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These are slides which are part of the ASC L2 Milestone Review. The following topics are covered: Legion Backend, Distributed-Memory Partitioning, Sparse Data Representations, and MPI-Legion Interoperability.
The primary objective of this project was to improve our ability to predict performance of an Enhanced Geothermal System (EGS) reservoir over time by relating, in a quantitative manner, microseismic imaging with fluid and temperature changes within the reservoir. Historically, microseismic data have been used qualitatively to place bounds on the growth of EGS reservoirs created by large hydraulic fracturing experiments. Previous investigators used an experimentally based fracture opening relationship (fracture aperture as a function of pressure), the spatial extent of microseismic events, and some assumptions about fracture frequency to determine the size of an EGS reservoir created during large pumping tests. We addressed a number of issues (1) locating microearthquakes that occur during hydraulic fracturing, (2) obtaining more information about a reservoir than the microearthquake locations from the microearthquake data, for example, information about the seismic velocity structure of the reservoir or the scattering of seismic waves within the reservoir, (3) developing an improved methodology for estimating properties of fractures that intersect wellbores in a reservoir, and (4) developing a conceptual model for explaining the downward growth of observed seismicity that accompanies some hydraulic injections into geothermal reservoirs. We used two primary microseismic datasets for our work. The work was motivated by a dataset from the Salak Geothermal Field in Indonesia where seismicity accompanying a hydraulic injection was observed to migrate downward. We also used data from the Soultz EGS site in France. We also used Vertical Seismic Profiling data from a well in the United States. The work conducted is of benefit for characterizing reservoirs that are created by hydraulic fracturing for both EGS and for petroleum recovery.
This report describes the basic aspects of control room modernization projects in the U.S. nuclear industry and the need for supplementary guidance on the integration of human factors considerations into the licensing and regulatory aspects of digital upgrades. The report pays specific attention to the integration of principles described in NUREG-0711 (Human Factors Engineering Program Review Model) and how supplementary guidance can help to raise general awareness in the industry regarding the complexities of control room modernization projects created by many interdependent regulations, standards and guidelines. The report also describes how human factors engineering principles and methods provided by various resources and international standards can help in navigating through the process of licensing digital upgrades. In particular, the integration of human factors engineering guidance and requirements into the process of licensing digital upgrades can help reduce uncertainty related to development of technical bases for digital upgrades that will avoid the introduction of new failure modes.
This report describes the basic aspects of control room modernization projects in the U.S. nuclear industry and the need for supplementary guidance on the integration of human factors considerations into the licensing and regulatory aspects of digital upgrades. The report pays specific attention to the integration of principles described in NUREG-0711 (Human Factors Engineering Program Review Model) and how supplementary guidance can help to raise general awareness in the industry regarding the complexities of control room modernization projects created by many interdependent regulations, standards and guidelines. The report also describes how human factors engineering principles and methods provided by various resources and international standards can help in navigating through the process of licensing digital upgrades. In particular, the integration of human factors engineering guidance and requirements into the process of licensing digital upgrades can help reduce uncertainty related to development of technical bases for digital upgrades that will avoid the introduction of new failure modes.
This progress report (Level 4 Milestone Number M4SF-19LL010301042) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite Activity Number SF-19LL010301041. LLNL is leading efforts in the development of thermodynamic databases in support of the Spent Fuel and Waste and Science Technology (SFWST) program. Thermodynamic models provide the basis for understanding the stability of solid phases and speciation of aqueous species and modeling the evolution of repository conditions. The LLNL effort is being performed in coordination with other US database development efforts. The effort includes a review of available thermochemical databases and a path forward for database integration. International coordination with the NEA-TDB is supported through crystalline international work package
Learning how to achieve sustained operation at high plasma pressure is an important goal of the National Spherical Torus Upgrade (NSTX-U) program and is critically important to any future fusion power plant. This research addresses high priority stability and control issues outlined by the NSTX-U Program. Reduced models of the stabilization physics learned continue to be created and tested against actual plasma operation in NSTX, NSTX-U, and other tokamak devices. This research is forming physics-based models for disruption forecasting that aims to unify results across different fusion machines for more confident extrapolation of the techniques studied to future devices including ITER. The Disruption Event Characterization and Forecasting (DECAF) code was expanded to analyze data from the TCV tokamak, including real-time MHD analysis signals. The presence of a mode and likelihood of its m/n numbers can be tracked, as well as the mode frequency, although a limitation at low frequency does not yet allow tracking modes to the locking point in TCV. Initial analyses of NSTX-U operation were conducted using the DECAF code. The timing of sawtooth oscillations was investigated taking advantage of the more extensive L-mode operation of the device in its first run campaign. An initial investigation of thermal collapses in NSTX-U was also conducted with specific comparison to the Greenwald density limit. A local island power balance model to explain the density limit in tokamaks has been tested with NSTX data and is being evaluated as a potential disruption forecaster in the DECAF code, presently showing similarities to the global Greenwald density evolution and limit. A module was written for the DECAF code that generalizes magnetic spectrogram analysis of MHD modes. This new portable code, written in Python, discriminates the toroidal mode number of instabilities and can track each mode as a DECAF event, monitoring the evolution of the mode through bifurcation and locking points. This is a critical new capability to allow disruption event characterization for general tokamak databases.
We derive the integral expression for the reflected solar flux F (W/m 2 ) seen by a sensor deployed on a satellite circling the earth at elevation h t . The sensor’s responsivity is that of a Si photodiode, (0.3, 1.1) µm. We first ignore atmospheric absorption, but account for light emanating from all angles, zenith up to parallel to earth surface. The expression, which holds for all “Solar–Earth–Sensor” (elevation) angles φ e , corrects a long-used equation in the USNDS program, that models “background irradiance,” Ramsey [3], Eq.(2.2). Flux striking the sensor is proportional to the sea-level solar radiance I 0 and the average earth albedo A. Results are displayed for h t = 2.02 · 10 4 km. The integral varies with angle φ e , i.e., F = F(φ e ). When sensor and sun are aligned with earth center, F = F(0) = 0.620 I 0 AR 2 , where R = r e /(r e + h t ) and r e is the average earth radius. For the specified h t , |φ e | < 180 - θ 0 deg, where θ 0 = 13.90. For larger angles, F = 0 since the sensor cannot see any part of earth illuminated by the sun. The flux F(φ e ) decays as ϵ 3.5 when φ e = 180-θ 0 - ϵ and ϵ→ 0, i.e., as the sensor enters darkness. Our ϵ 3.5 result differs from Ramsey’s [3], Eq.(2.2), which has ϵ 4.3 . We conclude Eq.(2.2) is incorrect for two reasons: the erroneous decay and, more importantly, that it vanishes only at φ e = 180. Our result, Eq.(14), presents a simple approximation to F(φ e ), accurate to better than 0.5%. The approximation blends Ramsey’s expression for small φ e with an ϵ 3.5 decay for large angles. Assuming a solar radiance I 0 = 1120, W/m 2 Wikipedia [7], and albedo A = 0.3, NASA, yields F(0) = 3.8 W/m 2 , which is 80% less than the leading term in Ramsey [3], Eq.(2.2). If atmospheric absorption is not ignored our integrand is modified by the factor exp(-τ N atm ), where τ is the atmosphere’s optical depth and N atm accounts for slant path absorption. The result is spectrally dependent. Absorption significantly decreases the flux as φ e → 180 - θ 0 .
Due to increasing economic competition from renewables and combined-cycle natural gas plants, nuclear power plants are looking toward flexible operations to enhance their cost competitiveness. The Integrated Energy Systems project under the Light Water Reactor Sustainability program of the U.S. Department of Energy focuses on joint electricity-hydrogen flexible operations. Joint electricity-hydrogen flexible operations entail the nuclear power plant diverting thermal energy via main steam to a hydrogen production plant located nearby. The steam serves to enhance the efficiency of the hydrogen production. Furthermore, high temperature electrolysis requires a large amount of electricity, which the plant can also provide. The plant provides steam and electricity to the hydrogen plant throughout the day, but during peak demand hours the nuclear power plant returns to solely providing electricity to meet the high demand. Through this flexible concept of operations, the plant can optimize the thermal energy it produces without having to maneuver the power of the plant. This report documents the human factors process to design and develop a prototype human-system interface for the steam extraction loop that serves as the conduit between the nuclear power plant and the adjacent hydrogen plant. The design process followed the human factors guidelines set by NUREG-0711, Human Factors Engineering Program Review Model (O’Hara, Higgins, & Fleger, 2012), and expanded upon by the Guideline for Operational Nuclear Usability and Knowledge Elicitation (GONUKE; Boring, Ulrich, Joe, & Lew, 2015; Boring, Lew, & Ulrich, 2016). The design process entailed operator interviews to determine the concept of operations for the steam extraction loop, a review and adaptation of digital interface design concepts developed by the team in prior projects, an iterative design process, and reviews conducted by both operators and human factors experts. Several versions of the prototype human-system interface were developed. Operators were interviewed to determine what design features they found useful and would like to see in the interface. The design underwent a review by human factors experts against NUREG-0700, Human Interface Design Review Guidelines (U.S. Nuclear Regulatory Commission, 2019), to ensure compliance with the latest human factors standards for digital interfaces in nuclear applications. The design was then prototyped as a functional windows-based application integrated with the Generic Pressurized Water Reactor simulator modified to include the steam extraction loop. The simulation is supported by the Human Systems Simulation Laboratory at Idaho National Laboratory, which supports operator-in-the-loop testing. This is an ongoing project and the next phase of the project entails performing an operator-in-the-loop usability study to evaluate the interface and examine the proposed concept of operations to extraction steam from the nuclear power plant for delivery to the coupled hydrogen production plant.
This annual report describes the activities of PCMDI – An Earth System Model Evaluation Project for the period November 1, 2019 – June 30, 2020. PCMDI is a project at Lawrence Livermore National Laboratory that is funded by the Climate and Environmental Sciences Division (CESD) of the Department of Energy. PCMDI is funded by two efforts within CESD: The Regional and Global Model Analysis program area (Dr. Renu Joseph, program manager) and the Atmospheric Systems Research program (Dr. Shaima Nasiri and Jeffrey Stehr, program managers). As instructed by Drs. Joseph, Nasiri, and Stehr, we provide an annual report addressing sections 4 (Progress and Plans) and 5 (Staffing and Budget Summary) of the SFA Program Management and Performance Reporting Criteria.
Microreactors are small mobile transportable nuclear reactors with thermal power less than 20 MWt that are designed to meet the needs of remote areas, military installations, emergency operations, and disaster relief zones. These reactor are concepts that are intended to be factory-manufacturable, easily transportable, and will allow for semi- or full-autonomous operation. To assist with development of microreactors, modeling and experimentation need to be performed with verification and validation to gain confidence in the models and tools. The objective of this study is apply advanced modeling and simulation capabilities being developed by the DOE Office of Nuclear Energy Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to perform integrated modeling and simulation capability for multiphysics simulation of the microreactor concepts. This activity will provide an opportunity to exercise the codes to provide both gain experience and to provide feedback to the developers.
The U.S. Nuclear Regulatory Commission (NRC) staff reviews the human factors engineering (HFE) aspects of nuclear power plants in accordance with the Standard Review Plan (NUREG-0800, Standard Review Plan for the Review of Safety Analysis Reports for Nuclear Power Plants: LWR Edition). The Human Factors Engineering Program Review Model (NUREG-0711, Revision 3, issued November 2012) contains detailed design review procedures. As part of the review process, the interfaces between plant personnel and the plant's systems and components are evaluated for conformance with HFE guidelines. This document, Human-System Interface Design Review Guidelines (NUREG-0700, Revision 3), provides the guidelines necessary to perform this evaluation. The review guidelines address the physical and functional characteristics of human-system interfaces (HSIs). Because these guidelines only address the HFE aspects of design and not other related considerations, such as instrumentation and control and structural design, they are referred to as HFE guidelines. In addition to the review of actual HSIs, the NRC staff can use the NUREG-0700 guidelines to evaluate a design-specific HFE guidelines document or style guide. The HFE guidelines are organized into four basic parts, which are divided into sections. Part I contains guidelines for the basic HSI elements: information displays, user-interface interaction and management, and analog displays and controls. These elements are used as building blocks to develop HSI systems to serve specific functions. Part II contains the guidelines for reviewing the following HSI systems: alarm system, safety parameter display system, group-view display system, soft control system, computer-based procedure system, automation system, and communication system. Part III provides guidelines for the review of workstations and workplaces. Part IV provides guidelines for the review of HSI support (i.e., maintainability of digital systems and degraded HSI and instrumentation and control conditions).
This progress report (Level 4 Milestone Number M4SF-20LL010301042) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite Activity Number SF-20LL010301041. LLNL is leading efforts in the development of thermodynamic databases in support of the Spent Fuel and Waste Science Technology (SFWST) program. Thermodynamic models provide the basis for understanding the stability of solid phases and speciation of aqueous species and modeling the evolution of repository conditions. The LLNL effort is being performed in coordination with other US database development efforts. The effort includes a review of available thermochemical databases and a path forward for database integration. International coordination with the NEA-TDB is supported through crystalline international work package.
This report presents the results of a collaborative effort under the Verification, Validation, and Uncertainty Quantification (VVUQ) thrust area of the North American Energy Resilience Model (NAERM) program. The goal of the effort described in this report was to integrate the Dakota software with the NAERM software framework to demonstrate sensitivity analysis of a co-simulation for NAERM.
Focal Area(s): Focal Area 2: AI for predictive modeling, including AI-derived model components, and AI-enabled applications including parameter optimization, data assimilation, and uncertainty quantification. Focal Area 1: AI-assisted data assimilation using numerical Earth system models. Science Challenge: Earth system models have structural biases that lead to uncertain predictions, and their complexity and expense makes it difficult constraining the models with data or improved physical understanding.
This report summarizes work done under the Verification, Validation, and Uncertainty Quantification (VVUQ) thrust area of the North American Energy Resilience Model (NAERM) Program. The specific task of interest described in this report is focused on sensitivity analysis of scenarios involving failures of both wind turbines and thermal generators under extreme cold-weather temperature conditions as would be observed in a Polar Vortex event.
This progress report (Level 4 Milestone Number M4SF-21LL010301052) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite Activity Number SF-21LL01030105. LLNL is leading efforts in the development of thermodynamic databases in support of the Spent Fuel and Waste Science Technology (SFWST) program. Thermodynamic models provide the basis for understanding the stability of solid phases and speciation of aqueous species and modeling the evolution of repository conditions. The LLNL effort is being performed in coordination with other US database development efforts. The effort includes a review of available thermochemical databases and a path forward for database integration. International coordination with the NEA-TDB is supported through crystalline international work package.
The primary purpose of this report is to provide an overview of the advancement in artificial intelligence and machine learning (AI/ML) technologies and their applications in power systems. It offers a foundation for understanding the transformative role of AI/ML in power systems and aims to stimulate further research and development in this area. This report begins with a historical perspective of AI/ML technologies, then explores their advancement to today’s prominence. The document highlights key contributors to the success of AI/ML technologies, including increased computational power, greater data availability, innovative algorithms, and advanced tools. It further introduces various AI/ML techniques, including supervised, unsupervised and reinforcement learning, graph neural networks, and generative AI. It also emphasizes the critical importance of ensuring the safety, security, and trustworthiness of these AI/ML techniques within this sector. The report reviews the recent representative advancements in various power system applications enhanced by AI/ML techniques, underscoring key developments and their transformative impact as evidenced by numerous studies. It also explores both the opportunities and challenges associated with the application of AI/ML technologies to improve power system applications. While the report extensively covers AI/ML applications in power systems, focusing primarily on the technical and operational aspects, it may not thoroughly explore the sociopolitical, economic, and broader regulatory implications of AI/ML integration in power systems. AI/ML techniques hold significant potential for enhancing power system applications; however, they are not omnipotent. It is crucial to acknowledge their limitations and understand that they may not be able to address all challenges in the power system domain. Various factors must be considered that influence the implementation, adoption, and effectiveness of AI/ML solutions, including but not limited to safety, security, transparency, and trustworthiness. Additionally, the incorporation of advanced human–machine interfaces is essential, as it enables humans to validate the effectiveness of AI/ML solutions while remaining actively engaged, fostering trust in AI/ML deployment. Finally, the report summarizes AI/ML research activities supported by the Department of Energy (DOE) Office of Electricity (OE) through the Advanced Grid Modeling (AGM) program. The work aligns with the interests and mission of DOE-OE AGM, with the report serving as a resource for identifying existing progress and for pinpointing future applications within AI/ML that need further exploration and support.
This milestone report details the advancements made in fiscal year 2024 under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to improve the modeling of fission gas behavior in high burnup UO 2 nuclear fuel in the BISON fuel performance code. As nuclear fuel is pushed to higher burnups, significant microstructural changes occur within the fuel, including the formation of a high burnup structure (HBS) on the pellet rim and a dark zone deeper within the pellet. These regions, characterized by subgrain formation and increased pore densities, have critical implications for fission gas behavior and release, which are not well understood. The modeling capabilities in BISON did not adequately predict these phenomena, leading to an underestimation of fuel restructuring and - potentially - of fission gas release. To address these gaps, this milestone focused on three key objectives: (1) reviewing and assessing Sifgrs's capabilities for low burnup fuel, on which high burnup capabilities rely, (2) validating and expanding HBS fission gas modeling capabilities, including investigating mechanisms for fission gas release from HBS, and (3) expanding Sifgrs to enable modeling of dark zone formation and its effects on fission gas behavior. These objectives were achieved and are described herein. The achievements of this NEAMS milestone are significant for the industry's goal of burnup extension. The improved predictive modeling capabilities for both low- and high-burnup conditions enhance our understanding of fuel performance under both normal operations and transient scenarios. Although goals were reached, future work is necessary to validate these models against experimental data and quantify their accuracy in different conditions. In parallel, mechanistic modeling efforts should continue to extend and refine these capabilities to increase accuracy while reducing reliance on empirical models. This will ensure robust performance across a broader range of conditions.