed to implement the new methods in measurements and analysis correspondingly. These improvements have enabled us to successfully perform BBA during the RHIC 2012 and 2013 runs. The BPM offset corrections calculated from the BBA results were implemented and significant improvements were seen in most of the before-and-after comparisons. Here we discuss the measurability and other important BBA issues in RHIC. We present the new measurement procedure, new data analysis algorithm and the new codes that have been developed. We summarize the BBA measurements, analysis, and corrections in RHIC to date. The BBA accuracy limitations are also discussed. As a continuing effort, we also present application code that is under development for future BBA operations in RHIC.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

Technical Report on Modeling for Quasispecies Abundance Inference with Confidence Intervals from Metagenomic Sequence Data

The overall aim of this project is to develop a software package, called MetaQuant, that can determine the constituents of a complex microbial sample and estimate their relative abundances by analysis of metagenomic sequencing data. The goal for Task 1 is to create a generative model describing the stochastic process underlying the creation of sequence read pairs in the data set. The stages in this generative process include the selection of a source genome sequence for each read pair, with probability dependent on its abundance in the sample. The other stages describe the evolution of the source genome from its nearest common ancestor with a reference genome, breakage of the source DNA into short fragments, and the errors in sequencing the ends of the fragments to produce read pairs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

The Effect of Process History on Grain Boundaries and Dislocation Substructures on Functional Properties of Nb for SRF Cavities: Plastic Formability and Microstructural Evolution. Final report

While much progress has been made in improving the performance of cavities in the past decade, reproducible performance remains elusive. This renewal of “The cost of grain boundaries on the performance of superconducting cavities” examines in further depth the interactions between forming and annealing processes and functional performance at the scale of dislocation substructure and grain boundaries. There are five tasks identified in the renewal, which are: Characterization / analysis of current cavity technology: Effect of deformation, heat treatment, surface treatments, and residual damage on cavity function (Lee and Bieler) Characterization of dislocation mechanisms in single and bicrystals (Bieler) Quantifying effects of impurities and deformation mechanisms in Nb using density functional theory calculations (Solanki) Crystal plasticity constitutive model development (Pourboghrat and Eisenlohr) Modeling and measuring thermal conductivity in thin layers (Wright)

36 MATERIALS SCIENCE↗

DOE OSTI2021

Experimental Assessment of Elastic Modulus vs. Relative Density for Stretch- and Bend-Dominated Lattices

This project was designed to study the possibility of using structural properties of lattices to replicate the material properties of certain hard to manufacture designs and use topology optimization to determine the lattice type and density required to mimic these properties. The integration of additively manufactured lattice structures with topology optimization highlights the need for well characterized mechanical properties and uncertainty analyses to insure these optimized structures respond as predicted. Stereolithographically printed octet and rhombic dodecahedron lattices were manufactured at 10%, 25% and 65% density by volume. As a separate task, yet integrated into this work, finite element analysis (FEA) was used to predict the printed lattice’s mechanical properties, which were then compared to our experimental results. After comparing FEA and experimentallymeasured elastic moduli, it was determined that the FEA provides highly reliable predictions for the modulus of these printed lattice structures. These lattices also exhibited greater tensile stiffness than that of the solid material demonstrating the flexibility that lattices provide to designing parts with designer structural properties. The accurate printing and reliable modeling of these lattices will enable topology optimization of complex parts from well-characterized rhombic dodecahedron and octet lattice structures of varying densities.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Low-Cost Recyclable Oxygen Carrier and Novel Process for Chemical Looping Combustion

The University of North Dakota, through its Institute for Energy Studies and Energy & Environmental Research Center, partnered with Envergex LLC, Barr Engineering and Microbeam Technologies to develop a transformational enabling technology for advancement of Chemical Looping Combustion technology. Industrial support was provided by Carbontec Energy Corporation. The project targeted the two biggest challenges to chemical looping combustion: (1) High costs of oxygen carrier replacement/loss due to expensive manufacturing and high replacement rates from physical attrition and/or decrease in reactivity, and (2) Inherently slow fuel char conversion which represents the rate-limiting step for chemical looping combustion and results in very large equipment sizes, and overall lower carbon dioxide capture efficiency. The project activities were addressed in a series of seven tasks. Task 1 extended throughout the entire project and oversaw project management and execution. Task 2 and 3 focused on development and evaluation of the novel oxygen carrier. Task 4 to 7 focused on design of the novel reactor, testing with the novel oxygen carrier and a techno-economic assessment of the process. The list of tasks are: Task 1 – Project management and planning, Task 2 – Laboratory scale oxygen carrier manufacturing and assessment, Task 3 – Modeling and laboratory-scale evaluation of oxygen carrier performance with coal, Task 4 – 10-kilowatt thermal integrated system installation, Task 5 – Scaled-up oxygen carrier manufacturing, Task 6 – 10-kilowatt testing, and Task 7 – Process design and techno-economic analysis.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

High-Density Implosions on OMEGA and the National Ignition Facility (NIF) (Final FY23 report for the subcontract B656484 (formerly B640112))

This final FY23 report for the subcontract B656484 (formerly B640112) “High-Density Implosions on Omega and the National Ignition Facility (NIF)” summarizes MIT’s support of LLNL experiments at OMEGA and the NIF with nuclear diagnostics, platforms and analysis, and of training PhD students. Overall, the tasks identified as Statement-Of-Work (SOW) items for FY23 were accomplished.

42 ENGINEERING↗

DOE OSTI2024

Codes and Ordinances for Clean Energy: Highland Park Pathways to Power [Slides]

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Distribution Grid Impact Study in Highland Park, Michigan: Understanding Rooftop Solar, Behind-the-Meter Energy Storage, Electric Vehicle Charging, and Building Electrification [Slides]

Through the Communities LEAP (Local Energy Action Program) Pilot, the National Renewable Energy Laboratory (NREL) engaged the Highland Park Stakeholder Coalition to scope four technical assistance work areas to address their energy needs and goals. This slide deck addresses the highlighted tasks under work area "B" related to policy analysis, due diligence, and case studies for removing barriers and providing best practices for local clean energy development. Highland Park community members face frequent, long-duration power interruptions due largely to the aging distribution system serving the area and the legacy design standards used in its construction. While degrading physical infrastructure such as poles, crossarms, and transformers can result in this substandard reliability, another notable characteristic of this legacy system is the lower, 4.8 kV, voltage class. This is a legacy construction standard which many utilities, DTE included, are phasing out in favor of higher, 15 or 25 kV, voltage classes instead. The existing 4.8 kV distribution system serving Highland Park may limit significant adoptions of clean-energy technologies like high percentages of building electrification or electric vehicle adoption. The following analysis seeks to quantify these limitations under a variety of clean-energy technology adoption scenarios. It compares the overall system risks of the present system to those of a hypothetical, upgraded 13.2 kV system, using NREL-developed risk metrics and offers upgrade cost considerations. The legacy 4.8 kV voltage class serving Highland park is not, as we have modeled it, a major limitation to the widespread adoption of cost-optimal rooftop solar and/or behind-the-meter energy storage. Within our modeling framework, these technologies namely impact secondary, low-voltage assets, which may be remedied without the need for a system-wide upgrade to a 13.2 kV voltage class. Our model of the current 4.8kV system indicates it is not capable of supporting community-wide electrification efforts. Widespread building electrification, and the resulting large increase in wintertime load, dramatically increases the prevalence of voltage violations and thermal overloading on the current 4.8 kV distribution system. These impacts illustrate the need for system-wide upgrades to a 13.2 kV voltage class to accommodate these technologies. Low to Moderate DER adoption does not adversely impact the grid but does improve undervoltage and asset overloading issues. However, these benefits are insufficient to defer grid upgrades. Higher DER penetration is shown to increase overvoltage and asset overloading in future electrification scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗