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At least 631 records · Page 35

Determining the Role of Synthesis and Reaction Conditions on the Exposed Surface and Shape of ..beta..-Mo2C Catalysts

The production of drop-in quality biofuels typically can be achieved through catalytic fast pyrolysis (CFP) of biomass, followed by hydrodeoxygenation (HDO) of the vapor-phase product to reduce the oxygen content and stabilize the bio-oil product. To favor selectivity toward desired deoxygenation products, bifunctional catalysts with both acidic and hydrogenation sites are often employed. Due to both its oxophilicity and its ability to promote hydrogenation reactions, molybdenum carbide catalysts have shown promise for promoting the HDO of CFP vapors. However, the surface structure and composition of carbide catalyst particles are not well understood, precluding the establishment of fundamental structure-function relationships needed for the rational development of molybdedum carbide for HDO, as well as other reactions. For orthorhombic ..beta..-Mo2C, the (100) facet is largely studied for computational surface-reaction analyses, possibly due to its similarities with the close-packed (111) facet of noble-metal catalysts. However, it has previously been postulated through ab initio thermodynamic studies that the (111), (110), and (011) facets are most stable, though there is no consensus. Furthermore, while a majority of previous surface stability analyses of ..beta..-Mo2C focus on bulk terminations, the highly carburizing synthesis conditions and the use of ..beta..-Mo2C in carbon-containing reaction applications could introduce non-stoichiometric coverages of surface carbon. As such, this study uses ab initio thermodynamics calculations to assess the relative surface stability of the low-index facets of ..beta..-Mo2C, including consideration of varying the coverage of surface carbon. From this information, Wulff constructions are utilized to predict the synthesis and reaction condition-dependent shape and surface carbon composition of ..beta..-Mo2C catalyst particles.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify if the software is reliable (or unreliable) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify software reliability (or unreliability) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Development and Refinement of DER Steady State Model for QSTS Analysis

EPRI formed a working group of utilities and software vendors called the Distribution Advanced PV Model Working Group (DAWG). The working group has been focused on building consensus regarding the models that represent the reactive power capabilities outlined in Section 5 of IEEE Std 1547™-2018. This report provides an overview of the reactive and active power capabilities specified in IEEE Std 1547™-2018 and outlines a specification of key functions for usage during steady state snapshot and time series power flow analysis. Additionally, it presents a framework for performing tests to verify the correct operation of functions during power flow analysis.

14 SOLAR ENERGY↗

A Unified Testing Platform to Mature Blockchain Applications for Grid Emulation Environments: Preprint

Blockchain technology is a relatively novel technology that can be used to develop more decentralized, autonomous and tamper-evident solutions. A feature that can aid Transactive Energy Systems to reach their goals by enabling individual actors to communicate and reach consensus with other participants in a more decentralized fashion. However, technical barriers to evaluate and adopt this type of technology within the electrical domain still exist. To facilitate this task, BLOSEM Unified Testing Platform (UTP), a DOE-sponsored, multi-lab effort intends to accelerate the development of solutions by offering a common set of reusable services that can be used to interconnect existent grid tools with blockchain services. UTP is intended to serve as development platform that can provide application engineers with the technical means to evaluate potential blockchain solutions, by enabling them to concentrate on the actual application functionalities while at the same time abstracting the connectivity and performance measurement tasks. The use of BLOSEM UTP is further demonstrated by implementing two potential use cases that are intended to validate both the feasibility of implementing these applications as blockchain-based solutions while also demonstrating the features provided by UTP.

blockchain↗

Internal Collaboration on Recent Nuclear Criticality Safety Assessments

Prevention of inadvertent criticality at facilities with large quantities of fissionable materials is one of the most important requirements those facilities grapple with. Given that criticality cannot be mitigated, only eliminated, a hard line must be taken on this requirement. The facilities and sites with the possibility of such an event must abide by a plethora of requirements, most stemming from the ANSI/ANS-8 series of consensus standards. One such standard, ANSI/ANS-8.19, Administrative Practices for Nuclear Criticality Safety, gives requirements and recommendations necessary for establishing a nuclear criticality safety program for a given facility or site. One of those requirements includes periodic assessments of the NCS program. To meet the assessment requirement, Los Alamos National Laboratory (LANL) conducts periodic assessments on individual facilities and overall programmatic health aspects. The teams developed to perform these assessments include people both inside and outside the LANL NCS program. Recently, the Nuclear Criticality Safety Division and the Critical Experiments Team of the Advanced Nuclear Technology Group established a collaboration to aid in fulfilling the assessment requirement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Nuclear Criticality Safety Pipeline Course with Hands-On Experimental Training at Lawrence Livermore's Inherently Safe Subcritical Assembly Training Center

The Nuclear Criticality Safety Divisions at Lawrence Livermore National Laboratory (LLNL) and Los Alamos National Laboratory (LANL) have partnered with Prof. Massimiliano Fratoni of the University of California Berkeley to offer a semester long course on nuclear criticality safety. This course is part of a larger pipeline project among many of the Department of Energy (DOE) laboratories designed to stimulate student interest in the field of criticality safety. The course focuses on teaching the fundamentals of criticality safety, familiarity with national and consensus standards, and preparing criticality safety evaluations. Students also receive hands-on experience with special nuclear material by performing experiments with the Inherently Safe Subcritical Assembly (ISSA) at LLNL. Guest lectures are taught remotely and in-person by criticality safety engineers at LLNL and LANL, giving students an opportunity to interact with professionals in the field. The students complete a semester long project involving developing and writing a criticality safety evaluation. As universities tend to focus heavily on nuclear power and advanced nuclear reactor design, this course gives students a better understanding and perspective of what criticality safety entails. The goal of this pipeline course is to introduce students to criticality safety as another available field for nuclear engineers. It is also a way for criticality safety programs to identify talented students who have the interest and aptitude to work in criticality safety for hire upon graduation. LLNL and LANL have both hosted past students as summer students, participated in student's graduate projects, and hired students as criticality safety engineers. This has provided a unique opportunity for criticality safety programs to spot young talent with better retention outcomes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Selective thermal evolution of native oxide layer in Nb and Nb$_3$Sn-coated SRF grade Nb: An in-situ angular XPS study

This contribution discusses the results of an in-situ angular XPS study on the thermal evolution of the native oxide layer on Nb3Sn and pure Nb. XPS data were recorded with conventional spectrometers using an AlK(alpha) X-ray source for spectra collected up to 600 C, and an MgK(Alpha) X-rays source for temperatures above 600 C. The effect of the thickness, composition, and thermal stability of that oxide layer is relevant to understanding the functional properties of superconducting radiofrequency (SRF) cavities used in particle accelerators. There is a consensus that oxide plays a role in surface resistance (Rs). The focus of this study is Nb3Sn, which is a promising material that is used in the manufacturing of superconducting radiofrequency (SRF) cavities as well as in quantum sensing, and pure Nb, which was included in the study for comparison. The thermal evolution of the oxide layer in these two materials is found to be quite different, which is ascribed to the influence of the Sn atom on the reactivity of the Nb atom in Nb3Sn films. Nb and Sn atoms in this intermetallic solid have different electronegativity, and the Sn atom can reduce electron density around neighbouring Nb atoms in the solid, thus reducing their reactivity for oxygen. This is shown in the thickness, composition, and thermal stability of the oxide layer formed on Nb3Sn. The XPS spectra were complemented by grazing incident XRD patterns collected using the ESRF synchrotron radiation facility. The results discussed herein shed light on oxide evolution in the Nb3Sn compound and guide its processing for potential applications of the Nb3Sn-based SRF cavities in accelerators and other superconducting devices.

Cano, Arely↗

Blockchain applicability framework

Technology related to blockchain cybersecurity solutions and a blockchain applicability framework is disclosed. In one example of the disclosed technology, a system is configured to receive parameters for a blockchain candidate application and evaluate the parameters to determine a recommendation for types of blockchain to apply to the candidate application. The recommendation may be based on an evaluation of the parameters to determine a level of applicability of blockchain usage, a level of applicability of one or more blockchain privacy types, and a level of applicability of one or more blockchain consensus types. The system may be configured to calculate an overall percentage distribution of the levels of applicability and to output an indication of the overall percentage distribution.

Gourisetti, Sri Nikhil Gupta↗

Investigation of the Use of Dynamic Probabilistic Risk Assessment Methodologies for Identifying Digital I&C System Common Cause Failures

Digital Instrumentation and Control (I&C) systems have a key role in nuclear power plants in the upgrade of aging analog systems. Digital systems improve plant safety and reliability through features such as increased hardware reliability and stability and improved failure detection capability. There is no consensus on which of the current probabilistic risk assessment methods are most suitable for use in the reliability analysis of digital I&C systems. While the traditional event-tree/fault-tree (ET/FT) approach is still used for their reliability modeling, there are concerns regarding this approach in properly accounting for dynamic interactions among system components since potentially significant dependencies among failure events may not be identified and/or their likelihood may not be properly quantified. Dynamic methodologies are expected to provide a much more accurate representation of probabilistic evolution of the I&C systems in time due to their capability to more properly account for complex interactions than the static approach. The applicability of dynamic PRA methodologies for digital I&C system is investigated using the criteria presented in the NUREG/CR-6901, and the comparisons made in NUREG/CR-6901 are updated in light of the latest studies. The Dynamic Event Tree (DET) approach has been identified as one of the top dynamic methods when evaluated against the requirements for the reliability modeling of digital I&C systems. The DET method is a strong candidate for integration into existing PRA studies, as it bears many similarities to the traditional ET approach. In this study, the DET approach has been applied to the Plant Protection System of the APR1400 design, and the results are compared to results from its available traditional ET/FT analysis. Possible approaches to evaluate and quantify the effects of common cause failures on system safety using dynamic methods are also examined.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Machine Learning for Advanced Building Construction: Preprint

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)- enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

building retrofits↗

Measurement of the muon anomalous precession frequency at the Muon g − 2 Experiment at Fermilab

The anomalous magnetic moment of the muon, $a_\mu = \frac{g-2}{2}$ is the fractional deviation of the muon $g$-factor from the value of 2. It emerges as the cumulative effect of the virtual particles participating in the muon interaction with a magnetic field via quantum loop corrections. Its value encodes all the possible interactions between the virtual particles and, for this reason, represents an important test of the Standard Model (SM). In particular, any deviation from the SM theoretical evaluation could be due to new physics contributions. The new Muon $g-2$ (E989) Experiment at Fermilab is currently operating to repeat and improve the previous E821 experiment at Brookhaven National Laboratory (BNL), aiming to reduce the experimental error by a factor of 4 to the final accuracy of 140 parts per billion (ppb). On April 7th, 2021, the E989 collaboration published the first result based on the first year of data taking (Run-1), measuring $a_\mu = 0.001~165~920~40(54)$ with a precision of 460 ppb. The measured value is consistent with the BNL measurement and strengthens the long-standing tension with the data-driven SM prediction to a combined discrepancy of 4.2$\sigma$. On the theory side, however, new efforts involving lattice-QCD techniques are starting to question the current consensus on the theoretical prediction, demanding new improvements on both the experimental and theoretical sides. The E989 collaboration is now finalizing the analysis of Run-2 and Run-3 data and a new publication is expected in the first half of 2023 with a combined statistical uncertainty of 200 ppb. The anomalous magnetic moment $a_\mu$ is measured as the ratio between the muon spin anomalous precession frequency, $\omega_a$, and the average magnetic field experienced by the muons as they circulate in the storage ring. This thesis presents a precession frequency analysis of the Run-1 data and an evaluation of the related systematic uncertainties. A new positron reconstruction developed for the analysis of the subsequent data-taking periods, aiming to reduce some of the major systematic uncertainties of the $\omega_a$ measurement, is presented. The author's involvement in the production of the Run-2/5 data and in the precise calibration of the detectors is discussed. Finally, the complete Run-1 $a_\mu$ result is presented.

43 PARTICLE ACCELERATORS↗

Machine Learning for Advanced Building Construction

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)-enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

build scans↗

Strategies in Education, Outreach, and Inclusion to Enhance the US Workforce in Accelerator Science and Engineering

We summarize the community-based consensus for improvements concerning education, public outreach, and inclusion in Accelerator Science and Engineering that will enhance the workforce in the USA. The improvements identified reflect the product of discussions held within the 2021-2022 Snowmass community planning process by topical group AF1: Beam Physics and Accelerator Education within the Accelerator Frontier. Although the Snowmass process centers on high-energy physics, this document outlines required improvements for the entire U.S. accelerator science and engineering enterprise because education of those entering and in the field, outreach to the public, and inclusion are inextricably linked.

43 PARTICLE ACCELERATORS↗

Study on the Strategy for Final Disposal and Reuse of Decontamination Wastes after the Fukushima Daiichi NPP Accident - 20426

After the Fukushima Daiichi NPP accident, a large area of East Japan was contaminated by radio-Cs. To discuss the final disposal of the contaminated wastes off-site and the required development of related technologies, it is important to understand the volume and radiation concentration of the wastes after various treatments. For a scientific investigation purpose, a research committee has been formed by the Society for the Remediation of Radioactive Contamination in the Environment, which is independent from the Ministry of Environment. This committee's work is focused on discussing various scenarios for the final disposal considering a mass balance evaluation of the entire process, economic evaluations, and stakeholder consultation for final consensus. The current discussions of this committee are herein reported. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Alloy 709 Advanced Austenitic Stainless Steel

Provide material solutions to enabling design, construction, licensing and operation of advanced reactors Including Fast Reactors, Gas-cooled Reactors and Molten Salt Reactors (solid or liquid fuel) Could be of modular or micro designs, and from hundreds of MWe to kWe Provide technical bases needed for design and licensing of advanced reactor components Develop & validate improved high temperature design methodology Provide qualification data (to NQA-1 or equivalent) on structural materials Utilize consensus standards when appropriate (e.g., ASME, ASTM, etc.) Target resolution of issues needed for near to mid-term deployment of advanced reactors

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Workshop on Addressing Rigor and Reproducibility in Thermal, Heterogeneous Catalysis

Heterogeneous catalysis has long served as the bedrock of the manufacturing of energy carriers, fuels and chemicals, and various technologies for pollution abatement. The significant complexity and variability spanning the entire breadth of catalyst material properties, synthesis methods, characterization techniques, and evaluation procedures, has focused attention on the need to establish community-accepted best practices for ensuring high-quality, benchmarked, and reproducible data. In addition, increased societal urgency to transition to clean energy and reduce greenhouse gas concentrations has incentivized interdisciplinary, convergent, and translational approaches to catalysis research in recent years. Research engineers and scientists with expertise cutting broadly across materials science, chemical synthesis, interfacial science, spectroscopy, and methods of data science and computational simulation, all bring diverse and important perspectives to catalysis research, but often with little awareness of the complexity of catalytic systems, especially in their working environment. As has already occurred in other scientific fields, there has been growing recognition and consensus in the heterogeneous catalysis research community that mechanisms are needed to improve the rigor and reproducibility (R&R) of experimental measurements, to ensure alignment of the broader research community with a common core of best practices specific to the realization of high-quality catalysis research. Similarly, the field is moving rapidly toward computationally informed and data science-driven catalyst design, but the success of implementing such predictive tools hinges on model training and validation rooted in rigorously obtained and reproducible experimental data that are benchmarked to common specifications. As such, this workshop was convened to prepare a report summarizing best practices for reporting data and performing experiments that researchers can use to benchmark, validate, and reproduce data in specific sub-fields of thermal, heterogeneous catalysis. Additionally, we discussed recommendations for future actions that may improve R&R in this field. The workshop organizers and participants include a diverse range of catalysis researchers from various employment sectors (e.g., academia, industry, national laboratory), institutional mission and resources (e.g., PhD-granting research universities, non-PhD-granting teaching universities), career stage (e.g., early, mid and late-career), technical expertise, and demographic background. This diverse group was involved in the discussion of workshop agenda items, writing this report, and discussing possible future action items for the community to consider, which helped ensure that a broad range of perspectives were captured in the description of the problems at hand and the creation of actionable solutions that may be effectively adopted by the diverse practitioners in catalysis research. Importantly, this group of workshop participants also included very early career researchers (e.g., senior PhD students, postdoctoral scholars) who will become the next generation of scientific leaders in various sectors, thus capturing emerging perspectives of newcomers to the field to shape its future while positively impacting the development of its future workforce. We envision that this effort will help advance the field of catalysis science by improving the rigor and reproducibility of experimental data collected by current researchers and future newcomers to the field, which is of broad importance to health and vitality of any scientific discipline. Therefore, best practices identified in this endeavor for thermal heterogeneous catalysis can be translated to such efforts in other areas of catalysis and other scientific fields involving the study of materials, and vice versa. We also envision this to be an ongoing effort, with future workshops that are convened to discuss issues of rigor and reproducibility on technical topics that were unable to be covered in this workshop due to its scope limitations, and as emerging methods and materials become more prevalent in the research community.

36 MATERIALS SCIENCE↗

Solving the Grid Optimization Competition Challenge 3 Problem

The Grid Optimization Competition Challenge 3 Problem posed a multiperiod security-constrained unit commitment problem with base-case AC power flow. The problem formulation includes binary unit commitment decisions, nonlinear AC power flow and balance, dispatchable loads, and linearized contingency real power flow, among other features. This talk will present a modified consensus ADMM algorithm, which splits the problem into mixed-integer linear and nonlinear components, as a heuristic solution method for this large-scale mixed integer nonlinear program. We will present some computational results from the competition for our implementation and reflect on the challenges of participating the grid optimization competition.

AC power flow↗