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DOE OSTI2022

Multi-modal Approach to Modeling Creep Deformation in Nickel-base Superalloy

This study developed the multi-modal modeling approaches to elucidate the physical and overall mechanical properties of two complex alloy systems of Ni-based Superalloys, namely Haynes 282 and Inconel 740. The overall strategies employed systematically in different scales of modeling are based on the common structural framework of the multi phases that made up the Ni-based Superalloys, namely the matrix phase of the Gamma ($\gamma$) phase which is embedded with ordered Gamma prime ($\gamma$’) precipitates and decorated with the dispersions of mostly carbide precipitates. With this in mind, we had set up the modeling platforms to incorporate a wide range of scales, starting from electronic structures to the continuum level via the crystal plasticity model.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2011

Joint inversion of marine seismic AVA and CSEM data using statistical rock-physics models and Markov random fields: Stochastic inversion of AVA and CSEM data

Joint inversion of seismic AVA and CSEM data requires rock-physics relationships to link seismic attributes to electrical properties. Ideally, we can connect them through reservoir parameters (e.g., porosity and water saturation) by developing physical-based models, such as Gassmann’s equations and Archie’s law, using nearby borehole logs. This could be difficult in the exploration stage because information available is typically insufficient for choosing suitable rock-physics models and for subsequently obtaining reliable estimates of the associated parameters. The use of improper rock-physics models and the inaccuracy of the estimates of model parameters may cause misleading inversion results. Conversely, it is easy to derive statistical relationships among seismic and electrical attributes and reservoir parameters from distant borehole logs. In this study, we develop a Bayesian model to jointly invert seismic AVA and CSEM data for reservoir parameter estimation using statistical rock-physics models; the spatial dependence of geophysical and reservoir parameters are carried out by lithotypes through Markov random fields. We apply the developed model to a synthetic case, which simulates a CO{sub 2} monitoring application. We derive statistical rock-physics relations from borehole logs at one location and estimate seismic P- and S-wave velocity ratio, acoustic impedance, density, electrical resistivity, lithotypes, porosity, and water saturation at three different locations by conditioning to seismic AVA and CSEM data. Comparison of the inversion results with their corresponding true values shows that the correlation-based statistical rock-physics models provide significant information for improving the joint inversion results.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Supplying materials needed for grain growth characterizations of nano-grained UO 2

This activity is supported by the US Nuclear Energy Advanced Modeling and Simulation (NEAMS) Fuels Product Line (FPL) and aims at providing experimental data for the validation of the mesoscale simulation code MARMOT. MARMOT is a mesoscale multiphysics code that predicts the coevolution of microstructure and properties within reactor fuel during its lifetime in the reactor. It is an important component of the Moose-Bison-Marmot (MBM) code suite that has been developed by Idaho National Laboratory (INL) to enable next generation fuel performance modeling capability as part of the NEAMS Program FPL. In order to ensure the accuracy of the microstructure based materials models being developed within the MARMOT code, extensive validation efforts must be carried out. In this report, we summarize our preliminary synchrotron radiation experiments at APS to determine the grain size of nanograin UO 2 . The methodology and experimental setup developed in this experiment can directly apply to the proposed in-situ grain growth measurements. The investigation of the grain growth kinetics was conducted based on isothermal annealing and grain growth characterization as functions of duration and temperature. The kinetic parameters such as activation energy for grain growth for UO 2 with different stoichiometry are obtained and compared with molecular dynamics (MD) simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Synchrotron characterization of nanograined UO 2 grain growth

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Total Dissolved Gas Prediction and Optimization in RIVERWARE

Management and operation of dams within the Columbia River Basin (CRB) provides the region with irrigation, hydropower production, flood control, navigation, and fish passage. These various system-wide demands can require unique dam operations that may result in both voluntary and involuntary spill, thereby increasing tailrace levels of total dissolved gas (TDG) which can be fatal to fish. Appropriately managing TDG levels within the context of the systematic demands requires a predictive framework robust enough to capture the operationally related effects on TDG levels. Development of the TDG predictive methodology herein attempts to capture the different modes of hydro operation, thereby making it a viable tool to be used in conjunction with a real-time scheduling model such as RiverWare. The end result of the effort will allow hydro operators to minimize system-wide TDG while meeting hydropower operational targets and constraints. The physical parameters such as spill and hydropower flow proportions, accompanied by the characteristics of the dam such as plant head levels and tailrace depths, are used to develop the empirically-based prediction model. In the broader study, two different models are developed a simplified and comprehensive model. The latter model incorporates more specific bubble physics parameters for the prediction of tailrace TDG levels. The former model is presented herein and utilizes an empirically based approach to predict downstream TDG levels based on local saturation depth, spillway and powerhouse flow proportions, and entrainment effects. Representative data collected from each of the hydro projects is used to calibrate and validate model performance and the accuracy of predicted TDG uptake. ORNL, in conjunction with IIHR - Hydroscience & Engineering, The University of Iowa, carried out model adjustments to adequately capture TDG levels with respect to each plant while maintaining a generalized model configuration. Validation results indicate excellent model performance with coefficient of determination values exceeding 92% for all sites. This approach enables model extension to an increasingly wider array of hydropower plants, i.e., with the proper data input, TDG uptake can be calculated independent of actual physical component design. The TDG model is used as a module in the systematic optimization framework of RiverWare, a river and reservoir modeling tool used by federal agencies, public utility districts, and other dam owners and operators to forecast, schedule, and manage hydropower assets. The integration and testing of the TDG module within RiverWare, led by University of Colorado's Center for Advanced Decision Support for Water and Environmental Systems (CADSWES), will allow users to generate optimum system schedules based on the minimization of TDG. Optimization analysis and added value will be quantified as system wide reductions in TDG achieved while meeting existing hydropower constraints. Future work includes the development of a method to predict downstream reservoir forebay TDG levels as a function of upstream reservoir tailrace TDG values based on river hydrodynamics, hydro operations, and reservoir characteristics. Once implemented, a holistic model that predicts both TDG uptake and transport will give hydropower operators valuable insight into how system-wide environmental effects can be mitigated while simultaneously balancing stakeholder interests.

13 HYDRO ENERGY↗

DOE OSTI2018

Rheological Evolution of Ti-Bearing Slag with Different Volume Fractions of TiN

The effect of the volume fraction of TiN (0 to 4.27 vol pct) on the rheological behaviors of Ti-bearing slag, including the viscosity and the constitutive equation, was investigated using a Brookfield rheometer at 1773 K. With the increasing TiN, the viscosity and the shear stress increased substantially, and the slag is converted from Newtonian to non-Newtonian. When the slag samples were dispersed with TiN (1.00 to 4.27 vol pct), the measured viscosities were greater than those estimated by the Einstein–Roscoe equation, and the complex internal structure was suggested to be responsible for the extra enhancement of the viscosity beyond that caused by TiN itself. The viscosity curves of the slag samples containing TiN (1.00 to 4.27 vol pct) were divided into three regions depending on the critical shear rate and the initial shear rate. In the Newtonian region, the apparent volume fraction of TiN was defined and used to develop a semiempirical model based on the Einstein–Roscoe equation to describe the viscosity. In the two non-Newtonian regions, the shear rate, the yield stress, and the viscosity differences between the non-Newtonians regions and the Newtonian regions were applied in developing the viscosity models.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Validation of Photovoltaic Modeling Tool HelioScope Against Measured Data

HelioScope is a cloud-based solar design software developed by Folsom Labs to model arrays based on physical design. Folsom Labs received a grant from the Department of Energy for the National Renewable Energy Laboratory to validate the performance of HelioScope's simulation engine against measured PV system performance. This study builds upon previous validation work by NREL of multiple other PV modeling tools, using the same fixed-tilt systems with available system specifications. HelioScope designs were set up using the same system specifications and concurrent weather data as were used in previous studies. The predicted performance results from the simulations were compared with measured data and to performance predictions by other PV modeling tools.

14 SOLAR ENERGY↗

DOE OSTI2021

NEAMS TH CRAB

The MOOSE framework is a library designed to make it straightforward for physicists and engineers to model partial differential equations using finite element and finite volume methods. Finite elements have been a part of the framework library since MOOSE's inception over a decade ago. Initial finite volume capability, however, was added only as recently as May of 2020. Since that time, significant work has been conducted to develop fluid modeling capability based on the finite volume method. Much of this work has taken place in MOOSE's navier_stokes module and in the NEAMS program's coarse mesh CFD code Pronghorn. We report here on development in MOOSE and Pronghorn of incompressible, porous incompressible, and weakly compressible finite volume simulation capabilities and their application to modeling of MSR and FHR advanced reactor concepts.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network

A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Report on SNL RCBC control options

The attractive performance of the S-CO 2 recompression cycle arises from the thermo-physical properties of carbon dioxide near the critical point. However, to ensure efficient operation of the cycle near the critical point, precise control of the heat removal rate by the Printed Circuit Heat Exchanger (PCHE) upstream of the main compressor is required. Accomplishing this task is not trivial because of the large variations in fluid properties with respect to temperature and pressure near the critical point. The use of a model-based approach for the design of a robust feedback regulator is being investigated to achieve acceptable control of heat removal rate at different operating conditions. A first step in this procedure is the development of a dynamic model of the heat exchanger. In this work, a one-dimensional (1-D) control-oriented model of the PCHE was developed using the General Plant Analyzer and System Simulator (GPASS) code. GPASS is a transient simulation code that supports analysis and control of power conversion cycles based on the S-CO 2 Brayton cycle. This modeling capability was used this fiscal year to analyze experiment data obtained from the heat exchanger in the SNL recompression Brayton cycle. The analysis suggested that the error in the water flowrate measurement was greater than required for achieving precise control of heat removal rate. Accordingly, a new water flowmeter was installed, significantly improving the quality of the measurement. Comparison of heat exchanger measurements in subsequent experiments with code simulations yielded good agreement establishing a reliable basis for the use of the GPASS PCHE model for future development of a model-based feedback controller.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

A guideline to document occupant behavior models for advanced building controls

The availability of computational power, and a wealth of data from sensors have boosted the development of model-based predictive control for smart and effective control of advanced buildings in the last decade. More recently occupant-behavior models have been developed for including people in the building control loops. However, while important objectives of scientific research are reproducibility and replicability of results, not all information is available from published documents. Therefore, the aim of this paper is to propose a guideline for a thorough and standardized occupant-behavior model documentation. For that purpose, the literature screening for the existing occupant behavior models in building control was conducted, and the occupant behavior modeling processes were studied to extract practices and gaps for each of the following phases: problem statement, data collection, and preprocessing, model development, model evaluation, and model implementation. Here, the literature screening pointed out that the current state-of-the-art on model documentation shows little unification, which poses a particular burden for the model application and replication in field studies. In addition to the standardized model documentation, this work presented a model-evaluation schema that enabled benchmarking of different models in field settings as well as the recommendations on how OB models are integrated with the building system.

Building control↗

DOE OSTI2022

On the mechanism of marine fouling-prevention performance of oil-containing silicone elastomers

For many decades, silicone elastomers with oil incorporated have served as fouling-release coating for marine applications. In a comprehensive study involving a series of laboratory-based marine fouling assays and extensive global field studies of up to 2-year duration, we compare polydimethylsiloxane (PDMS) coatings of the same composition loaded with oil via two different methods. One method used a traditional, one-pot pre-cure oil addition approach (o-PDMS) and another method used a newer post-cure infusion approach (i-PDMS). The latter displays a substantial improvement in biofouling prevention performance that exceeds established commercial silicone-based fouling-release coating standards. We interpret the differences in performance between one-pot and infused PDMS by developing a mechanistic model based on the Flory–Rehner theory of swollen polymer networks. Using this model, we propose that the chemical potential of the incorporated oil is a key consideration for the design of future fouling-release coatings, as the improved performance is driven by the formation and stabilization of an anti-adhesion oil overlayer on the polymer surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Modeling of Liquid Hydrogen Tank Cooled with Para-Orthohydrogen Conversion

With the accelerating development of liquid-hydrogen storage facilities, the problem of boil-off hydrogen losses becomes very important. A promising method to reduce these losses is to utilize the endothermic para-orthohydrogen conversion of vented hydrogen, which can effectively decrease heat loads on a hydrogen tank. To model such a process, a hybrid computational model has been developed, based on the application of computational fluid dynamics for an ullage space, where knowledge of thermal stratification is important, and reduced-order models for other system elements. The simulation results for a spheroidal tank in selected conditions indicated a 10–25% reduction of boil-off losses due to cooling produced by vented hydrogen undergoing para-ortho-conversion.

08 HYDROGEN↗

DOE OSTI2025

GUI-Enabled Radiative Modeling Suite for Particle-Based Simulations

Methods and lessons learned in developing GUI-enabled radiative modeling suite for particle-based simulations including accelerated MCRT, distribution factor surrogate-models, and volumetric participating media

DEM↗

DOE OSTI2022

Elevated temperature nanoscratch of Inconel 617 Superalloy

Inconel 617 superalloy is a main candidate to be used for mechanical and tribo-components in high temperature helium-cooled reactors. Recent findings show that it grows a unique surface oxide, especially under high temperature helium with distinct wear, friction, and contact properties. Here, this study reports the elevated temperature nanoscratch behavior of Inconel 617 and further utilizes it to understand the effect of temperature on contact friction constituent contributors, adhesion and plowing at small scales. Inconel 617 is aged in high temperature helium, and consequently, the total kinetic friction coefficient of the alloy surface oxide is obtained in temperatures ranging from 25 °C to 400 °C. A finite element model is developed and validated based on the experimental results. The model is then utilized along with previously established techniques to determine the adhesion and plowing components of the friction coefficient. At small scale, the experimental results show that with increasing temperature the friction coefficient increases. It was inferred that this increase is mainly due to the increased contribution of plowing friction at high levels of deformation.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Increasing Mosquito Abundance Under Global Warming

Mosquitoes are a key virus vector that poses significant health threats globally, affecting 700 million individuals and causing 1 million deaths annually. Accurately predicting mosquito abundance and dispersion remains a challenge. Complex interactions between mosquito dynamics and various environmental factors, notably hydrology, contribute to this challenge. Existing models typically focus on precipitation and temperature and often overlook further impacts of hydrological variables within mosquito modeling. In this study, we developed an artificial intelligence‐based model for mosquito dynamics, explicitly accounting for different hydrological variables, such as precipitation, soil moisture and streamflow. Using Toronto, Canada, as a case study, we identified causal relationships between changes in mosquito populations, hydrological factors, vegetation (e.g., leaf area index), and climate variables (e.g., daylight length, precipitation, and temperature). We embedded these relationships into a Long Short‐Term Memory (LSTM) Neural Network Model capable of accurately detecting mosquito dynamics across annual, seasonal, and monthly time scales. The LSTM is able to explain, on average, approximately 40% of the variance in the observed mosquito abundance data. Using the calibrated model, we predicted that the summer season mosquito abundance would increase by ∼16% and ∼19% under an intermediate greenhouse emission scenario, Shared Socioeconomic Pathway (SSP) 2–4.5, and a high greenhouse emission scenario, SSP5‐8.5, respectively. We expect that this model can serve as a valuable tool and inform science‐based decisions affecting mosquito dynamics and public health. It can also build a foundation for future risk analysis at the regional and larger scales.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Filamentary surface plasma discharge flow length and time scales

Nanosecond surface dielectric barrier discharges (ns-SDBDs) are a class of plasma actuators that utilize a high-voltage pulse of nanosecond duration between two surface-mounted electrodes to create an electrical breakdown of air, along with rapid heating. These actuators usually produce multiple filaments when operated at high pulse frequencies, and the rapid heating leads to the formation of shock waves and complex flow fields. In this work we replicate a single filament of the ns-SDBDs and characterize the induced flow using velocity measurements from particle image velocimetry and density measurements from background-oriented schlieren. The discharge is produced by a high voltage electrical pulse between two copper electrodes on an acrylic base. A hot gas kernel characterizes the flow field formed close to the electrodes that expands and cools over time and a vortex ring that propagates away from the surface while entraining cold ambient fluid. The gas density deficit inside the kernel displays a power-law decay over time. Based on the observations, we develop a simplified theoretical model based on vortex-driven cooling and perform a scaling analysis to obtain the induced flow length and time scales. The results show that the cooling process's time scales correspond to a circulation-based time scale of the vortex ring, and the length scale of the kernel corresponds to the vortex ring radius. Furthermore, these findings can guide the choice of optimal filament spacing and pulse frequencies in the design, deployment, and operation of ns-SDBDs for flow control.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Synchronous Wind: Evaluating the Grid Impact of Inverterless Grid-Forming Wind Power Plants

Grid-forming (GFM) control of Type-3 and Type-4 wind turbine generators has attracted substantial attention in power systems research; however, the limited over-current capability of power electronics converters continues to deteriorate the grid strength of the evolving power systems. This paper develops the generic model of synchronous wind power generators, also known as Type-5 wind turbines, for power system integration studies. The Type-5 wind turbine interfaces with the electric grid through a synchronous generator; hence, its operation and consequent grid impacts are similar to a conventional power plant. Based on the developed model, a Type-5 wind power plant is integrated into the IEEE 14-bus test bed to evaluate its control and operation with a bulk power system. Finally, the stability properties of Type-5 wind turbine generators are compared with Type-3 wind turbines in GFM control mode through impedance characterization.

grid strength↗