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DOE OSTI2024

Assessing heterogeneity of patient and health system delay among TB in a population with internal migrants in China

Backgrounds The diagnostic delay of tuberculosis (TB) contributes to further transmission and impedes the implementation of the End TB Strategy. Therefore, we aimed to describe the characteristics of patient delay, health system delay, and total delay among TB patients in Shanghai, identify areas at high risk for delay, and explore the potential factors of long delay at individual and spatial levels. Method The study included TB patients among migrants and residents in Shanghai between January 2010 and December 2018. Patient and health system delays exceeding 14 days and total delays exceeding 28 days were defined as long delays. Time trends of long delays were evaluated by Joinpoint regression. Multivariable logistic regression analysis was employed to analyze influencing factors of long delays. Spatial analysis of delays was conducted using ArcGIS, and the hierarchical Bayesian spatial model was utilized to explore associated spatial factors. Results Overall, 61,050 TB patients were notified during the study period. Median patient, health system, and total delays were 12 days (IQR: 3–26), 9 days (IQR: 4–18), and 27 days (IQR: 15–43), respectively. Migrants, females, older adults, symptomatic visits to TB-designated facilities, and pathogen-positive were associated with longer patient delays, while pathogen-negative, active case findings and symptomatic visits to non-TB-designated facilities were associated with long health system delays (LHD). Spatial analysis revealed Chongming Island was a hotspot for patient delay, while western areas of Shanghai, with a high proportion of internal migrants and industrial parks, were at high risk for LHD. The application of rapid molecular diagnostic methods was associated with reduced health system delays. Conclusion Despite a relatively shorter diagnostic delay of TB than in the other regions in China, there was vital social-demographic and spatial heterogeneity in the occurrence of long delays in Shanghai. While the active case finding and rapid molecular diagnosis reduced the delay, novel targeted interventions are still required to address the challenges of TB diagnosis among both migrants and residents in this urban setting.

Sun, Ruoyao↗

DOE OSTI2024

Initial Development of Fusion Magnet Simulation Capabilities for Performance and Safety Evaluation Using the MOOSE Framework

Fusion energy holds the promise of being a transformative technology as a carbon-neutral, sustainable source of energy. Whole device modeling and the development of fusion digital twins will be increasingly important for emerging fusion device concepts at both national laboratories and within the commercial fusion industry. However, meeting the challenge of whole device modeling of fusion energy devices requires robust, multiphysics, multiscale modeling and simulation technologies capable of running on large-scale supercomputers. Detailed analysis of individual systems at-scale is also required to ensure safe and efficient operation as well as provide the safety basis for future device designs and licensing activities. In a tokamak, toroidal and poloidal magnets confine and shape the fusion plasma to promote the fusion reaction. High plasma temperatures and high magnetic field requirements in modern design concepts (leading to high amounts of energy stored within each magnet) impose electrical, thermal, and mechanical loads on the magnet components, which in turn impacts the safety considerations of the magnet and their supporting systems. Idaho National Laboratory (INL) has a history of working in this space, including development and benchmarking of the Magnetic System Circuitry Analysis Program (MSCAP) and Magnet Arcing (MAGARC) codes to study magnet quench events; notably, MAGARC was used to study quenching during the ITER Engineering Design Activity. However, these legacy codes and capabilities are not parallel and scalable, and new tools are required for future advances in this area, which leads to the INL-developed Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Developed originally for fission reactor systems under United States Department of Energy, Office of Nuclear Energy modeling and simulation programs, the MOOSE framework has been well-suited to multiscale, multiphysics modeling and simulation needs for nuclear systems. The framework is open-source, well-tested, under continuous development and deployment, and developed to a Nuclear Quality Assurance, Level 1 software quality standard. MOOSE has also been used in the fusion space previously in several projects: INL’s Tritium Migration Analysis Program, Version 8 (TMAP8) for tritium migration, UK Atomic Energy Authority’s A Unified Resource for OpenMC (fusion) Reactor Applications (AURORA) code for fusion thermo-mechanical and neutronics analysis, and Argonne National Laboratory’s Cardinal for high-fidelity computational fluid dynamics and neutronics. However, to model superconducting magnets, several MOOSE enhancements are required: additions to the current MOOSE electromagnetic capabilities, new material libraries for superconductors of interest (such as YBCO), as well as fusion-specific models for thermo-mechanics. This talk will discuss initial development activities to build these capabilities in MOOSE, focusing on initial validation and benchmarking activities. Proposed coupling workflows and future work to support the simulation of fusion magnets and magnet structural assemblies for performance and safety evaluation in MOOSE will also be discussed.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

ERAD: A Graph-Based Tool for Energy Resilience Analysis of Electric Distribution Systems

Understanding the impact of extreme events on people's ability to access energy is crucial for designing resilient energy systems. In the event of a disaster, damage to the electric system and related infrastructure (e.g., downed power lines, flooded equipment, hacked communication systems, damaged roads, etc.) can impact people's access to critical services, including not just electricity but also shelter, food, healthcare, and more. There is a key need to understand such impacts better and evaluate options to improve energy resilience. The Energy Resilience Analysis for Electric Distribution Systems (ERAD) tool is a free and open-source software package designed to help researchers and decision-makers analyze and improve energy system resilience.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Code Coverage Status of the ARC Code RCT

The Argonne Reactor Code (ARC) software system supports users in their fast reactor design goals by providing neutronic, thermal-hydraulic, and structural analysis capabilities. REBUS plays a pivotal role in the ARC system as the primary fuel cycle analysis capability for fast reactor problems. Over its 60 year history, ARC software usage with REBUS has been applied to numerous fast and thermal spectrum reactor analysis projects with good to excellent comparison against experiments. The RCT code is a later addition and uses the REBUS restart files to define its input. The RCT code was built to provide pin depletion details on EBR-II models and thus many features of RCT were specifically tailored to the needs of EBR-II models. Additional approximations were invoked which are likely only valid for the EBR-II reactor and the particular fuel management that was done for it. The purpose of the present work is to identify a set of test problems for RCT and assess the code coverage for those test problems. The goal is to document what parts of the existing RCT code are touched by the set of test problems and which are not. Because no detailed verification work has been done on RCT, the existing regression testing suite was chosen for the code coverage assessment. The code coverage analysis of RCT was performed with the Code Coverage Tool of the Intel Fortran compiler which requires modifications to the compilation of RCT. The detailed coverage tables are given for each part of RCT. As will be discussed and shown, some parts of the RCT capability that are known to be used by the EBR-II analysis work are not tested by the regression testing suite. These aspects should be resolved before major source code changes are taken for the RCT software. Because REBUS and DIF3D are not subroutines of RCT, the coverage changes in both of those codes is not altered by RCT. The same is true for all of the modules of DIF3D that are used by RCT such as SYSLIB and SEGLIB.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Design and Development of the Handling Frame Assembly Table for the National Ignition Facility Blast Shields

The Handling Frame Assembly Table is a custom designed piece of equipment developed to support the NIF Sustainment Project. The goal of the NIF Sustainment Project is to refurbish the National Ignition Facility (NIF), the world’s largest and most energetic laser, so it can continue advancing research in fusion energy, national security, and high energy density physics. A key aspect of this effort involves replacing all 1,728 blast shields on the NIF, which requires specialized equipment such as the Handling Frame Assembly Table to support the production of new blast shields. This project follows a systems engineering approach that guides the design sequence. This report documents the design process from initial concept through final design, including stakeholder analysis, requirements development, conceptual design, final design, and design validation. Future work will focus on building and commissioning the system.

42 ENGINEERING↗

DOE OSTI2026

Design and Development of the Handling Frame Assembly Table for the National Ignition Facility Blast Shields

42 ENGINEERING↗

DOE OSTI2021

On-Line Monitoring of Gas-Phase Molecular Iodine Using Raman and Fluorescence Spectroscopy Paired with Chemometric Analysis

Molten salt reactors (MSRs) have the potential to safely support green energy goals. However, licensing and deployment of these systems will be aided through development of new technology. This includes on-line monitoring tools for real-time compositional analysis. Of particular interest is quantifying iodine within reactor off-gas streams to support design and operational control of reactor off-gas treatment systems. Here we discuss the development of advanced Raman spectroscopy systems for the on-line analysis of I 2(g) within the gas phase. Signal response is explored with two Raman instruments utilizing a 532 nm and a 671 nm excitation source, as a function of I 2(g) pressure and temperature. Furthermore, the applicability of chemometric modeling for advanced analysis of data is explored. Raman spectroscopy paired with chemometric analysis is demonstrated to be a powerful route to analyzing I 2(g) composition within the gas phase, which lays the foundation for applications within molten salt reactor off-gas analysis and other significant chemical processes producing iodine species.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Computational analysis of deployable wind turbine systems in defense operational energy applications.

The U.S. military has been exploring pathways to reduce the logistical burden of fuel on virtually all their missions globally. Energy harvesting of local resources such as wind and solar can help increase the resilience and operational effectiveness of military units, especially at the most forward operating bases where the fuel logistics are most challenging. This report considers the potential benefits of wind energy provided by deployable wind turbines as measured by a reduction in fuel consumption and supply convoys to a hypothetical network of Army Infantry Brigade Combat Team bases. Two modeling and simulation tools are used to represent the bases and their operations and quantify the impacts of system design variables that include wind turbine technologies, battery storage, number of turbines, and wind resource quality. The System of Systems Analysis Toolkit Joint Operational Energy Model serves as a baseline scenario for comparison. The Hybrid Optimization of Multiple Energy Resources simulation tool is used to optimize a single base within the larger Joint Operational Energy Model. The results of both tools show that wind turbines can provide significant benefits to contingency bases in terms of reduced fuel use and number of convoy trips to resupply the base. The match between the turbine design and wind resource, which is statistically low across most of the global land area, is a critical design consideration. The addition of battery storage can enhance the benefits of wind turbines, especially in systems with more wind turbines and higher wind resources. Wind turbines may also provide additional benefits to other metrics such as resilience that may be important but not fully considered in the current analysis. ACKNOWLEDGEMENTS The authors would like to thank the following individuals for their helpful support, feedback and review to improve this report: U.S. Department of Energy Wind Energy Technologies Office, Patrick Gilman and Bret Barker; Idaho National Laboratory, Jake Gentle and Bradley Whipple; The National Renewable Energy Laboratory, Robert Preus and Tony Jimenez; Sandia National Laboratories, Alan Nanco, Dennis Anderson, and Hai Le. In addition, numerous discussions with military and industry stakeholders over the year were invaluable in focusing the efforts represented in this report.

17 WIND ENERGY↗

DOE OSTI2020

Lattice physics calculations using the embedded self-shielding method in polaris, Part II: Benchmark assessment

Polaris is a 2D multigroup lattice physics capability in the SCALE code system used for the analysis of light water reactor fuel designs. In this work, verification and validation results of Polaris in SCALE 6.2 are presented. The SCALE/KENO continuous-energy Monte Carlo code is used as a reference to compare the results of more than 2,000 combinations of pressurized water reactor and boiling water reactor lattice designs and state points. Radiochemical assay measurements were also used to assess the accuracy of depletion calculations. Results show that Polaris, with ENDF/B-VII.1 cross section library, produces accurate lattice physics data for core simulators.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Bridging knowledge gaps in liquid- and vapor-fed CO 2 electrolysis through active electrode area

Increased use of gas diffusion electrodes for CO 2 electroreduction widens the experimental phase space that was previously inaccessible using foil electrodes, raising fundamental questions over the impacts of key variables that translate between liquid- and vapor-fed CO 2 electrolysis systems. This work focuses on studying the interplay of current-potential profiles and electrochemically active surface area (ECSA) by implementing a Cu nanoflower catalyst morphology. The results show decreased overpotentials as much as 460 and 174 mV for foil and gas diffusion electrodes, respectively, while maintaining or improving multi-carbon product current density. Furthermore, these overpotential shifts and product activities normalized by ECSA lead to current-potential relationships akin to those of the Tafel description, which are found through a continuum model to be useful for describing the roughness dependence for both liquid- and vapor-fed systems. This analysis establishes a holistic approach for establishing catalyst design criteria to improve materials development for CO 2 .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Decoupling Power Quality Issues in Grid-Microgrid Network Using Microgrid Building Blocks

Microgrids are evolving as promising options to enhance reliability of the connected transmission and distribution systems. Traditional design and deployment of microgrids require significant engineering analysis. However, Microgrid Building Blocks (MBB), consisting of modular blocks that integrate seamlessly to form effective microgrids, are promising technologies to enable faster and broader adoption of microgrids. Back-to-Back converter placed at the point of common coupling of microgrid is an integral part of MBB. This paper presents applications of MBB to decouple power quality issues in grid-microgrid network serving power quality sensitive critical loads such as data centers, new grid-edge technologies such as vehicle-to-grid generation, and emergency condition loads such as electric vehicle charging loads during evacuation prior disaster events. Simulation results show that MBB effectively decouple the power quality issues across networks and allow network with low power quality to transfer high-power quality power to connected networks during emergency conditions.

Acharya, Samrat S. [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2020

Quantifying Load Uncertainty Using Real Smart Meter Data

As we get closer to customers in distribution systems, load stochasticity increases. In the past, due to lack of real-time data, the comprehensive knowledge of load behavior was limited, and simplistic assumptions had to be made for distribution system modeling and analysis, especially in the processes of network design and expansion. With the deployment of Advanced Metering Infrastructure (AMI), ample real-time smart meter data has become available to utilities. In this paper, using real hourly smart meter data, we have quantified load uncertainty in terms of average, maximum and maximum noncoincident demands on a daily basis, as well as load factor and diversity factor. These uncertainty metrics are examined for individual residential, commercial and industrial customers, as well as distribution transformers serving residential customers. This paper provides a benchmark on load uncertainty quantification for practicing engineers and researchers.

Bu, Fankun↗

DOE OSTI2022

Performance Analysis and Optimization for Scientific Data Workloads

Scientific data generated at experimental and observational facilities are increasingly being processed on large-scale compute systems. Most of the experimental data analysis workflows are not designed or implemented to run on large scale environments and take full advantage of HPC compute and storage resources. These applications are unlike the traditional tightly-coupled scientific applications and hence face significant performance and scalability challenges as the volume of data increases exponentially. In this paper, we conduct a performance and scalability analysis for experimental analysis applications and workflows operating on data from light sources. Our analysis detects and quantifies I/O performance, scalability and runtime bottlenecks for three data analysis applications that run on NERSC resources. Based on our analysis we propose and implement a set of optimizations that lead to reducing the amount of time spent on I/O operations by almost 90%.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Bayesian Analysis of TRISO Fuel: Quantifying Model Inadequacy, Incorporating Lower-Length-Scale Effects, and Developing Parallel Active Learning Capabilities

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel-cycle systems. This program has been providing engineering-scale support for the continued development of BISON, a high-fidelity, high-resolution fuel performance tool. Fuel behavior in nuclear reactors is governed by a complex network of mechanisms that interact with various other physics aspects in the reactor system. Any model developed to represent fuel behavior will likely be idealized, resulting in uncertainties when comparing their predictions against the observed data. In Fiscal Year (FY)-23, we initiated the Uncertainty Quantification (UQ) work by using Bayesian methods to establish a level of model trustworthiness and further improve it, with a particular emphasis on TRI-Structural isOtropic (TRISO) nuclear fuel. This year, we further expanded on that UQ work by investigating an approach to quantifying model inadequacy and accounting for lower-length scale (LLS) effects in TRISO silver (Ag) release modeling. Furthermore, we are implementing parallel active learning capabilities to reduce the computational cost (i.e., required computational resources and elapsed time) of performing UQ. Specifically, we utilized The Kennedy O’Hagan framework for Bayesian uncertainty quantification (KOH) to account for model inadequacy in TRISO Ag release predictions made by BISON. The KOH framework represents an improvement over the standard Bayesian framework used in FY-23. Explicitly accounting for model inadequacy in the Bayesian framework helps establish the level of experimental noise uncertainty in the Advanced Gas Reactor (AGR) data. We compared the inverse UQ results obtained from both the standard Bayesian and KOH frameworks in light of the AGR-2/3/4 data, and also compared the predictive UQ results obtained from these two frameworks in light of the AGR-1 data. Next, we investigated the impact of considering LLS effects in the Ag release simulations. We developed an expanded database of LLS simulated effective diffusivities for Ag, covering a wide range of microstructures and temperatures. Using this database, we developed a framework for incorporating LLS effects into the engineering-scale Ag release UQ. We developed both parametric and non-parametric approaches for bridging the length scales. We then investigated the inverse UQ results in light of the AGR-2/3/4 data and the predictive UQ results in light of the AGR-1 data, and compared the LLS-informed approach and the Arrhenius equation, which does not include microstructure information. Finally, we discussed implementing parallel active learning capabilities in the Multiphysics Object Oriented Simulation Environment (MOOSE)/BISON to reduce the computational cost (i.e., computational resources and elapsed time) of Bayesian UQ. For verification purposes, we first tested these new capabil ities on a species interaction problem. We then demonstrated them on the TRISO Ag release application, showing that parallel active learning capabilities can enhance the accuracy of UQ while also substantially reducing the computational cost in comparison to the reference methods developed in FY-23.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system’s composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO’s autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO’s search when used as a prior.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

PV High Wind User Test Facility

The creation of a PV high wind test facility at the NREL Flatirons Campus that will enable private and public research efforts aimed at storm hardening PV systems. The facility will be instrumented to monitor effects of high wind conditions on modules and other array components. These in-field tests will coordinate with flow models, wind tunnel testing, validation of PV aeroelastic design codes, and post-testing module and system component analysis.

14 SOLAR ENERGY↗

DOE OSTI2022

Fast Modular Reactor Nuclear Design Parameters of Fuel Cycle and Power Distributions

This paper describes conceptual design of a 112 MW thermal (50 MW electric) Fast Modular Reactor (FMR) system operating at 7 MPa with inlet/outlet temperatures of 509/800 ºC. The reactor system includes the reactor core, fuel assemblies, fuel rods, reactor internals, reflector, neutron control system, flow control, and structural components. The nuclear design and analysis were conducted to search for a baseline core with a cycle length greater than 8-year and the power peaking factor less than 1.5 by adjusting the fuel assembly, reactor core, and reflector configurations. The neutronics calculations of the baseline core showed that a refueling interval of 9-year is achieved with a total peaking factor of 1.47 and a fuel rod average linear power of 3.6 kW/m.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Fast modular reactor nuclear design parameters of fuel cycle and power distributions

This paper describes conceptual design of a 112 MW thermal (50 MW electric) Fast Modular Reactor (FMR) system operating at 7 MPa with inlet/outlet temperatures of 509/800 Celsius degrees. The reactor system includes the reactor core, fuel assemblies, fuel rods, reactor internals, reflector, neutron control system, flow control, and structural components. The nuclear design and analysis were conducted to search for a baseline core with a cycle length greater than 8-year and the power peaking factor less than 1.5 by adjusting the fuel assembly, reactor core, and reflector configurations. The neutron transport calculations of the baseline core showed that a refueling interval of 9-year is achieved with a total peaking factor of 1.47 and a fuel rod average linear power of 3.6 kW/m. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗