Impact of cross section libraries on loading curves for burnup credit criticality safety analysis models
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Elzohery, Rabab↗
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Modeling a Gas-Cooled Microreactor Balance of Plant for the Virtual Test Bed
High Temperature Gas-cooled Reactors (HTGRs) are one of the technology considered for microreactor designs because of their high performance and technological maturity. Several designs are being developed. These microreactor cores are connected to power conversion system (PCS) through a heat exchanger. For transient analysis, the feedback of the PCS or of the primary loop can be important, therefore, it is necessary to develop robust models for the balance of plant that can be coupled to a core model. In this work, a balance of plant model using the MOOSE Thermal Hydraulics module (THM) is presented and demonstrated for startup and load-follow transients. This model can be seamlessly coupled with any MOOSE-based multi-physics core model using the MOOSE multi-app approach.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
A Fast, Accurate Prediction for System-Wide Damage Due to Dynamic Wind Loading
The complex relationship between photovoltaic (PV) hardware configurations, overall system dynamics, and turbulent aerodynamic phenomena generates highly unsteady, non-uniform loads that can lead to damaging instabilities. These effects may result in glass breakage, cell cracking, and structural failures in frames and mounting systems, even under moderate wind conditions. Addressing industry concerns about premature system failures in field conditions deemed survivable, our research aims to develop a fast and accurate predictive model for system damage. This model integrates configurable hardware choices with advanced simulation tools to represent the overall system-specific dynamics effectively. Using this model, we predict responses under varying weather conditions and hardware setups, translating these predictions into pre-trained surrogate models capable of accurately identifying failure risks and rapidly testing new system hardening measures. In this presentation, we will showcase preliminary results in capturing system dynamics through our customizable library of PV hardware configurations. Additionally, we will highlight how these new tools build upon PVade's established wind load modeling capabilities and foster the development of advanced AI/ML surrogates for improving system robustness.
97 MATHEMATICS AND COMPUTING↗
Engineering Analysis Baseline Model Requirements
The purpose of this document is to set requirements for the development, documentation, implementation, and maintenance of engineering analysis baseline models used by the Advanced Engineering and Analysis group to model and simulate systems. The intended audience of this document is the Engineering Technology and Design division Advanced Engineering and Analysis group—in particular the baseline model owners and users—and the Group Leaders and members of the Systems Engineering groups. Section 1 presents a discussion of general concepts related to engineering analysis models and baseline models, including definitions of common terms used in this requirements document. It also presents the purpose and intentions of creating and using baseline models and exposes some underlying assumptions about what baseline models are supposed to be. Section 2 presents requirements on the general categories of: the Advanced Engineering and Analysis Group Leader and the baseline model owner; parts, materials, and load cases; the process of generating and using the model; and how the baseline model will be stored, maintained, and documented. This document presents requirements on the baseline model as well as some aspects of the development process and the people responsible for the development; it does not address how the results of simulations utilizing these models will be used to make assessments
42 ENGINEERING↗
Develop and verify soil/structure interaction for pile/foundation interaction
Phase II of the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) project was used to verify the implementation of a new soil-structure interaction (SSI) model for use within offshore wind turbine modeling software. The REDWIN Macro-element model implemented and verified in this study enables a computationally efficient way to model the linear and nonlinear SSI problem, including hysteretic damping, of a monopile structure. The modeling approach was integrated into several modeling tools and a series of increasingly complex simulations was conducted using the IEA 10MW reference turbine mounted on a monopile support structure to verify the coupling between the tools and the REDWIN Macro-element SSI model. This campaign includes only numerical verification between various software and modeling approaches so no experimental measurements are available. The load cases (LC) considered include: LC1 – static response of the tower and substructure LC2 – frequency and mode-shape analysis of the tower and substructure LC3 – response of the tower and substructure due to wind-only loading LC4 – response of the tower and substructure due to wave-only loading LC5 – response of the tower and substructure due to wind and wave loading. Detailed properties of the modeled system are found in the following reference, “Bergua, Roger, Amy Robertson, Jason Jonkman, and Andy Platt. 2021. "Specification Document for OC6 Phase II: Verification of an Advanced Soil-Structure Interaction Model for Offshore Wind Turbines.” Golden, CO: National Renewable Energy Laboratory. NREL/TP-5000-79938. https://www.nlr.gov/docs/fy21osti/79938.pdf. Details on the results from the OC6 Phase II project can be found in the following reference, “Bergua R, Robertson A, Jonkman J, et al. OC6 Phase II: Integration and verification of a new soil–structure interaction model for offshore wind design.” Wind Energy. 2022;25(5):793-810. doi:10.1002/we.2698
17 WIND ENERGY↗
Development of fundamental power coupler for high-current superconducting RF cavity
Brookhaven National Laboratory took a project of developing a 704 MHz five-cell superconducting RF cavity for high-current linacs, including Energy Recovery Linac (ERL) for planned electron-hadron collider eRHIC. The cavity will be fed by a high-power RF amplifier using a coaxial Fundamental Power Coupler (FPC), which delivers 20 kW of CW RF power to the cavity. The design of FPC is one of the important aspects as one has to take into account the heat losses dissipated on the surface of the conductor by RF fields along with that of the static heat load. Using a simple simulation model we show the temperature profile and the heat load dissipated along the coupler length. To minimize the heat load on FPC near the cavity end, a thermal intercept is required at an appropriate location on FPC. A 10 K intercept was chosen and its location optimized with our simulation code. The requirement on the helium gas flow rate for the effective heat removal from the thermal intercept is also discussed.
43 PARTICLE ACCELERATORS↗
Modeling and Impact of Hyperloop Technology on the Electricity Grid
This paper provides an overview of hyperloop technologies, including a brief discussion of key components of a hyperloop system that determine the electric power requirements as a function of time. At-scale hyperloop systems do not yet exist, so a model was used to generate a set of load profiles for conceptual hyperloop realizations at four locations in the United States: two systems in California and one each in Colorado and Ohio. In all four cases, the modeled hyperloop load profiles included pulses in both active and reactive power. Grid modeling was performed to estimate the grid impacts and for dis-cussing grid integration challenges. The paper discusses how energy storage systems might be used to eliminate the pulsating load characteristics or significantly reduce it to accommodate current grid planning guidelines
24 POWER TRANSMISSION AND DISTRIBUTION↗
Temporal ensemble learning of univariate methods for short term load forecasting
Short term load forecasting (STLF) is a fundamental component of demand response programs in smart grids. Recent literature has focused on complex neural networks and deep learning models for solving STLF. While these models work well for load forecasting with complex non-linear relationships, they have been shown to be less effective than simpler univariate models for STLF problems with under a 6 hours horizon. Given the lack of multivariate data (such as temperature) in many practical datasets, we need better univariate prediction models for STLF. By partitioning the dataset by temporal features, we develop a novel ensemble learning method that exploits daily seasonality in electricity consumption data to improve accuracy of popular univariate models. We train an ensemble of models from the dataset partitions. We develop a variety of methods, including Ridge Regression, to increase the robustness of the ensemble prediction. To show the effectiveness of our approach, we perform detailed evaluation using an aggregated user electricity consumption dataset collected by the Los Angeles Department of Water and Power (LADWP). We select four popular prediction algorithms in literature for our experiments, including Kernel Regression (KR), Support Vector Regression (SVR) and neural network approaches. We compare the performance of these algorithms applying our ensemble approach to training only one single model. Our approach leads to an 11.2% decrease in mean absolute percentage error (MAPE) and 21.3% decrease in root mean squared error (RMSE) over the single model approach for KR, and a 30% and 32.4% decrease in MAPE and RMSE respectively for SVR. These ensemble models also outperform the neural network approaches.
14 SOLAR ENERGY↗
Refinement of Modeling Techniques for the Structural Evaluation of Hanford Single-Shell Nuclear Waste Storage Tanks
ABSTRACT Several tanks at the Hanford Site (in Washington State, USA) belong to the first generation of underground nuclear waste storage tanks known as single shell tanks (SSTs). These tanks were constructed between 1943 and 1964 and are well beyond their design life. This article discusses the structural analysis approach and modeling challenges encountered during the ongoing analysis of record (AOR) for evaluating the structural integrity of the SSTs. There are several geometrical and material nonlinearities and uncertainties to be dealt with while performing the modern finite element analysis of these tanks. The analysis takes into account the temperature history of the tanks and allowable mechanical operating loads of these tanks for proper estimation of creep strains and thermal degradation of material properties. The loads prescribed in the AOR models also include anticipated loads that these tanks may see during waste retrieval and closure. Due to uncertainty in a number of inputs to the models, sensitivity studies were conducted to address questions related to the boundary conditions to realistically or conservatively represent the influence of surrounding tanks in a tank farm, the influence of backfill excavation slope, the extent of backfill and the total extent of undisturbed soil surrounding the backfill. Because of the limited availability of data on the thermal and operating history for many of the individual tanks, some of the data was assumed or interpolated. However, the models developed for the analysis of record represent the bounding scenarios and include the loading conditions that the tanks were subjected to or anticipated. The modeling refinement techniques followed in the AOR resulted in conservative estimates for force and moment demands at various sections in the concrete tanks. This article discusses the modeling aspects related to Type-II and Type-III SSTs. The modeling techniques, methodology and evaluation criteria developed for evaluating the structural integrity of SSTs at Hanford are in general applicable to any similar tanks or underground concrete storage structures.
Waste Managament, Single Shell Tanks, Structural E↗
Topology Control for Load Shed Recovery
This paper introduces load shed recovery actions for transmission networks by presenting the dc optimal load shed recovery with transmission switching model (DCOLSR-TS). The model seeks to reduce the amount of load shed, which may result due to transmission line and/or generator contingencies, by modifying the bulk power system topology. Since solving DCOLSR-TS is computationally difficult, the current work also develops a heuristic (MIP-H), which improves the system topology while specifying the required sequence of switching operations. Experimental results on a list of N-1 and N-2 critical contingencies of the IEEE 118-bus test case demonstrate the advantages of utilizing MIP-H for both on-line load shed recovery and recurring contingency-response analysis. This is reinforced by the introduction of a parallelized version of the heuristic (Par-MIP-H), which solves the list of critical contingencies close to 5x faster than MIP-H with 8 cores and up to 14x faster with increased computational resources. The current work also tests MIP-H on a real-life, large-scale network in order to measure the computational performance of this tool on a real-world implementation.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Grid Impacts of Hyperloop Technology in the United States
This report provides an overview of Hyperloop technologies including the current technology developments and a brief discussion on key components of the Hyperloop system that determine the electric power requirements as a function of time. At-scale Hyperloop systems do not exist yet so a model was used to generate a set of load profiles for conceptual systems at four locations in the United States: two in California, one in Colorado, and one in Ohio. In all four cases, the modeled Hyperloop load profiles showed a pulsating characteristic in both active and reactive power. Grid modeling was performed by Pacific Northwest National Laboratory to estimate the grid impacts and to discuss grid integration challenges. This report discusses the outcomes of the grid simulations for four study cases and offers some perspective of how existing large industrial loads with similar load characteristics are treated to accommodate current grid planning guidelines. The report concludes with a discussion on how energy storage system might be used to eliminate the pulsating load characteristics or significantly reduce it.
24 POWER TRANSMISSION AND DISTRIBUTION↗
OC7 Project Phase II: Code Comparison and Experimental Validation of Hydroelastic Effects and Member-Level Loads in Floating Structures
This paper presents results from Phase II of the OC7 project, focusing on hydroelastic modeling and member-level load prediction for a flexible floating offshore structure. Numerical predictions from 11 academic and industrial partners are validated against experimental measurements obtained from a 1:70-scale test of the VolturnUS-S semisubmersible platform. A comprehensive set of verification and validation cases is examined. The results demonstrate that hydrodynamic added mass has a significant impact on predicted elastic natural frequencies. Under regular wave excitation, the numerical models reproduce platform motions and mooring line tensions with good accuracy. Member-level loads are also predicted with reasonable accuracy. Some discrepancies are observed for potential-flow models not accounting for higher-order effects associated with the instantaneous wetted surface.
17 WIND ENERGY↗
Study of photon–magnon coupling in a YIG-film split-ring resonant system
By using the stripline Microwave Vector–Network Analyser Ferromagnetic Resonance and Time Domain spectroscopy techniques, we study a strong coupling regime of magnons to microwave photons in the planar geometry of a lithographically formed split-ring resonator (SRR) loaded by a single-crystal epitaxial yttrium–iron–garnet (YIG) film. Strong anti-crossing of the photon modes of SRR and of the magnon modes of the YIG film is observed in the applied-magnetic-field resolved measurements. The coupling strength extracted from the experimental data reaches 9% at 3 GHz. Theoretically, we propose an equivalent circuit model of the SRR loaded by a magnetic film. This model follows from the results of our numerical simulations of the microwave field structure of the SRR and of the magnetisation dynamics in the YIG film driven by the microwave currents in the SRR. The results obtained with the equivalent-circuit model are in good agreement with the experiment. This model provides a simple physical explanation of the process of mode anti-crossing. Our findings are important for future applications in microwave quantum photonic devices as well as in nonlinear and magnetically tuneable metamaterials exploiting the strong coupling of magnons to microwave photons.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
BuildingsBench: A Benchmark for Universal Building Load Forecasting [SWR-23-51]
The residential and commercial building stock in the United States is responsible for a significant percentage of energy consumption and greenhouse gas emissions. Electrification of end-uses, as well as decarbonizing the electrical grid through renewable energy sources such as solar and wind, constitutes the pathway to zero-emission buildings. Forecasting day-ahead building energy consumption is an integral part of this solution. Currently, specialized forecasting models are hand-made for each individual building, which is time-consuming, expensive, and leads to duplicated efforts. BuildingsBench is a Python software framework for training and comparing generalized machine learning models for universal building load forecasting. This challenge tasks a single foundational model to generalize its forecasts for a wide variety of buildings, across geographic regions, building types, weather patterns, and more. This software provide code for pre-training such models and subsequently evaluating their performance on a suite of hundreds of diverse real and synthetic buildings. BuildingsBench is a platform for: - Large-scale pretraining with the synthetic Buildings-900K dataset for short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock and is extracted from the NREL End-Use Load Profiles database. - Benchmarking on two tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. We provide an index-based PyTorch Dataset for large-scale pretraining, easy data loading for multiple real building energy consumption datasets as PyTorch Tensors or Pandas DataFrames, simple (persistence) to advanced (transformer) baselines, metrics management, and more.
Emami, Patrick↗
Communication-Constrained Robust Control and Learning of Grid-Connected
The electric grid of things (EGoT) promises great potential for innovative grid services by tapping into vast load flexibility. However, the unique characteristics of EGoT, being a part of the cyber-physical electric power system, present both opportunities and challenges, especially concerning supply-demand balancing, stability, and communication constraints. Traditionally, centralized control was employed to ensure balance and stability in power systems. However, with the massive influx of EGoT devices, new strategies are needed to efficiently coordinate and control these distributed devices for optimal grid operations. While some studies have explored efficiency and economic models, there remains a gap in ensuring reliability under everyday operations and resilience during extreme conditions. Addressing this gap, this project develops the technology for an Energy Service Interface (ESI) that includes novel pricing, control, learning, and distributed optimization algorithms, which will enable utilities to recruit EGoT assets for crucial grid services such as load flexibility, voltage regulation, and situation-awareness. The key novelty of the proposed technology is the careful distribution of learning and control functions across utility and EGoT asset owners such that provably efficient and resilient grid operations are attained while respecting communication and information-exchange constraints. Specifically, the project team develops machine-learning enhanced load modeling methods to allow EGoT asset owners to learn their load capability and flexibility, and develops pricing-based and decentralized learning-based control so that asset owners can coordinate to meet system-wide demand-supply balance and reliability goals. For extreme situations involving high-impact, low-probability catastrophic events (termed the “black-sky” events), the team also develops (1) a “Feeder-Operating Center-on-a-Laptop” (FOCAL) software that can assist utility personnel in leveraging EGoT assets to accelerate the service recovery of damaged feeders, and (2) distributed optimization algorithms that can coordinate the operation points of EGoT devices under severe communication constraints. The proposed technology has been extensively tested and evaluated through simulations and on a testbed. In summary, as we transition into a more interconnected and digital power grid era, our project’s findings and developments offer a pivotal step toward guaranteeing both efficiency and resilience in the face of both everyday operations and rare “black-sky” events.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Next Generation System Analysis Model: Recently Added Features and Future Plans
To better enable informed decision-making regarding the back-end of the nuclear fuel cycle, the Integrated Waste Management System (IWMS) program within the U.S. Department of Energy, Office of Nuclear Energy (DOE-NE) has been sponsoring the development and application of system analysis tools capable of analyzing various system options for the management of spent nuclear fuel (SNF) and high-level radioactive waste (HLW).With these tools, IWMS architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). The Next Generation System Analysis Model (NGSAM) is an agent-based simulation software tool expressly designed to be capable of modeling features within various IWMS architectures. NGSAM imports data from Oak Ridge National Laboratory (ORNL)’s unified database (e.g., historic assembly information, thermal profiles for assembly heat, and at-reactor dry storage loadings) to ensure that each simulation initializes with a realistic representation of the state of commercial SNF in the U.S. Recent major enhancements implemented into NGSAM in the period since NGSAM was last presented at the WM2019 conference include: • Tracking of railroad escort car acquisition and buffer car acquisition • The addition of heavy haul truck (HHT) and barge routes for some sites, as well as support for user-defined inter-modal routes • Updates to the logic that checks the transportation cask thermal limit maps prior to package transport • An allocation method that predicts when reactor sites would pack assemblies from their spent fuel pools for dry storage, and prioritizes shipments directly from the pools of those reactor sites in the preceding periods (before the predicted loadings to dry storage), thus reducing the number of casks loaded into dry storage at reactor sites • The addition of “reactor site family” operational limits to restrict the number of loads of SNF taken from the pool and from dry storage at a given reactor site each year • Added support for multiple canister loading map options and packages with multiple compatible transportation overpacks • Updates to the handling of non-commercial SNF, including a new database that contains data to support the updates • Updates to allow analysis of hypothetical scenarios which include repackaging at reactor sites, e.g., for possible comparative analysis with other scenarios • Implementation of additional output reports, or modification of existing ones • The ability to generate and implement user edits via the NGSAM website • The ability to model loading SNF from pool storage at an interim storage facility (ISF) into dry storage at the ISF • The ability to model consolidating SNF from different existing storage containers at a DOE site into the same DOE standard canister • The ability to model transferring SNF casks from one transportation mode to another, e.g., from HHT to rail, referred to as transloading. These new features have improved NGSAM capabilities and users’ experience with the model. Preliminary NGSAM requirements for modeling advanced reactor fuels, reprocessing, treatment, and conditioning were considered, and this paper describes them at a high level. Other nuclear fuel cycle system analysis tools developed under sponsorship of DOE-NE, like the VISION code developed at Idaho National Laboratory, might be better suited for initial high-level analysis of those technologies and advanced fuel cycles. As technologies are developed and system concepts evolve, NGSAM could provide value by providing more detailed modeling of transport, storage, and disposal of spent fuel and wastes from advanced reactors and advanced fuel cycles at the fuel element and waste container level.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Experimental characterization and constitutive modeling of the mechanical behavior of molybdenum under electromagnetically applied compression-shear ramp loading
Magnetically applied pressure-shear (MAPS) is a new experimental technique that provides a platform for direct measurement of material strength at extreme pressures. The technique employs an imposed quasi-static magnetic field and a pulsed power generator that produces an intense current on a planar driver panel, which in turn generates high amplitude magnetically induced longitudinal compression and transverse shear waves into a planar sample mounted on the drive panel. In order to apply sufficiently high shear traction to the test sample, a high strength material must be used for the drive panel. Molybdenum is a potential driver material for the MAPS experiment because of its high yield strength and sufficient electrical conductivity. To properly interpret the results and gain useful information from the experiments, it is critical to have a good understanding and a predictive capability of the mechanical response of the driver. In this work, the inelastic behavior of molybdenum under uniaxial compression and biaxial compression-shear ramp loading conditions is experimentally characterized. It is observed that an imposed uniaxial magnetic field ramped to approximately 10 T through a period of approximately 2500 μs and held near the peak for about 250 μs before being tested appears to anneal the molybdenum panel. In order to provide a physical basis for model development, a general theoretical framework that incorporates electromagnetic loading and the coupling between the imposed field and the inelasticity of molybdenum was developed. Based on this framework, a multi-axial continuum model for molybdenum under electromagnetic loading is presented. The model reasonably captures all of the material characteristics displayed by the experimental data obtained from various experimental configurations. Additionally, data generated from shear loading provide invaluable information not only for validating but also for guiding the development of the material model for multiaxial loadings.
36 MATERIALS SCIENCE↗
Side-by-Side Comparison of Subhourly Clipping Models
Over the past several years there have been numerous attempts at quantifying the inherent power clipping of inverters due to subhourly irradiance variability that is not captured in hourly PV performance models. Different models have been proposed to correct for these clipping losses in PV performance estimates, including matrix lookup models, distribution modeling of the PV power performance within a given hour, and machine learning methods. To date, there have been few comprehensive quantitative comparisons of these inverter clipping correction modeling approaches to evaluate the effectiveness of these approaches in predicting the actual behavior of PV system inverter clipping. In this study, we perform such a comparison, evaluating the Allen and Walker correction loss modeling approaches recently implemented in the System Advisor Model (SAM) against clipping losses modeled with 1-minute climate data. These comparisons were performed across a variety of climate locations and inverter loading ratios to thoroughly analyze the effectiveness of these modeling approaches relative to each other. Results from this analysis reveal that both clipping correction approaches improve annual energy accuracy to within 2% of 1-minute modeled energy yield. The two models predict annual clipping loss more accurately than simple hourly power limit clipping, with the Allen method typically being slightly more accurate at typical ILR values and the Walker method often being slightly more accurate at high ILR values The models can improve accuracy over the status quo clipping approach up to 3 percentage points in systems with ILR of 2.0, showing the importance of this modeling factor in energy yield estimates.
accuracy↗