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DOE OSTI2022

Dynamic Modeling, Parameter Estimation, and Data Reconciliation of a Supercritical Pulverized Coal-Fired Boiler

A high-fidelity, spatially distributed dynamic model of a supercritical pulverized coal-fired boiler was developed for calculating transient thermal profiles of the boiler tubes, which are often not measured or unmeasurable in industrial settings due to the harsh operating conditions. In this work, rigorous models of the subcritical and supercritical steam properties, which can be highly nonlinear, were used to capture the transition between two-phase and one-phase flow, respectively, as supercritical boilers frequently transition through the critical point during load-following operation. The transient boiler model was also used for data reconciliation and parameter estimation for the purpose of model validation against industrial data. An approach was developed to reduce the size of large-scale reconciliation problem by focusing on the implementation of bias terms for the reconciled variables, which can be estimated in place of the reconciled variables themselves. In addition, a functional approximation of the reconciliation problem was defined so that it can be more simply optimized. Combining this functional approximation optimization with reformulation of the reconciliation problem considering measurement biases can significantly improve the tractability of many such large-scale problems. The validated boiler model was then used to study transient responses under load-following operation with specific attention on the calculation of unmeasurable variables.

42 ENGINEERING↗

DOE OSTI2021

Structural dynamics modeling of spent nuclear fuel during hypothetical package drop events

The response of spent nuclear fuel (SNF) to hypothetical package drop events is of particular interest in the scope of spent fuel storage and transportation because of the mechanical shock encountered in such scenarios. Previous testing and modeling by the U.S. Department of Energy has demonstrated that the shock and vibration environment of normal shipping and handling conditions (excluding package drop events) is relatively benign and does not challenge the integrity of spent nuclear fuel. Cask drop events are worth considering because SNF packages are required to withstand free drops onto unyielding surfaces as part of their licensing basis. The acceleration experienced during drop events can be orders of magnitude higher, and thus more advanced models are needed to encompass potential nonlinear behavior of the fuel, such as spacer grid buckling and rod-to-rod impact. This work describes a number of finite element models developed to calculate the response of spent nuclear fuel to various hypothetical drop events that have been validated by package and fuel assembly drop tests conducted in the last decade. Sensitivity of the model response to factors such as package drop orientation, secondary impacts, and irradiated material properties as well as their potential impacts to fuel cladding integrity, was also investigated. Cask drops are not expected as a regular occurrence during SNF transportation, but this work helps raise the understanding of SNF mechanical loads to the point of consistency with the package design requirements.

Kadooka, Kevin↗

DOE OSTI2026

Performance Evaluation of a Microgrid System with Grid-Forming and Grid-Following Inverters with Diesel Generators: Insights From Hardware Experiments

This paper presents a comprehensive performance evaluation of a microgrid system integrating grid-forming (GFM) inverters, grid-following (GFL) inverters, and a diesel generator, focusing on their interactions and behavior under various dynamic events. The study is conducted using a pure hardware setup comprising two GFM inverters, one GFL inverter, a diesel generator, load banks, a point of common coupling (PCC), and an emulated main grid. The evaluation specifically examines dynamic scenarios, including voltage jumps, phase jumps, rate of change of frequency (ROCOF), synchronization, and islanding operations, which pose critical challenges to system stability. Among all the tests conducted, the overloading and phase jump tests proved to be the most challenging. The capacity of the DC side is crucial for withstanding overloading and grid disturbance tests; otherwise, GFM inverters frequently trip due to DC undervoltage. Throughout all grid disturbance tests, the diesel generator consistently stands out as the most robust and reliable GFM unit in the system. Overall, insights from these hardware experiments shed light on the response characteristics of different generation types during grid disturbances and identify potential stability concerns in such hybrid microgrid systems.

14 SOLAR ENERGY↗

DOE OSTI2022

Hybrid Heat Pump Controls: Conventional Dual Fuel versus Seamlessly Fuel Flexible Heat Pump

This paper compares the performance of a novel seamlessly fuel flexible heat pump (SFFHP) and conventional dual fuel heat pump (DFHP) for space heating. The conventional dual fuel systems either run on the gas furnace or electric heat pump at any given moment, as a comparison, the proposed seamlessly fuel flexible heat pump simultaneously consumes gas and electricity by continuously optimizing the proportion of each. The process air flows across the heat pump condenser first and then flows across the furnace coil, therefore, the heat pump temperature lift is reduced. SFFHP delivers energy savings by allowing each subsystem, i.e., gas furnace and electric heat pump, to operate where it performs best.For DFHP, two operation control strategies, i.e., non-restricted control and restricted control, are available on market. For the non-restricted mode, the thermostat has a switching temperature-programmed according to the balancing point of heating load and capacity curve. Heat pump operates above the switching temperature, while the furnace takes over under the switching temperature. For the restricted control, the compressor of a heat pump is disabled below a predefined lockout outdoor temperature to let the furnace take over. For SFFHP, a model predictive control strategy is developed to continuously adjust the capacities of the electric heat pump and gas furnace based on the foreseen weather data, utility price signals, and marginal grid emission signals with the goal of minimizing the utility cost and CO2 emission while guaranteeing comfort requirements.In this paper, DFHP and SFFHP are simulated using high-fidelity heat pump performance curves generated from DOE/ORNL heat pump design model. Performance comparison of DFHP and SFFHP during 2019-2020 heating season in Los Angeles shows that SFFHP with model predictive control achieves 23% utility cost reduction and 17 % CO2 emission reduction. Case studies demonstrate that SFFHP can deliver significant reductions in peak demand, utility cost, and CO2 emission. As a result, SFFHP can deliver superior benefits for utility cost reduction and CO2 emission reduction over conventional dual fuel heat pump.

Li, Zhenning↗

DOE OSTI2020

Learning-Based Load Control to Support Resilient Networked Microgrid Operations

Microgrids have proven to be an effective option for increasing the resiliency of critical end-use loads during extreme events. Building on past operational experiences, some microgrid operators are examining the potential to network microgrids to further improve resiliency. However, the frequency deviations experienced on isolated microgrids during transient events, such as switching operations, step increases in load, and loss of generation, are significantly larger than those typically seen on bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. This paper presents a method of mitigating the impact of transient events by engaging end-use loads using Grid-Friendly Appliance TM (GFA) controllers. An online, i.e., real-time, device-level algorithm is presented, which adjusts individual GFA controller frequency set-points based on the operational characteristics of each end-use load, and on the changing grid dynamic characteristics. The presented method improves the dynamic stability of the networked microgrid operations while minimizing the interruptions to end-use loads. The presented work is validated with dynamic simulations using a modified version of the IEEE 123-node test system with three microgrids, using the GridLAB-D TM simulation environment.

Radhakrishnan, Nikitha↗

DOE OSTI2019

Substructure Flexibility and Member-Level Load Capabilities for Floating Offshore Wind Turbines in OpenFAST

OpenFAST is an open-source, physics-based engineering tool applicable to the load analysis of land-based and offshore wind turbines, including floating offshore wind turbines. The substructure for a floating wind turbine has historically been modeled in OpenFAST as a rigid body with hydrodynamic loads lumped at a point, which enabled the tool to predict the global response of the floating substructure but not the structural loads within its individual members. This limitation is an impediment to designing floating substructures - especially newer designs that are more streamlined, flexible, and cost-effective. This paper presents the development plan of new capabilities in OpenFAST to model floating substructure flexibility and member-level loads, including the functional requirements and modeling approaches needed to understand and apply them correctly.

17 WIND ENERGY↗

DOE OSTI2020

Implementation of Substructure Flexibility and Member-Level Load Capabilities for Floating Offshore Wind Turbines in OpenFAST

OpenFAST is an open-source, physics-based engineering tool applicable to the load analysis of land-based and offshore wind turbines, including floating offshore wind turbines. The substructure for a floating wind turbine has historically been modeled in OpenFAST as a rigid body with hydrodynamic loads lumped at a point, which enabled the tool to predict the global response of the floating substructure but not the structural loads within its individual members. This limitation is an impediment to designing floating substructures - especially newer designs that are more streamlined, flexible, and cost-effective. This paper presents the development of new functionality in OpenFAST to model floating substructure flexibility and member-level loads, as well as the concepts and mathematical background needed to understand and apply it correctly.

17 WIND ENERGY↗

DOE OSTI2023

A subdivision-stabilized B-spline mixed material point method

Subjected to external loadings, polymeric materials, e.g., biological tissues, hydrogels, and elastomers, may undergo extreme, nearly incompressible, (self-)contact deformations. For numerical modeling employing mesh-based techniques such as the finite element method (FEM), these deformations pose significant challenges due to large distortions in the deformed geometry, accuracy issues stemming from volumetric locking effects, and increased computational cost from complex contact searches. As an alternative to mesh-based methods, the material point method (MPM), a continuum-based particle technique, is gaining attention for its ability to handle extreme distortions and capture no-slip contact without added cost. For nearly incompressible material behaviors, while mixed formulations can address locking effects by treating displacements and pressure as independent fields, they can suffer from numerical instabilities close to the incompressibility limit due to the violation of the inf-sup condition, leading to inaccurate nodal pressure solutions. Here we propose an efficient and stable mixed B-spline material point method with highest achievable regularity for quasi-compressible polymeric materials. Using the two-scale relation of B-splines, we introduce a subdivision-stabilization for the two-field mixed MPM and obtain numerically stable, oscillation-free nodal solutions with equal-order interpolations with optimal regularity. Building on the Eulerian-Lagrangian nature of MPM, a previously-converged solution framework is adopted to mitigate issues related to cell-crossing and numerical fracture artifact present in standard MPM. We assess the stability and accuracy of the developed mixed MPM at large deformations for soft materials through the benchmark Cook’s membrane problem. Additionally, we test the robustness of the proposed MPM by modeling several examples, including the compression and indentation of a circular block into a quasi-compressible substrate and the twisting deformation of a rectangular block. The findings demonstrate the MPM’s capabilities for modeling practical soft material applications.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Data Processing Package for Cyclic Integrated Reversible Bending Fatigue Testing

A data processing software package has been introduced. The package was developed using MATLAB with the aid of the Curve Fitting Toolbox. The package is made up of four modules: pre-processing, data processing for static testing, data processing for monitoring, and data processing for measurements. CIRFT data are structured with multiple levels of architecture involving group, specimen, session, and scan/block. The degree of complexity of the data structure depends on whether a test is static or cyclic.The test results are presented in figures, scatter plots, and tables. For static testing, the output in tables provides bending mechanical properties and characteristic points of moment–curvature relation: flexural rigidities in linear segments of loading and unloading stages, intersection points between characteristic segments of the curve, and equivalent stress and strain quantities. For cyclic testing, the table output lists control and fatigue life and responsive/dependent quantities including moment, curvature, flexural rigidity, flexural hysteresis, rigidity phase angle, and equivalent stresses and strains. In addition, derivatives such as half-gage length and sensor spacing correction are included. The output also provides standard deviations of the reported quantities when they are applicable or available. The data processing package can serve as a fundamental characterization tool in mechanical study of materials. The package is intended primarily for data processing for the CIRFT process and can also be used in applications for which similar testing requirements exist.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Machine Learning-Based Identification of the Interface Regions for Coupling Local and Nonlocal Models

Local-nonlocal coupling approaches provide a means to combine the computational efficiency of local models and the accuracy of nonlocal models. However, the coupling process can be challenging, requiring expertise to identify the interface between local and nonlocal regions. Here, this study introduces a machine learning-based approach to automatically detect the regions in which the local and nonlocal models should be used. The method uses loading functions evaluated at grid points to decide the model selection at those points. Training of the networks is based on datasets provided by classes of loading functions for which reference coupling configurations are computed using accurate coupled solutions, where accuracy is measured in terms of the relative error between the solution to the coupling approach and the solution to the nonlocal model. We study two approaches that vary in data structure. The first, the full-domain input data approach, uses the entire load vector and outputs a complete label vector, performing a global classification. The second, a window-based approach, processes loads into windows and addresses the problem as a node-wise classification where each window's central point is classified individually. The classification problems are solved via deep learning algorithms based on convolutional neural networks. The performance of these approaches is studied on one-dimensional numerical examples using F1-scores and accuracy metrics. Notably, the windowing approach achieves an accuracy of 0.96 and an F1-score of 0.97, highlighting its potential to automate coupling processes effectively and enhance computational efficiency in material science applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Improvements to the Simplified Loads Methodology in IEC 61400-2: November 22, 2021 - November 21, 2022

The "Simplified Loads Model" (SLM) of IEC 61400-2 provides a simple methodology to assess the structural integrity of a small wind turbine (SWT). The SLM is unique to the small wind turbine standard. It was included to allow SWT manufacturers with limited resources to undertake integrity checks at a reasonable cost in time and resources and avoid the expense of detailed aeroelastic simulations. Unfortunately, the SLM has gained the reputation of being overly conservative and this has reduced its value to the SWT community and its use in SWT design and certification. Conservatism in design standards is needed but excessive conservatism is not. The aim of this report is to address the principal areas of excess conservatism and recommend changes to the SLM that preserve its simplicity but reduce the excess. The changes for the ultimate loads are consistent with their treatment in aeroelastic modelling for certification and with related codes for wind loading on structures. The recommendations for a new fatigue design load case are also based on aeroelastic simulations, in this case of five SWTs of varying configurations with rated power from 2.4 to 50 kW. It is also pointed out the design load case for yawed operation omits an important term. The recommended inclusion of this term would make the SLM slightly more conservative for this case.

17 WIND ENERGY↗

DOE OSTI2024

A Data-Driven Methodology for Contextual Unit Commitment Using Regression Residuals

Day after day, system operators are faced with the challenge of taking unit commitment (UC) decisions under uncertain net load conditions. The standard operating procedure for taking UC decisions begins by leveraging auxiliary data on covariates (such as the day of the week or latest weather information) to generate a point prediction for net load, which is used in solving a deterministic UC problem. Such an approach, however, is known to deliver a notoriously poor out-of-sample (OOS) performance, as it completely disregards the stochastic nature of net load. While stochastic programming models explicitly represent uncertainty, they mostly do so using a generic set of scenarios that neglect covariate observations, squandering useful auxiliary data that could be harnessed to glean insights into uncertainty. In this article, we discuss a contextual stochastic optimization approach to UC, which effectively exploits covariate observations while explicitly assessing uncertainty so as to boost the OOS performance of UC decisions. The key thrust of our approach is to leverage regression models, along with their empirical residuals, to set up and solve sample average approximation problems. Not only do we prove that our approach satisfies the requisite conditions for asymptotic optimality and consistency laid out in (Kannan et al., 2022), but we also assess its performance on several case studies conducted using real-world data collected in California ISO and New York ISO grids. In conclusion, results show that the proposed approach can significantly improve OOS performance compared to alternative methods proposed in the literature under varying dataset sizes.

Yurdakul, Ogun↗

DOE OSTI2021

Employing Weibull Analysis and Weakest Link Theory to Resolve Crystalline Silicon PV Cell Strength Between Bare Cells and Reduced- and Full-Sized Modules

Weibull analysis and weakest link theory are employed to resolve the probability of crystalline silicon PV cell fracture when measured as bare cells and when stressed in reduced- and full-sized modules. Experimental results indicated that the characteristic cell strength is reduced by ~20% once packaged into the laminate of a one-cell module and loaded in four-point flexure (4PF). This experimental observation was shown consistent with a weakest link theory prediction that the strength limiting flaws reside on the surface of the cell's edge. Here, the analysis is ultimately extended to present the equivalent loading of four-cell modules by uniform pressure and 4PF and a uniformly loaded full-sized module and demonstrates that smaller, representative, modules must be loaded to a much higher level than their parent full-sized modules to achieve an equivalent probability for cell fracture.

14 SOLAR ENERGY↗

DOE OSTI2020

Crushing behaviors of unidirectional carbon fiber reinforced plastic composites under dynamic bending and axial crushing loading

The dynamic crushing characteristics of unidirectional carbon fiber reinforced plastic composites under two loading types, dynamic three-point bending and axial crushing, are investigated by experiment and finite element simulation. Hat section samples with two different layup orientations are tested at various impact velocities to investigate its effects. The experiment results show that delamination plays a critical role in dynamic bending deformation. Different layup orientations lead to similar crushing bending behaviors, but remarkable variations in delamination pattern evolutions. In contrast, layup orientation exerts more significant effects on failure modes and energy absorption in axial crushing. A finite element model with multiple layers of thick shell element and cohesive element is established, and simulations are performed for both dynamic three-point bending and axial crushing. The model can capture the crushing behaviors in the two loading types reasonably well. The damage evolution and energy absorption through various mechanisms at different loading conditions are discussed.

42 ENGINEERING↗

DOE OSTI2022

Modelling of edge plasma dynamics with active wall boundary conditions

A self-consistent 2D model is presented for transport in boundary plasma and plasma-facing material walls. Plasma dynamics in the domain is represented by a 2D collisional plasma fluid model in the edge-plasma code UEDGE, and transport of hydrogen and heat in the wall is represented by a system of reaction–diffusion equations in the 1D wall code FACE. To account for variation of parameters along the wall, in the coupled model multiple instances of the FACE code run in parallel. Here, the coupled model provides a tool for investigating a range of dynamic plasma–material interactions phenomena in 2D. For demonstration of its capability, one application of particular interest is the role of active wall in tokamak strike point sweeping proposed for mitigation of divertor heat loads. In the present study, the coupled calculations are applied to investigation of the impact of heat and hydrogen transport in the material wall on the divertor plasma and target heat load during sweeping of the target strike point for parameters of a high-power tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Seasonal Cost-Benefit Analysis of Automated Distribution Feeder Upgrades with Advanced Mitigation Technologies

The increasing deployment of distributed solar photovoltaics (DPV) to meet clean energy goals can trigger adverse grid operation issues, such as voltage excursions and the violation of thermal loading constraints of the power delivery elements (e.g., lines and transformers) on the evolving electricity infrastructure. Such integration issues would require distribution upgrades with associated costs to mitigate them and to maintain reliable and resilient grid operating conditions. Traditional distribution network upgrade approaches use a specific single snapshot analysis that is overly conservative. This study considers a multi-time point analysis to capture both moderate (probable bounds) and extreme grid operating conditions using time points such as minimum load with minimum photovoltaics (PV), maximum load with maximum PV, maximum load with minimum PV, and minimum load with maximum PV. Further, this study investigates seasonal variation impacts and associated distribution upgrade costs for a spring season case (March, representing a low load and high PV scenario) and a summer case (July, representing a high load and high PV scenario). Such seasonal analysis will allow system operators to characterize upgrade requirements and associated costs across various periods. Because the spatial distribution of DPV can impact upgrade and associated costs, this study investigates three common DPV deployment scenarios - randomly deployed, close to the substation, and far from the substation - at different penetration levels. Apart from spatial distribution impacts, this project evaluates the techno-economic impacts of the nodal photovoltaic penetration factor (NPPF) for generating the various DPV deployment scenarios at increasing penetration levels. This project investigates the impact of varying nodal PV-to-load ratios using conservative and extreme NPPF values of 3 and 10, respectively. This study investigates the deployment of traditional infrastructure upgrade strategies, such as installing new voltage regulating equipment, transformers and lines replacements, and the activation of advanced inverter functionality (e.g., autonomous volt/VAR) in expanding PV hosting capacity. Existing DPV systems are assumed to operate with the legacy unity power factor, and we considered the possibility of retrofitting such systems with the activation of volt/VAR control as integration standards and regulations continue to evolve to allow such functions. The cost-benefit analysis metrics used in study include distribution upgrade costs, average cost per watt of the upgrade cost, average marginal cost per watt of the upgrade cost, and power losses.

14 SOLAR ENERGY↗

DOE OSTI2025

Integrating Machine-learning-assisted Computer Vision with RICH System

Developments in artificial intelligence have vastly expanded the capabilities of robots. Currently, the Spallation Neutron Source (SNS) beamlines at Oak Ridge National Lab (ORNL) have robotic sample loaders to increase the efficiency of running experiments. However, they require retraining if anything about the situation changes, e.g., where the samples are, and cannot notice if errors occur. So, the viability of using computer vision and machine learning to enhance these sample loaders’ functionality was investigated. In this project, the RICH system with a Dobot CR3 6-axis robot present at the VULCAN beamline assisted by an Intel Realsense D435i camera, a unique camera that enables convenient translation of 2D pixel coordinates to 3D world points, was programmed to load ceramic crucibles into a thermogravimetric analyzer (TGA) furnace. An algorithm was constructed in Python with three major phases planned: (1) obtaining a sample, (2) moving it to the target location, and then (3) bringing the sample back to its original location once the experiment finished. In the first phase, the algorithm would dynamically detect sample locations using ArUco markers to recognize the samples’ general location and a custom-trained yolov5 object detection model to locate the crucibles’ centers. Afterward, the robot would be directed to pick up samples based on the crucibles’ calculated positions. In the second phase, the robot would move the sample to a secondary point, reorient its grip, and place the sample at the target location. In the final phase, the robot would determine whether the sample was intact and would bring it back to its original place if it was or raise an alarm. Using this algorithm, the robot was able to pick up different types of crucibles at varying positions. These results indicate that integrating machine-learning-assisted computer vision with robotic sample loaders can result in effective autonomous detection of samples.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Power system load flexibility forecasting

The example embodiments are directed to a system and method for forecasting load flexibility of a power grid. In one example, the method includes receiving temperature values associated with temperature set points of a plurality of loads that are included on a power grid, forecasting a flexibility of the plurality of loads using a polynomial-time mixed-integer non-linear programming (MINLP) optimization based on the received temperature values for the plurality of loads, and outputting information about the forecasted flexibility for display to a display device. The MINLP optimization performs the forecasting of the load flexibility on a fine-grained basis in comparison to conventional methods and is still fast enough that it can be computed in real-time.

Genc, Sahika↗