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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development and Validation of MALAMUTE model for Electric Field Assisted Sintering of Structural Materials

Fusion power plant designs feature extreme material performance requirements for structural material candidates. In addition to conventional alloys, more advanced composites and oxide dispersion strengthened (ODS) alloys are being explored, however, achieving the desired microstructures to maximize performance using traditional manufacturing methods can be challenging. The advanced manufacturing (AM) electric field-assisted sintering (EFAS) technique offers improved control over the final microstructure through higher heating and cooling rates and moderate pressures. Modeling and simulation tools show promise in elucidating the process-structure-property-performance (PSPP) correlation for AM-produced parts, including the EFAS process. An inherently multiscale process, the EFAS technique aligns well with the multiscale modeling capability of the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE)[cite]. We present here an electro-thermo-mechanical approach to modeling the EFAS process using the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) code. Prediction of the field and gradient distributions across the EFAS tooling is required to accurately describe the conditions for the lower-scale microstructural evolution models. In this work we present the MALAMUTE model developed to predict the electrical potential, temperature, and mechanical stress distribution across the EFAS graphite tooling and part at the larger engineering-scale. Validation of the MALAMUTE engineering-scale model is completed using data from experimental densification and pre-densified runs of iron powder via EFAS at 1000oC. These runs were conducted using a Thermal Technology DCS-5 EFAS system. Data collected during the experiment runs include the direct current (DC) supplied to the graphite tooling, the temperature of the graphite tooling as measured with a pyrometer, and the force applied to the top of the graphite tooling stack, and the data were recorded every 10 seconds. Our validation approach used the current and force data from the EFAS run as boundary condition inputs to the MAMALUTE simulation; the temperature data were used to evaluate the MALAMUTE EFAS model prediction. Results of the MALAMUTE simulations are employed to connect the external pyrometer temperature measurement to the temperature profile across the part undergoing consolidation. We investigate the impact of material property variation and mesh deformation on the temperature profile as predicted by MALAMUTE. We conclude by highlighting projects where the MALAMUTE EFAS modeling and simulation capabilities will be used to assist experimental design.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

Experimentally Validated Computational Modeling of Creep and Creep-Cracking for Nuclear Concrete Structures

In a Nuclear Power Plant, one of the most important components is the concrete nuclear reactor cavity, which serves both a structural and protective function as the biological radiation shield. Given that creep has been identified as a major knowledge gap in the assessment of nuclear structures (NUREG/CR-7153), this work helps to further the understanding of creep behavior of massive concrete containment structures for decades to enable safe and long-term operation of these facilities. This project has developed a robust, experimentally validated model to predict creep in nuclear concrete structures for up 60 years using short-term creep data thereby enabling a longer service life of critical facilities and early detection of structural failure. The work presented in this report is a pairing of computational and experimental methods. For the first time, the time temperature superposition (TTS) principle was successfully used to generate a uniaxial creep compliance master curve to predict mortar creep response for up to 22,500 days (nearly 60 years) at a reference temperature of 20°C. These data were used as input into finite element analysis (FEA) codes that use highly realistic random, 3D concrete microstructures from reconstructed coarse limestone aggregates. Finite element analysis performed provides the ability to quickly upscale mortar viscoelastic behavior to long-term concrete creep/relaxation data. A master creep compliance curve, constructed from the TTS principle, spanning 27 years, was used to validate two and a half decades of simulated concrete creep. Concurrently, three different simulated wall specimens were designed to mimic the behavior of post-tensioned concrete nuclear containment facility vessel walls over time as a result of concrete creep. The specimens were designed with different thicknesses, transverse and longitudinal reinforcement ratios, and level of post-tensioning stress. Each specimen contained various instrumentation to measure internal concrete temperature, concrete strain, and post-tensioning strain hourly for over 3 years. The concrete creep model developed in this project, based on the FEA concrete simulations, was applied to simulate the structural-scale experiments of prestressed concrete walls conducted in this project using the Grizzly code. These models can represent the effects of reinforcing and prestressing. Although there are some discrepancies with the experimental data, the model can predict the overall trends of the creep response in these experiments. One of these experimental models was also applied to an extended time to demonstrate how the findings from this study can be used to predict the behavior of actual structures of interest that have been in service for extended periods of time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Highlighting the Impact of Yaw Control by Parsing Atmospheric Conditions Based on Total Variation: Preprint

Identification of atmospheric conditions within a multivariate atmospheric dataset is a necessary step in the validation of wind plant control strategies. Most often, operating conditions are characterized in terms of aggregated observations and assume that the atmosphere is 'quasi-steady'. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operation strategies. Controlling observational data for statistical stationarity highlights significant enhancements to the power production of waked turbines under wake steering wind plant control. Considering the combined energy ratio of the turbine to which yaw offsets are prescribed as well as the waked turbine show only moderate improvements when filtering for total validation. The same quality control that highlights wake steering also serves to underpin the off-nominal operation of the controlled turbine.

17 WIND ENERGY↗
DOE OSTI2022

Analysis of historic fires to determine most frequent challenging events

The fire probabilistic risk assessment framework for nuclear power plants relies on experimental data to determine expected fire behavior or to validate models to predict fire conditions in the plant. To support reducing the uncertainty in this experimental data, a research effort was conducted to identify the most frequent and challenging fire scenarios using historic fire data from nuclear power plants in the United States. To support this effort, an electronic version of the publicly available Updated Fire Event Database developed by Electric Power Research Institute was produced resulting in data on 2111 fire events, 540 events were labelled as being challenging fires with 74.2% of these challenging fire events being due to eleven selected fire types. In conclusion, of these fire types, electrical and electronic equipment, transient combustibles, and liquid fires were the most frequent of the challenging fires. The fire scenario specifics were characterized for each of the eleven selected types and then related to existing fire experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Secondary Frequency Regulation from Variable Generation through Uncertainty Decomposition: An Economic and Reliability Perspective

The rapid integration of variable generation (VG), such as photovoltaics (PV), necessitates an increase in the secondary frequency regulation (SFR) to handle the system intra-dispatch interval imbalance because of VG's variation. Although PV has the control capability to provide SFR, the main challenge is in guaranteeing the delivery of PV's energy and SFRs capacities in the automatic generation control (AGC) within each dispatch interval considering its uncertainty. This paper proposes a deliverable VG SFR provision model with endogenous VG's power uncertainty decomposition. First, the uncertainty of VG is decomposed using distributionally robust chance constraints through which the deliverable SFR provision is guaranteed. Next, the intra-interval frequency response of PV's SFR is validated with a user defined AGC model including the PV power plant. Finally, the economic benefits and the reliability improvements such as the frequency deviation reduction with PV providing SFRcan be integrated and evaluated. The proposed model is tested in a modified 18-bus system and in the Western Electricity Coordinating Council test system. Results demonstrate that with the deliverable SFR from PV, the system cost and the frequency reliability can be improved simultaneously. PV's SFR performance can be guaranteed with the proposed model.

14 SOLAR ENERGY↗
DOE OSTI2022

HPC Analytics of Fused Thermal Plants Data to Optimize Operating Envelope

In this project, ORNL extensively reviewed the ORAP RAM data, and it guided us to develop machine learning models that can predict time to next failures and forecast failure trends, which will be useful for optimizing power plant operation strategies. More specifically, we trained multiple random forest models and evaluated the model accuracy to validate with 10+ years of historical data. In addition, we implemented a web-based graphical user interface system for the models to show how our models can be used in more intuitive ways. This proof of concept allowed exploration of model use with power plant operators in mind. Developed machine learning models will be helpful for managing risks, planning maintenance and operation, ultimately reducing the down time and increasing the service hours. For future work, there are several interesting research topics including but not limited to model enhancement, creating synergy with traditional failure modeling approaches, and data-driven actionable recommendation and suggestions.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Open Source High Fidelity Modeling of a Type-5 Wind Turbine Drivetrain

The increasing integration of renewable energy resources in evolving bulk power system (BPS) is impacting the system inertia. Type-5 wind turbine generation has the potential to behave like a traditional synchronous generator and can help mitigate the impact on system inertia. A hydraulic torque converter (TC) and gearbox with torque limiting feature are integral parts of a Type-5 wind turbine unit. A high fidelity model of Type-5 wind turbine drivetrain is not openly and widely available for grid integration and transient stability studies. This hinders appropriate assessment of Type-5 wind power plant’s contribution to bulk grid resilience. This work develops and validates a TC model based on those generally used in automobile’s transmission system. Moreover, the concept of torsional coupling is leveraged to integrate the TC and gearbox system dynamics. The entire integrated model will be open sourced and publicly available for grid integration studies.

17 WIND ENERGY↗
DOE OSTI2023

Open Source High Fidelity Modeling Of A Type-5 Wind Turbine Drivetrain

The purpose of this code is to provide a high-fidelity model of Type-5 wind turbine drivetrain for grid integration and transient stability studies. The increasing integration of renewable energy resources in evolving bulk power system (BPS) is impacting the system’s inertia. Type-5 wind turbine generation has the potential to behave like a traditional synchronous generator and can help mitigate the aforementioned impact. A hydraulic torque converter (TC) and gearbox with torque limiting feature are integral parts of a Type-5 wind turbine unit. A generic and high-fidelity model of a Type-5 wind turbine drivetrain is not openly and widely available for grid integration and transient stability studies. This hinders appropriate assessment of Type-5 wind power plant's contribution to the BPS resilience. This work develops and validates a TC model based on those generally used in automobile's transmission system.

Hussain, Tanveer↗
DOE OSTI2024

Validation Assessment of Turbulent Reacting Flow Model Using The Area-Validation Metric on Medium-Scale Methanol Pool Fire Results

Accident analysis and ensuring power plant safety are pivotal in the nuclear energy sector. Significant strides have been achieved over the past few decades regarding fire protection and safety, primarily centered on design and regulatory compliance. Yet, after the Fukushima accident a decade ago, the imperative to enhance measures against fire, internal flooding, and power loss has intensified. Hence, a comprehensive, multilayered protection strategy against severe accidents is needed. Consequently, gaining a deeper insight into pool fires and their behavior through extensive validated data can greatly aid in improving these measures using advanced validation techniques. A model validation study was performed at Sandia National Laboratories in which a 30-cm diameter methanol pool fire was modeled using the SIERRA/Fuego turbulent reacting flow code. This validation study used a standard validation experiment to compare model results against, and conclusions have been published. The fire was modeled with a Large Eddy Simulation (LES) turbulence model with subgrid turbulent kinetic energy closure. Combustion was modeled using a strained laminar flamelet library approach. Radiative heat transfer was accounted for with a model utilizing the gray-gas approximation. In the present study, additional validation analysis is performed using the area validation metric (AVM). These activities are done on multiple datasets involving different variables and temporal/spatial ranges and intervals. In conclusion, the results provide insight into the use of the area validation metric on such temporally varying datasets and the importance of physics-aware use of the metric for proper analysis.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Comparison of modular analytical wake models to the Lillgrund wind plant

Efficient and accurate wake models are required for wind plant performance modeling and the suite of engineering processes that support wind plant layout, control, and monitoring. Although many analytical and engineering wake models with low computational costs have been proposed, their ability to represent the power production of large wind plants in a wide range of atmospheric conditions is not completely understood. The following validation study reviews the underlying theory for analytical wake models, outlines quality control procedures for observational data, and compares model results with observational data from the Lillgrund Wind Plant. Lillgrund makes a valuable case study for wake modeling because of its regular arrangement and the relatively close spacing of constituent wind turbines, which lead to regular and significant wake interactions within the wind plant and the development of deep array flow conditions. Formulations for the velocity deficit, wake-added turbulence, and wake superposition methods are considered in a modular sense, yielding many possible configurations to represent wind turbine wakes, of which seven are examined in detail. Velocity deficit models that account for flow conditions in the near wake are better able to reproduce power production for wind turbines in the transitional region of the wind plant, where wind turbines experience as many as five wakes from upstream turbines. Finally, in the deep array, where power production reaches asymptotic values and wake statistics become quasi-periodic, wake superposition schemes become the largest driver in error reduction; using the linear or maximum wake superposition methods can reduce the relative root mean square error by as much as 40% in the deep array.

17 WIND ENERGY↗
DOE OSTI2022

Validation of MPACT BWR capabilities against critical experiments

Over the years, significant validation work for the neutronics code MPACT has been performed against zero-power critical benchmarks and measured data from operating nuclear power plants. As a part of the Modeling and Analysis of Exelon BWRs for Eigenvalue and Thermal Limits Predictability project, new validation efforts relevant to boiling water reactor (BWR) core applications have been performed. This paper presents the results of the critical experiment portion of the BWR validation efforts for MPACT. The Kritz-4 experiments and IPEN/MB-01 BWR-relevant configurations are modeled with MPACT. The Kritz-4 BWR critical experiments were performed at both cold and hot conditions, which is a unique feature among other critical experiment facilities. The MPACT results with linear source (LS) Method of Characteristics (MOC) have very good agreement with the measured criticality. When using the 60-group BWR library with LS and P2 scattering, the maximum k{sub eff} error is 109 pcm, and the average cold-hot bias is only -0.11 pcm/K. The fission rate distributions of MPACT are also verified with Serpent calculations. For the IPEN facility, two configurations are considered: one with a large central void, and one with a cruciform control rod. The MPACT k{sub eff} errors are less than 50 pcm for all IPEN problems. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

RE-INTEGRATE EMT Simulation Software: Graph Convolutional Network for Sparse Matrix Pattern Detection

The increasing complexity of power networks, driven by proliferation of inverters, presents analytical challenges that simplified models often fail to capture, necessitating Electromagnetic Transient (EMT) simulations. EMT models are represented as discretized differential-algebraic equations (DAEs), forming a linear system Ax = b that is computationally intensive to solve. Due to inherent sparsity of adjacency matrix A, distinct patterns emerge that, when accurately identified, enable efficient solver selection to minimize computation time. However, identifying ideal pattern is complicated by numerous reordering algorithms and limited structural insights. To address this, we introduce a Graph Convolutional Network (GCN) model for classifying sparse matrix patterns common in power system analysis. The model, achieving 96% test accuracy, is validated using PV plant models of 125 MW capacities connected to New England 39-bus transmission system (TS), and further scaled to a 4,992-bus network with 384 PV plants, yielding 191, 616 × 191, 616 sized A matrix. For all cases, the GCN model accurately identifies the matrix’s intrinsic sparse pattern, demonstrating its potential to enhance solver performance in EMT analysis.

Hossain, Md Rifat [Florida International Universit↗
DOE OSTI2022

OVERVIEW OF PARTICLE DEPOSITION MODELS FOR SPENT NUCLEAR FUEL STORAGE SYSTEMS

Deposition models were built to evaluate contaminant deposition on spent nuclear fuel (SNF) canisters. The primary contaminant of concern is chloride, which is dispersed in the atmosphere and then deposits onto the canisters. During dry storage, the primary degradation process is likely to be Chloride Induced Stress Corrosion Cracking (CISCC) at the heat-affected zones of the canister welds. It is known that stainless steel canisters are susceptible to CISCC; however, the rate of chloride deposition onto the canisters is poorly known, based on sparse field data from a small number of sites. The models presented in this study could be useful for determining the rate of deposition on the canisters and the likelihood of CISCC to help with SNF canister ageing management. The deposition models were developed with the commercial computational fluid dynamics (CFD) code STAR-CCM+. Various deposition mechanisms were considered and incorporated into the models, and a sensitivity study was conducted to determine the most important mechanisms for deposition within a SNF storage system. The models included both a vertical and horizontal configuration storage system: NAC International’s Modular, Advanced Generation, Nuclear All-purpose STORage System (MAGNASTOR®) and a NUHOMS® horizontal storage module respectively. The resulting canister deposition on the horizontal canister is visually compared with inspection data taken onsite at the Calvert Cliffs Nuclear Power Plant. These models are preliminary, and development of the models will continue. Future validation exercises are currently being planned, including the Canister Deposition Field Demonstration (CDFD) effort funded by U.S. Department of Energy office of Nuclear Energy office of Spent Fuel Waste Science and Technology. The goal of the modeling presented is to demonstrate a potential modeling technique that could be used to plan and inform SNF canister ageing management programs with predictive models for the timing and occurrence of canister CISCC.

Suffield, Sarah R.↗
DOE OSTI2022

Multi-Kernel Adaptive Support Vector Machine for Scalable Predictive Maintenance

Application of data-driven solutions across an industry is challenging, since the data are often stored locally, and increasing privacy and security concerns restrict access to the data. In addition, it is highly unlikely that all potential data patterns are captured in a single data source. Because it is highly unlikely that all potential data patterns are captured in a single data source, machine learning (ML) models developed from a single source cannot be robust enough. An alternative is to train the ML model at each source and develop a distributed knowledge discovery and aggregation approach to build global knowledge. In this paper, we develop and demonstrate a distributed ML model, federated transfer learning (FTL), using a multi-kernel-based adaptive support vector machine (MK-A-SVM). For federated learning (FL), the multi-kernel (MK) approach enables feature-specific model aggregation under data heterogeneity; whereas for transfer learning (TL) the adaptive model enables utilization of an aggregated model from a different task. The proposed approach is validated using nuclear power plant (NPP) vertical motor-driven pump data to predict the health condition of vertical motor-driven pumps as an anomaly detection. The efficiency of the proposed approach is also quantified and compared with neural network.

42 ENGINEERING↗
DOE OSTI2021

Golden Eagle Behavioral Modeling Enabled by High-Fidelity Atmospheric Models

The DOE-funded Golden Eagle Behavioral Modeling project combines knowledge of eagle behavior, eagle telemetry data, high-fidelity atmospheric and wind power plant flow models, and machine learning to develop tools that predict behavior and risk around wind power plants. This presentation provides background on the project and modeling approaches, as well as recent modeling and validation results from the region around the Top of the World wind plant in Wyoming.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2023

Component level modeling of materials degradation for insights into operational flexibility of Existing Coal Power Plants

Increasingly, coal-fired power plants are required to balance power grids by compensating for the variable electricity supply from renewable energy sources. Fossil-fueled power plants, originally designed to be base loaded, will increasingly need to operate on a load following or cyclic basis. This demanding requirement for operational flexibility needs insights into accelerated material degradation arising due to the harsh operating conditions (e.g., fatigue, early oxide exfoliation due to stresses) along with current damage mechanisms (fireside corrosion, creep and erosion) observed in service. Our research objective is to develop component level modeling toolkit for materials-based degradation for two key mechanisms that can accelerate with cyclic operations. In more detail, this includes the fireside corrosion/steam oxidation/erosion/creep/fatigue of superheaters/reheaters and steam pipework and also the water droplet erosion/ fatigue of last stage steam turbine blades degradation mechanisms, that demand routine and sometimes unplanned maintenance and repair. The innovation is in developing a computational fluid dynamics/finite element (CFD/FE) modeling toolkit for the component level models of the boilers and low-pressure steam turbines in coal power plants that can tackle multidisciplinary failure mechanisms occurring concurrently for extreme environment materials. Lifetime assessment in such environments also needs to account for the unit-specific analyses, operational history and fuel feedstock; this can only be obtained by destructive analysis of components. This, in turn, enables validation of the model toolkits utilizing service feedback data, improving the probability of time/temperature dependent life prediction.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Wind Loading on CSP Collectors

The project significantly enhanced the community's understanding of the fundamental physics drivers underlying the wind-loading experienced by concentrating solar power (CSP) collector structures (i.e., parabolic troughs and heliostats) as well as their support structures. This project had two overarching objectives: (1) detailed measurements to characterize the prevailing wind conditions and resulting operational loads on collector structures, and (2) development and validation of a computationally efficient, high-fidelity modeling tool capable of predicting wind-loading in deep-array installations. Over three years, we conducted comprehensive at-scale field measurements of the atmospheric turbulent wind conditions, and the resulting wind loads on parabolic troughs and heliostats. Two at-scale measurement campaigns yielded first-of-its-kind, high-resolution, long-term datasets that are used to characterize the complex flow field and wind loading on parabolic-troughs and heliostats in operational power plants. The high-resolution measurements collected during these campaigns were used to validate the high-fidelity computational models developed at NREL. These open-source computationally efficient models were shown to be accurate in predicting wind-driven loads on collectors without the need for a large supercomputer.

14 SOLAR ENERGY↗
DOE OSTI2025

Modeling receiver flux of commercial power tower concentrating solar power plants using ray tracing: a round-robin comparison of SolTrace, Solstice, and TieSOL

This study presents a multi-stage, cross-validation comparison of three software packages for Monte Carlo ray tracing (MCRT) applied to central tower concentrating solar power (CSP) systems. The three packages evaluated are: (1) SolTrace, an open-source tool developed by the National Renewable Energy Laboratory (NREL); (2) Solstice, an open-source program created by CNRS-PROMES and Meso-Star, with enhancements for CSP applications (called solsticepy) from the Australian National University; and (3) TieSOL, a commercial software developed by Tietronix. This investigation extends previous ray tracing comparisons by incorporating models of multi-facet heliostats within a commercial-scale solar field, taking into account zoned focal lengths and canting configurations. Receiver flux distributions were compared across the tools using a series of case studies, including single-heliostat scenarios, isolated blocking situations, and comprehensive full-field simulations. The case studies were designed to diagnose differences across the models at varying levels of complexity, and to identify and resolve discrepancies as additional parameters were introduced. Key factors examined in the analysis include sun positions, heliostat location, facet and canting focus, and aimpoint strategies. The comparison aims to improve the accuracy and reliability of these tools while providing benchmark cases for validating future optical modeling tools.

14 SOLAR ENERGY↗