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Presentation for the International High-Level Radioactive Waste Management conference on the System Integration Analysis and Support control account in the Integrated Waste Management program
Spent nuclear fuel (SNF) generated by the current fleet of commercial nuclear reactors is being stored at the reactor sites in spent fuel pools (SFPs) and in dry independent spent fuel storage installations (ISFSIs). The US Department of Energy Office of Nuclear Energy (DOE-NE) is developing an Integrated Waste Management Program (IWMP) comprising a suite of options and supporting analyses to enable future informed choices. The IWMP is organized into the following five major areas: 1) Consent-Based Siting, 2) IWM Facilities and Equipment Concepts and Development, 3) Transportation Capability Analysis and Support, 4) Information Technology Solutions and Support, and 5) System Integration Analysis and Support. This paper discusses the activities ongoing in the IWMP System Integration Analysis and Support area. Two main areas of research in system integration are data and tools development, as well as system analysis assessments. One of the tools being developed in the system integration area is the Used Nuclear Fuel-Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS) tool. It is being developed as a foundational resource for DOE-NE to manage SNF data, along with several compatible analysis tools for time-dependent characterization of SNF and related systems. UNF-ST&DARDS has the unparalleled ability to track SNF through the entire back end of the fuel cycle—from the time the fuel is discharged from a reactor through its disposal in a geological repository. UNF ST&DARDS interfaces with the SCALE code system for nuclear analysis and COBRA-SFS for thermal analysis. Another main tool being developed is the Next Generation System Analysis Model (NGSAM). NGSAM is an agent-based simulation software tool expressly designed to be capable of modeling the waste management system. NGSAM has been developed to enable informed decision-making by providing the capability of analyzing various potential system options for the management of SNF and HLW. Using NGSAM, system architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analysis assessments may investigate the implications of various strategies such as different acceptance rates, acceptance queues, facility capacities and options, standardized canisters, and different assumed system operation start dates. Recently, some system analysis effort has begun to look at how the waste management system might operate for advanced reactor fuel cycles.
Spent nuclear fuel (SNF) generated by the current fleet of commercial nuclear reactors is being stored at reactor sites in spent fuel pools (SFPs) and in dry independent spent fuel storage installations (ISFSIs). The U.S. Department of Energy Office of Nuclear Energy (DOE-NE) Integrated Waste Management (IWM) program is examining a suite of IWM system options and conducting supporting analyses to enable future informed choices. The IWM program is currently organized into the following four major areas: (1) IWM facilities and equipment concepts and development, (2) transportation capability analysis and support, (3) information technology solutions and support, and (4) system integration analysis and support. This paper focuses on the activities ongoing in the IWM system integration analysis and support area. Two main research activities in this area are: data and tools development, validation, and maintenance; and special studies, analyses, and assessments.
Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.
Integrating biomass-derived intermediates into traditional petroleum refineries presents unique challenges and opportunities, requiring innovative analysis approaches that account for biofuel producers and refiners. Consequently, teams within the National Renewable Energy Laboratory (NREL) have developed a comprehensive refinery integration analysis framework that combines experimental data, detailed techno-economic analyses of bio-conversion pathways, and economic projections within refinery linear programming (LP) optimization models. These models enable the identification of promising bio-integration strategies tailored to specific refinery configurations, economic conditions, and production goals. They also capture upstream and downstream impacts, highlighting critical bottlenecks and research opportunities for increasing the share of biogenic feedstocks in traditional refining operations. Refinery models are also packaged into a broader bio-economy optimization framework which enables biofuel supply chain optimization with standalone biofuel production and refinery co-processing/repurposing options. This presentation discusses promising refinery bio-integration strategies along with key challenges and opportunities identified using NREL's refinery and bio-economy optimization frameworks.
In the search for physics beyond the Standard Model, the Muon g - 2 Experiment at Fermilab (E989) will make the most precise measurement of the anomalous magnetic moment of the muon, a μ . Improvements in precision come from both increased statistics and new techniques to significantly reduce previous systematic uncertainties. The muon a μ is determined by extracting both the anomalous spin precession frequency, ω a , and the average magnetic field sampled by the muons, B. Traditionally an energy threshold analysis method which requires reconstruction of decay positrons from the muon decay, μ + → e + ν e ν μ has been used to extract ω a . This thesis will describe a novel, energy-integrated analysis method for ωa extraction that does not require full reconstruction of positron hits and lowers the energy threshold therefore sampling more of the stored muons. This new technique has different sensitivities to effects like gain fluctuations and positron pulse pile-up. The analysis results will focus on data taken in 2019, the second year of running the experiment.
While a rich set of putative cis-regulatory sequences involved in mouse fetal development have been annotated recently on the basis of chromatin accessibility and histone modification patterns, delineating their role in developmentally regulated gene expression continues to be challenging. To fill this gap, here we mapped chromatin contacts between gene promoters and distal sequences across the genome in seven mouse fetal tissues and across six developmental stages of the forebrain. We identified 248,620 long-range chromatin interactions centered at 14,138 protein-coding genes and characterized their tissue-to-tissue variations and developmental dynamics. Integrative analysis of the interactome with previous epigenome and transcriptome datasets from the same tissues revealed a strong correlation between the chromatin contacts and chromatin state at distal enhancers, as well as gene expression patterns at predicted target genes. We predicted target genes of 15,098 candidate enhancers and used them to annotate target genes of homologous candidate enhancers in the human genome that harbor risk variants of human diseases. We present evidence that schizophrenia and other adult disease risk variants are frequently found in fetal enhancers, providing support for the hypothesis of fetal origins of adult diseases.
Here, an integrated workflow for fast-ion analysis was developed by adapting the One Modeling Framework for Integrated Task (OMFIT) workflow manager to support a standard and unified analysis platform for KSTAR users. The newly established analysis suite offers a graphical user interface–based workflow to enable users to readily access and handle experimental data archived in various data formats and servers. Further, users can analyze the data by importing modules designed for conducting certain tasks, such as profile fitting, equilibrium reconstruction, and postprocessing of tokamak data. The procedures for preparing the inputs for fast-ion simulations are streamlined by a common workflow manager, which enables the parallel processing of various tasks to efficiently analyze large fast-ion datasets. The OMFIT platform comprises a flexible Python-based application that enables users to freely manipulate the Python scripts for applications that are unavailable in the standard workflow. The framework also offers mapping tools to translate the output data into the Integrated Modeling and Analysis Suite format to maintain application compatibility for future ITER burning plasma experiments.
This report documents the energy storage systems (ESS) integration portion of the work conducted under the Office of Energy Efficiency and Renewable Energy (EERE) advanced Manufacturing Office (AMO) Lab Call DE-LC-000L059 project titled "Advanced Turbine Airfoils for Efficient CHP Systems." The objective of this work is to analyze the previously-developed combined heat and power (CHP) plant with the upgraded gas turbine to include an ESS to improve the operational flexibility. The analysis includes identifying a candidate CHP configuration for the ESS integration and identifying possible storage systems to be integrated. A qualitative analysis was made to down select possible storage technologies and a techno-economic analysis (TEA) was made for the CHP system with the selected storage technology.
High-energy particles, including photons (x-ray, γ-ray, bremsstrahlung), electrons, and protons, possess the capability to penetrate materials and deposit energy within them. The degree of absorption depends on both the energy and type of particles, as well as the properties of the materials with which they interact. This energy deposition can manifest either at the material's surface or throughout its volume, potentially resulting in various failure modes. The primary aim of this paper is to establish a structured analysis methodology for evaluating the structural integrity of beam-intercepting devices when subjected to high-energy particles. Here, the paper also reviews some of the underlying physics, pertinent to the scope of the thermomechanical analysis, potential failure modes, and introduces verification and validation methodologies. Engineers and researchers can utilize the guidelines presented in this paper to effectively plan the development of beam intercepting devices, thereby ensuring their reliability and performance in the presence of high-energy particle exposure.
This paper presents a new method for enhancing Alternating Current Power Flow (ACPF) analysis. The method integrates the Newton-Raphson (NR) method with Enhanced-Gradient Descent (GD) and computational graphs. The integration of renewable energy sources in power systems introduces variability and unpredictability, and this method addresses these challenges. It leverages the robustness of NR for accurate approximations and the flexibility of GD for handling variable conditions, all without requiring Jacobian matrix inversion. Furthermore, computational graphs provide a structured and visual framework that simplifies and systematizes the application of these methods. The goal of this fusion is to overcome the limitations of traditional ACPF methods and improve the resilience, adaptability, and efficiency of modern power grid analyses. We validate the effectiveness of our advanced algorithm through comprehensive testing on established IEEE benchmark systems. Furthermore, our findings demonstrate that our approach not only speeds up the convergence process but also ensures consistent performance across diverse system states, representing a significant advancement in power flow computation.
The accurate quantification and mapping of subsurface natural fracture systems using borehole imaging logs are critical for the success of CO 2 sequestration in geologic formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and DL workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by using actual electrical resistivity of borehole wall from microresistivity imaging device alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to the Illinois Basin-Decatur Project site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with a high resolution of 0.01 ft or 0.12 in. (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO 2 pressure forecasting and postinjection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared with alternative feature extraction technologies. It presents exciting possibilities for advancing CO 2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO 2 sequestration projects, facilitating sustainable environmental practices, and combating climate change.
Ti6Al4V alloy has been widely used in many fields, such as aerospace and medicine, due to its excellent biocompatibility and mechanical properties. Most high-performance components made of Ti6Al4V alloy usually need to be polished to produce their specific functional requirements. However, due to the material properties of Ti6Al4V, its polishing process still requires significant development. Therefore, this study aimed to investigate the performance of polishing Ti6Al4V by using tools with different rigidities. Two kinds of bonnet tool were used, namely a pure rubber (PR) bonnet and a semirigid (SR) bonnet. The characterization of material removal and surface integrity after polishing was conducted through a series of experiments on a 6-DOF robotic polishing device. The results demonstrate that both bonnet tools successfully produce nanometric level surface roughness. Moreover, the material removal rate of the SR bonnet tool is significantly higher than that of the PR bonnet, which is consistent with the material removal characteristics of glass polishing in previous research. In addition, the presented analysis on key polishing parameters and surface integrity lays the theoretical foundation for the polishing process of titanium alloy in different application fields.
Crassulacean Acid Metabolism (CAM) is an adaptation that temporally separates carbon uptake at night from photosynthesis during the day. CAM has evolved repeatedly across vascular plants, as its emergence may depend on simple regulatory changes to deeply conserved metabolic pathways. Modern CAM research relies heavily on interpretation of transcriptomic data, though regulation occurs at multiple levels following transcription. Additionally, while most research to date has focused on a handful of genes and metabolites in the core CAM pathway, the co-option of conserved regulatory and functional genes is bound to have wide-ranging effects on other aspects of primary metabolism. In this study, we integrate transcriptomic, proteomic, and metabolomic data to compare primary metabolism between the CAM species Yucca aloifolia and closely related C 3 species, Y. filamentosa. We observe minimal correlation between protein abundance and mRNA expression, suggesting significant post-transcriptional regulation in CAM species. We also find evidence of shifts in gene expression and metabolite accumulation outside of the central CAM pathway, suggesting that the shift to CAM has cascading effects across primary metabolism, especially nitrogen metabolism. Our findings provide insights into the metabolic shifts associated with CAM evolution, and highlight the complexity of its regulation at multiple biological levels.
Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.
Abstract The neutronics performance of a tokamak has been identified as an important factor in designing a fusion power plant. The design of the tokamak should not only meet operational parameters such as sufficient tritium breeding, but also safety parameters such as low structural material activation. This paper investigates the impacts of the neutronics metrics for the ARC-class tokamak, a compact tokamak with an immersion blanket, by perturbing the first five layers of structural material—first wall, inner vacuum vessel, coolant salt channel, neutron multiplier, and outer vacuum vessel. The goal of this work is to provide insight into shaping and scaling the flux on each layer to obtain optimized operational and safety metrics through quantification of the responses from each perturbation. Results show that increased first wall thickness can increase the tritium breeding ratio (TBR) in specific configurations with high 6 Li enrichments and that vacuum vessels decrease TBR for low- 6 Li enrichment configurations. It was also found that the neutron multiplier can either increase or decrease TBR depending on the configuration. The response of metrics to the change in layer thickness and enrichment also varies depending on the vacuum vessel material. The integral impacts of 6 Li enrichment, layer thicknesses, and vacuum vessel material choice are investigated and presented in this paper.
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The “light mantle” deposit at the base of South Massif in the Moon's Taurus‐Littrow Valley was a primary science target for the Apollo 17 exploration. The possibility that it was a landslide triggered by ejecta from Tycho Crater is critical for establishing the age of Tycho and constraining recent lunar impact chronology; however, the mechanism of emplacement of the deposit has recently been questioned. The newly opened 73001/73002 double drive tube from Station 3 sampled 70.6 cm deep into the regolith and represents the first stratigraphic section of an extraterrestrial landslide deposit returned to Earth. Here we provide an overview of the stratigraphy of the 73001/73002 core based on top to bottom variations revealed by coordinated laboratory analyses and explore constraints on the emplacement of the light mantle deposit. Briefly, the upper ∼10 cm of 73002 contains a disturbed zone from space weathering and emplacement of ejecta from a nearby crater that excavated and ejected basaltic material. Below 10 cm is a nearly uniform unit of immature regolith. These data support a single event for the emplacement of the deposit at this location, followed by weathering and mixing of materials from nearby crater ejecta in the upper 10 cm. Slight variations in chemistry and clast components may reflect the relative stratigraphy of the South Massif slope, with material toward the bottom of 73001 originating from lower slopes and material from higher up in the core representing regolith from higher up the South Massif slopes.