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DOE OSTI2022

An Analysis of Grid Operator Survey Responses: Inexperience, Workload and Fatigue in the Control Room

Although a wide array of tools and technologies have been developed over the last decade to support power grid operators, deployment of these tools has been less successful. One reason for unsuccessful deployment may be an inadequate understanding of the factors that contribute to operator error in the control room. An analysis of operators’ current vulnerabilities may provide the baseline understanding needed to inform new technology integration. In an attempt to learn more about these vulnerabilities and their perceived impact on human error we collected and analyzed survey data from 20 electric grid control room operators. We asked survey respondents to consider the various operator, technology and interaction vulnerabilities that may arise during work in the control room and record their attitudes and experiences toward each. Results suggest operator inexperience, high mental workload and fatigue are the most common vulnerabilities experienced during a shift. Survey results were analyzed to explore these vulnerabilities in greater depth.

Inexperience, Workload, Fatigue↗

DOE OSTI2021

CMDV-RRM: Representation of Clouds and Convection Across Scales in E3SM (Final Report)

We have made three accomplishments as a participant of the LLNL-led CMDV-RRM project. The first is the derivation of 3D high-resolution atmospheric variational analysis (VAR) at 2 x 2 km resolution by integrating ARM data and operational analysis for a domain of approximately 450 x 400 km centered at the ARM SGP facility for several MC3E convective events and used the analyzed data to evaluate E3SM RRM and as forcing data for E3SM SCM. The second accomplishment is the demonstration of elevated maximum moist static energy during the night over the ARM SGP that was not accounted for in the default E3SM convection scheme. The third accomplishment is the explanation of the cause of the dry and warm biases over the SGP common to many climate models by using ARM data. We found that the precipitation deficit associated with lack of strong rainfall events in the summer is the main cause of the warm bias over the SGP.

42 ENGINEERING↗

DOE OSTI2026

Feasibility Analyses for the Microgrid of the Mountain in the Cordillera Central Region of Puerto Rico

Distributed energy resource (DER) development can benefit communities through improvement of access to reliable electricity and a resilient energy system. Many place-specific considerations must be accounted for when designing electrical systems with co-located generation and loads. This paper presents an overview of four studies focusing on different aspects for regional DER development in the Cordillera Central region of Puerto Rico, including a grid and load stability study with the introduction of solar energy generation, microgrid design based on the solar resource in the area, resilience considerations during power outage scenarios, and qualitative risk aspects for components in the microgrid. Additionally, a conceptual substation microgrid analysis is conducted using the Microgrid Design Toolkit (MDT) to evaluate trade-offs between cost and energy availability, providing insights into optimal design strategies. A microgrid resilience assessment is performed using the Resilient Nodal Cluster Analysis Tool (ReNCAT) to identify critical loads and evaluates their accessibility during outages, emphasizing the importance of community needs. Finally, a qualitative risk assessment utilizing Hazard and Operability Analysis (HAZOP) methods highlights potential hazards and mitigations for microgrid components, ensuring robust system design and operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Methods for estimating a property of a hydrocarbon

In at least one embodiment, the inventive technology relates to in-vessel generation of a material from a solution of interest as part of a processing and/or analysis operation. Preferred embodiments of the in-vessel material generation (e.g., in-vessel solid material generation) include precipitation; in certain embodiments, analysis and/or processing of the solution of interest may include dissolution of the material, perhaps as part of a successive dissolution protocol using solvents of increasing ability to dissolve. Applications include, but are by no means limited to estimation of a coking onset and solution (e.g., oil) fractionating.

Schabron, John F.↗

DOE OSTI2024

xCDAT: A Python Package for Simple and Robust Analysis of Climate Data

xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray (Hoyer & Hamman, 2017) for climate data analysis on structured grids. xCDAT streamlines analysis of climate data by exposing common climate analysis operations through a set of straightforward APIs. Some of xCDAT’s key features include spatial averaging, temporal averaging, and regridding. These features are inspired by the Community Data Analysis Tools (CDAT) library (Dean N. Williams et al., 2009) (D. N. Williams, 2014) (Doutriaux et al., 2019) and leverage powerful packages in the Xarray ecosystem including xESMF (Zhuang et al., 2023), xgcm (Abernathey et al., 2022), and CF xarray (Cherian et al., 2023). To ensure general compatibility across various climate models, xCDAT operates on datasets that are compliant with the Climate and Forecast (CF) metadata conventions (Hassell et al., 2017).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Electrification Futures Study: Power Systems Operation with Newly Electrified and Flexible Loads

The Electrification Futures Study (EFS) is a multi-year study designed to analyze the impacts of widespread electrification in the U.S. energy system. In this webinar we present the detailed power systems operational analysis from the final report in the EFS series. For this analysis, we simulate multiple 2050 power systems for the conterminous United States to assess how variations in the magnitude and shape of electricity demand driven by electrification, and the extent of load participation to more-actively provide grid services, might impact the hourly operation, operational costs, and emissions of various power systems in 2050. The impacts of electrification and demand flexibility are overlayed across systems with significantly greater penetrations of variable renewable energy than today. Overall, we find that the high electrification scenarios envisioned in the EFS with significant VRE penetration (66% of annual national generation) can be operated to meet future increased levels of electrified demand. We also find that demand-side flexibility can enhance operational efficiency and reduce overall annual production costs by $5-$10 billion. The complementary relationship between flexible electric vehicle charging loads and solar generation is particularly pronounced, but in the absence of demand-side flexibility electrification can lead to increased wind and solar curtailment. By helping to reduce renewable curtailment, flexibility can also reduce power sector CO2 emissions. The analysis highlights the value of increased integration and coordination of demand- and supply-side resources in future electric system planning and operations—particularly under high electrification futures.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)

Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as-built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data-driven thermodynamics approach, GOOML is able to accurately model the real-world performance characteristics of as-built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state-of-the-art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Organizing Large Data Sets for Efficient Analyses on HPC Systems

Upcoming exascale applications could introduce significant data management challenges due to their large sizes, dynamic work distribution, and involvement of accelerators such as graphical processing units, GPUs. In this work, we explore the performance of reading and writing operations involving one such scientific application on two different supercomputers. Our tests showed that the Adaptable Input and Output System, ADIOS, was able to achieve speeds over 1TB/s, a significant fraction of the peak I/O performance on Summit. We also demonstrated the querying functionality in ADIOS could effectively support common selective data analysis operations, such as conditional histograms. In tests, this query mechanism was able to reduce the execution time by a factor of five. More importantly, ADIOS data management framework allows us to achieve these performance improvements with only a minimal amount of coding effort.

Gu, Junmin↗

DOE OSTI2025

Radsource Mr: Mixed Reality Planning Tool For Radioactive Recovery

The RadSource MR system leverages Meta Quest 3's advanced mixed reality capabilities to create a comprehensive spatial planning platform for end-of-life sealed radioactive source recovery operations. The application utilizes the Quest 3's high-resolution passthrough cameras and spatial mapping algorithms to generate accurate 3D environmental models. Core technical components include: (1) Real-time spatial measurement algorithms calculating distances, angles, slopes, and surface areas with sub-centimeter accuracy; (2) Virtual object placement system allowing users to position digital representations of recovery equipment (trailers, containment vessels, protective barriers) within the real environment; (3) Voice recording and annotation system for hands-free documentation in protective equipment; (4) 3D mesh capture and storage capabilities for post-operation analysis and regulatory documentation. (5) Procedure documentation is available for viewing in Mixed Reality, providing an innovative and convenient way to access the information during pre-visit and inspection activities. (6) Support for screen capture for the view for real world and virtual objects together to use it later for planning. The system integrates computer vision techniques for environmental understanding, spatial mathematics for precise measurements, and human-computer interaction principles optimized for hazardous environment operations. Data persistence allows teams to save and share planning sessions across multiple stakeholders while maintaining operational security requirements.

Khadka, Rajiv [Idaho National Laboratory (INL), Id↗

DOE OSTI2025

Preliminary Look at the LBEG & MPEG Beam Transmissions between HPSim and Operation Data

This report summarizes recent work on estimating the transmission of LANSCE H- beams from the end of the present DTL to both the PSR stripper foil (LBEG) and WNR target 4 (MPEG). The LBEG beam might be considered a more typical LINAC beam and lends itself to continuous monitoring of transmission through the various stages of the accelerator. For the MPEG, however, the widely-spaced micropulses and lack of the requisite sensitivity current monitors throughout the accelerator make these measurements extremely difficult and more uncertain. Therefore, to assist in estimating the MPEG beam transmission, beam-dynamics simulations using HPSim were employed. These beam transmission estimates from the end of the Drift Tube Linac (DTL) to Target 4 (MPEG) and the PSR stripper foil (LBEG) are vital to determine the charge requirements for LAMP’s front end. In this technote, we demonstrate three major efforts in determining the transmission: (1) Convert the WNR beamline lattice from TRANSPORT to HPSim for use in the simulation; (2) Simulate the optimization process in the Central Control Room (CCR) that brings down the losses by up to a factor of 4000 between the end of the initial physics-based tuneup phase and production beam operation; (3) Analyze operational data to deduce measured transmissions. Table 1 shows the estimated losses with HPSim and operational analysis. Finally, a better measurement and other improvements to refine the results are also proposed.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Summer 2024 INL Intern Poster Session Submission - Brian Schumitz

This LRS submission is my poster for the INL Intern Poster Session, Summer 2024. Abstract: The Software Engineering and Cybersecurity Lab (SECL) at Montana State University has developed PIQUE, a system for evaluating software quality. PIQUE's adaptability allows for language-specific static-analysis operations, including a model for assessing cloud microservice ecosystems. These ecosystems often rely on Docker for efficient deployment and management of containerized services. Our research focuses on evaluating the network quality within these microservice ecosystems. To automate this process, we're utilizing Snort, an open-source intrusion detection system renowned for its ability to detect and log network traffic. By leveraging Snort's customizable rules, we aim to construct comprehensive testing methods for measuring and quantifying the network quality based on traffic between Docker containers. This research aims to enhance the overall security and reliability of cloud microservice ecosystems by providing automated and robust quality evaluation mechanisms, ultimately contributing to the advancement of software engineering practices in these environments

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Tune compensation in nearly scaling fixed field alternating gradient accelerators

In this paper, we investigate the stability of the particle trajectories in fixed field alternating gradient accelerators (FFAs) in the presence of field errors. The emphasis is on the scaling radial sector FFA type: A collaboration work is ongoing in view of better understanding the properties of the 150 MeV scaling FFA at Kyoto University Institute for Integrated Radiation and Nuclear Science in Japan and progress toward high-intensity operation. Analysis of certain types of field imperfections revealed some interesting features that required the development of an analytical model based on the scalloping angle of the orbits. This helped explain some of the experimental results as well as generalize the concept of a scaling FFA to a nonscaling one for which the tune variations obey a well-defined law. Based on this, a compensation scheme of tune variations in imperfect scaling FFAs is presented. This is the cornerstone of a novel concept of a fixed tune FFA in which the scaling is not achieved at every azimuthal position of the ring but rather in an average sense.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

A Discussion of Strength Reduction Factor Development for Thermal Aging Effect on Nuclear Structural Alloys

In consideration of structural alloy property deterioration during long-term exposure to elevated temperatures, the yield and ultimate tensile strength reduction factors are provided in the Boiler and Pressure Vessel Code Section III for nuclear reactor component design and operation analysis. Because the Gen IV reactor requirement of 40 ~ 60 years of service life makes it difficult to acquire such long exposure test data for developing the reduction factors, they must be derived from test data with relatively short exposure by predictive methods considered to be reasonably reliable. A novel approach with a physically-based model has recently been proposed for application to development of reduction factors for 9Cr-1Mo-V. In the model, contributors to the tensile strength are first identified and related to definite microstructural features of the alloy, then some physically-based methods are employed to simulate the microstructural evolution, and finally the model is assembled with test-data-calibrated parameters to generate the yield and ultimate tensile strength reduction factors covering elevated temperature exposure for up to 57 years. The approach is undoubtedly a trailblazing development that will, if proven reliable, lead to a paradigm shift in predicting thermal aging behavior of many other alloys. Its debut application to Section III, however, concerns nuclear safety and naturally warrants objective, impartial, and thorough technical scrutiny. In the present paper, the novel and conventional approaches are discussed. Necessary improvements to the novel approach are recommended for its application to nuclear structural component design and analysis, and for its potential expanded use to other alloys.

Ren, Weiju↗

DOE OSTI2021

DASSA v0.0.1

DASSA (Parallel DAS Data Storage and Analysis) provides a data storage engine and analysis engine for data from distributed acoustic sensing and other methods. It supports various data analysis operations, from FFT , signal filter, cross-correlation, compression, stacking etc.

Dong, Bin↗

DOE OSTI2025

High-burnup boiling water reactor steady-state operating conditions and fuel performance analysis

The primary operational costs for existing nuclear reactors are plant operation costs, maintenance costs, and fuel costs, all of which are influenced by the materials used and the design of the reactor core. Optimizing core design parameters—including burnup limits and enrichment levels—can lengthen cycles, reduce outages, reduce reload batch fractions and spent fuel storage requirements, and lower maintenance and operating expenses, thereby enhancing economic viability. Furthermore, developing higher-fidelity tools to simulate these parameters enables better identification of the available margin, improves overall plant safety, and improves the understanding a given plant’s responses to accident scenarios. Here, in the US, much of the research and development focus has traditionally been on pressurized water reactors (PWRs), but boiling water reactors (BWRs) comprise approximately one-third of the US reactor fleet. Modeling and simulation advances for BWRs and PWRs—particularly those achieved through the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program—are crucial to the long-term viability of the light–water reactor industry. A key research area of the high burnup and increased enriched fuel initiative is focused on addressing issues related to postulated loss-of-coolant accident (LOCA) scenarios. NEAMS has dedicated significant effort to enhancing tools to better support BWRs. A current focus is showcasing the BWR framework for high-burnup LOCA analysis. This high-fidelity steady-state analysis is a first step toward demonstrating a best-estimate, pin-by-pin high-burnup BWR LOCA analysis to assess full-core cladding rupture behavior for a representative BWR. The objective of this effort is to provide a modeling capability that will help elucidate and provide a best-estimate evaluation for cladding rupture susceptibility in BWRs. This modeling capability could then be used to prevent and/or mitigate cladding ruptures in postulated accident scenarios without penalizing operational parameters. Additionally, the results of this work will help identify strategies for finding additional margins or potentially limiting cladding ruptures through core design optimizations to enable more efficient core designs.

Capps, Nathan [Oak Ridge National Laboratory (ORNL↗

DOE OSTI2022

Human Performance Analysis Depending on Operator Expertise (Student vs. Operator) and Simulator Complexity (Rancor Microworld vs. Compact Nuclear Simulator)

Human reliability analysis (HRA) evaluates human errors and provides human error probabilities (HEPs) for application in probabilistic safety assessment (PSA), which is a comprehensive safety assessment method for nuclear power plants (NPPs). Generally, HRA methods estimate HEPs based on human reliability data collected from actual historical measurement, simulator experiments, or expert judgement. Most recent HRA data collection studies focus on collecting data via full-scope main control room (MCR) simulators with actual licensed reactor operators. Contrary to this, Idaho National Laboratory (INL) has adopted a different approach, which attempts to collect HRA data based on experiment using simplified simulators and student participants by following the Simplified Human Error Experimental Program (SHEEP). This approach has a couple of advantages compared to full-scope data collection. Representatively, it has relatively low entry point for collecting HRA data, and secures large sample sizes with reasonable cost and labor. In the previous studies, we developed the SHEEP framework, then verified whether the data collected through the framework could support a representative full-scope data collection study, i.e., the Human Reliability Data Extraction (HuREX) study. Also, we analyzed human performance measurements depending on participant type (i.e., student vs. operator). In this paper, we analyze human performance data collected from an experiment comparing operator expertise and simulator complexity when using the more simplified simulator developed by INL, i.e., Rancor Microworld and the less simplified simulator, i.e., Compact Nuclear Simulator (CNS) developed by Korea Atomic Energy Research Institute (KAERI). Analysis of variance (ANOVA) tests and correlation analysis are used for analyzing the experimental data.

99 GENERAL AND MISCELLANEOUS↗