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DOE OSTI2023

An Efficient Three-Dimensional CFD-Based Numerical Wave Tank for a Wave Energy Converter in Extreme Irregular Waves

A numerical wave tank approach for computational fluid dynamics (CFD) modelling of an extreme irregular seastate is presented. The technique couples a potential flow solution with a CFD solver for more efficient numerical predictions. This method has recently become attractive both for the research community and the industry working with offshore structures. The model is used to determine the response of a submerged pressure differential wave energy converter (WEC) in a fully nonlinear irregular wave condition using the high-fidelity CFD code, STAR-CCM+. Potential flow based numerical models are commonly used to predict motions and performance of wave energy converters. Wave kinematics can deviate from potential flow predictions for extreme wave conditions; the excitation loads on an absorber can also be increasingly influenced by viscous effects, not predicted by potential flow engineering level models. In these extreme conditions, a Reynolds-averaged Navier-Stokes CFD model can better predict motions and loads for a WEC. Long time series with varying random seed numbers can be used to identify singular extreme wave events from a stochastic irregular sea state. This approach simulates a more realistic wave series for a given sea state than a regular wave or a focused wave. However, it is computationally infeasible to run these long time series for three-dimensional (3D) CFD simulations. In this work, two-dimensional (2D) CFD simulations with a long domain allow the full development of an extreme nonlinear wave condition. The results are used to identify extreme events from a 50-year storm condition for the PacWave site off the coast of Oregon. A relatively short time window including this extreme event is then mapped to a 3D simulation using a user defined wave methodology. Convergence studies for domain length, wave forcing lengths, and time before the extreme event were conducted.

CFD↗

DOE OSTI2023

An Efficient Three-Dimensional CFD-Based Numerical Wave Tank for a Wave Energy Converter in Extreme Irregular Waves: Preprint

A numerical wave tank approach for computational fluid dynamics (CFD) modelling of an extreme irregular seastate is presented. The technique couples a potential flow solution with a CFD solver for more efficient numerical predictions. This method has recently become attractive both for the research community and the industry working with offshore structures. The model is used to determine the response of a submerged pressure differential wave energy converter (WEC) in a fully nonlinear irregular wave condition using the high fidelity CFD code, STAR-CCM+. Potential flow based numerical models are commonly used to predict motions and performance of wave energy converters. Wave kinematics can deviate from potential flow predictions for extreme wave conditions; the excitation loads on an absorber can also be increasingly influenced by viscous effects, not captured in engineering level models. In these extreme conditions, a Reynolds-averaged Navier-Stokes CFD model can better predict motions and loads for a WEC. Long time series with varying random seed numbers can be used to identify singular extreme wave events from a stochastic irregular sea state. This approach simulates a more realistic wave series for a given sea state than a regular wave or a focused wave. However, it is computationally infeasible to run these long time series for three-dimensional (3D) CFD simulations. In this work, two-dimensional (2D) CFD simulations with a long domain allow the full development of an extreme nonlinear wave condition. The results are used to identify extreme events from a 50-year storm condition for the PacWave site off the coast of Oregon. A relatively short time window including this extreme event is then mapped to a 3D simulation using a user defined wave methodology. Convergence studies for domain length, wave forcing lengths, and time before the extreme event were conducted.

cfd↗

DOE OSTI2024

THERMAL MODELING OF HANFORD CESIUM AND STRONTIUM CANISTERS DURING SIMULATED LOADING

A computational fluid dynamics (CFD) model was built to simulate planned testing of heater assemblies within a canister and overpack for the Hanford Lead Canister (HLC) project. The HLC is a canister storage system that will contain heaters to simulate the decay heat of nuclear material and provide the canister storage system with environmental conditions equivalent to the operating conditions on a dry storage pad. The HLC will be equipped with long-term data collection and monitoring systems to provide an early warning of corrosion, pitting, cracking, or other signs of canister degradation that might threaten the integrity of the containment boundary over the potentially long term of dry storage. An important part of the HLC development is to make pretest numerical predictions for the behavior of the heated canister during the simulated radiolytic decay heat testing, which simulates the dry storage system during loading operations. The simulated radiolytic decay heat test is planned for mid-2024 in a configuration that includes the heater assembly, overpack, and canister, but with the lids removed to allow loading cesium and strontium capsules into the canister. One of the goals of the test is to evaluate the thermal behavior of the canister and overpack assembly in the ambient air of the test facility, which will provide data critical to validating the thermal models and understanding how the HLC will perform as a system once deployed. To best approximate real-world conditions, the CFD model includes the full air volume of the mock-up truck bay the heated canister test will be performed in, enabling detailed investigation of how the heated canister affects airflow around it. Rigorous pre-deployment testing of the complete HLC cask and canister system is intended to be completed before the HLC is deployed in the 2028 timeframe. This study presents the pre-test temperature predictions of the simulated radiolytic decay heat test. A description of the heater assembly, canister, and overpack system is presented. The model was developed with the commercial CFD software STAR-CCM+. An uncertainty analysis was run with the CFD model to determine the uncertainty in the temperature predictions and provide a range over which the predicted temperatures are expected to vary. The uncertainty analysis was preformed by coupling STAR-CCM+ with the software Dakota, which provides advanced parametric analyses, including quantification of margins and uncertainty with computational models. This work is expected to provide insight into SNF canister behavior.

Carpenter-Graffy, Dina E.↗

DOE OSTI2020

Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. In this work, by defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Reduced Order Model to Predict Dispersion of Flammable Refrigerant into a Space

As the HVAC&R industry mobilizes to deploy more low-GWP refrigerants, relevant standards are being continually reviewed and updated. Those include the general safety standards ISO 5149 and ASHRAE 15, and the equipment standards IEC and UL. The standards systematically set the allowable maximum amount of refrigerant that should be used in different equipment types and different applications. To do so, they rely on predictions of how a leaked refrigerant mass will disperse into a space. Dispersion characteristics, such as total flammable volume and its residence time, determine the risk associated with the presence of the flammable refrigerant. The standards have included provisions for the use of flammable refrigerants for approximately two decades. They relied on limited analytical analyses and test cases in their development. Dispersion of a refrigerant into a space is complex. Computational fluid dynamics (CFD) are the most accurate in predicting a given problem. However, CFD is computationally expensive and requires specialized expertise and resources and is not suitable for use by standards development working group as prediction tool. This paper presents the development of a reduced order model (ROM) that predicts the key dispersion characteristics relevant to the dispersion of a leaked refrigerant into a space for any combination of input variables. The inputs are the refrigerant release height, the total released refrigerant mass and its release flow rate, the refrigerant molecular weight, the ventilation flow rate, the floor area and height of the space, recirculation air flow rate, and the tightness of the space. The outputs are histograms of volume fraction of the room in prescribed concentration bins and the total mass of the refrigerant in each bin normalized by the total refrigerant charge at 13 prescribed simulation time stamps between 1 and 900 seconds. The ROM is constructed from a set of CFD simulations with carefully chosen combinations of input parameters. The selection if done using a multidimensional sparse grid which is a generalization of the classical tensor approach but offers additional flexibility and thus can be more carefully tuned towards a specific model. The tuning is done to improve the accuracy, measured in the difference between the output values of the ROM and the CFD model, while minimizing the computational cost, measured in number of CFD simulations which is orders of magnitude more expensive than the processing the training data.

Edwards, Dean↗

DOE OSTI2020

Development and Validation of SAM Multi-dimensional Flow Model for Thermal Mixing and Stratification Modeling

Thermal mixing and stratification in large pools or enclosures are very important phenomena that are critical to nuclear reactor safety. Because of the wide ranges of time and length scales associated with such phenomena, accurate modeling and simulation of thermal mixing and stratification remain as the key unresolved, challenging problems for reactor transient analyses. In traditional system analysis codes, simplified zero-dimensional (0-D) models are widely used for their high numerical efficiency, but they generally suffer from very limited prediction accuracies or range of applicability. Like traditional system analysis codes, the current version of the SAM code has implemented such simplified 0-D and one-dimensional mixing models. On the other hand, high-resolution Computational Fluid Dynamics (CFD) tools are often used to model complex thermal mixing and stratification phenomena. They are, however, generally numerically expensive, and they require large amounts of computational resources. It is therefore desirable to implement advanced and efficient thermal mixing and stratification modeling capabilities embedded in a system analysis code. This approach will improve the accuracy of reactor safety analyses when thermal mixing and stratification are involved, and also avoid using the large computational resources needed for high-resolution CFD analysis. Currently, with the support of the U.S. DOE Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation program, an effort has been launched to develop and implement a multi-dimensional flow model in the system analysis code SAM, and demonstrate its applications to model thermal mixing and stratification phenomena in large enclosures. The main outcomes of this research and development activity are summarized in this report, which presents an attempt to include a built-in advanced multi-dimensional flow model in a system analysis code with the focus on overcoming the simulation challenges of thermal mixing and stratification phenomena. In this report, we start with the introduction of existing SAM code capabilities to simulate thermal mixing and stratification phenomena, which is followed by a short summary of the multi-dimensional model implemented in the SAM code, including both the physical model and the Finite Element Method code implementation. In this study, two options were implemented in the SAM code to model turbulent flows: a relatively simple built-in turbulence model and an interface to accept externally computed turbulent viscosities (e.g., from a high- resolution CFD simulation). Code validation studies on this newly added capability were then carried out to compare SAM simulation results with experimental data from the SUPERCAVNA facility, which was designed to study the complex flow recirculation and thermal stratification phenomena relevant to sodium fast reactor designs. In this study, one transient and two steady-state test cases were used for code validation. Different approaches have been used to model the complex turbulence flow fields in the SUPERCAVNA facility. A highly simplified zero-equation turbulence model was first used, but it was determined that it is too simple to capture the complex turbulence flow fields in these test cases. Subsequently, the code validation continued with the use of turbulent viscosity data from high-resolution STAR-CCM+ CFD simulations to improve the accuracy of the results. Using this approach, the SAM simulation results showed very good agreement with both the SUPERCAVNA experimental data and STAR-CCM+ simulation results. In this report, we demonstrate the development, implementation, and successful validation of a multi-dimensional flow model in the SAM code, which aims to improve the simulation accuracy for complex thermal mixing and stratification phenomena. Lessons have also been learned, including that in cases where the flow fields are not well predicted by the zero-equation model, the iinput of turbulent viscosities from an external source can enhance the overall predictive capabilities needed to accurately capture complex thermal-hydraulic phenomena. Therefore, future research will be needed to further improve the code’s capabilities, such as by developing a more efficient and robust approach to capture the turbulence effects in the SAM code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Reverse Osmosis Simulation Data

This dataset consists of computational fluid dynamics (CFD) output for various spacer configurations in a feed-water channel in reverse osmosis (RO) applications. Feed-water channels transport brine solution to the RO membrane surfaces. The spacers embedded in the channels help improve membrane performance by disrupting the concentration boundary layer growth on membrane surfaces. Refer to the "Related Work" resource below for more details. This dataset considers a feed-water channel of length 150mm. The inlet brine velocity and concentration are fixed at 0.1m/s and 100kg/m3 respectively. The diameter of the cylindrical spacers is fixed as 0.3mm and six varying inter-spacer distances of 0.75mm, 1mm, 1.5mm, 2mm, 2.5mm, and 3mm are simulated. The dataset comprising the steady, spatial fields of solute concentration, velocity, and density near each spacer is placed in the folder corresponding to the spacer configuration considered. We run two sets of CFD simulations and include the outputs from both sets for each configuration: (1) with a coarser mesh, producing low-resolution (LR) data of spatial resolution 20x20, and (2) with a finer mesh, producing high-resolution (HR) data of spatial resolution 100x100. These data points can be treated as images with the quantities of interest as their channels and can be used to train machine learning models to learn a mapping from the LR images as inputs to the HR images as outputs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Developing And Scaling an OpenFOAM Model to Study Turbulent Flow in a HFIR Coolant Channel

Improving the understanding of how computational fluid dynamics (CFD) direct numerical simulations (DNS) of flows in the High Flux Isotope Reactor (HFIR) perform when run in parallel using the high performance computing (HPC) platform Summit at the Oak Ridge Leadership Computing Facility (OLCF) is of particular importance to boost the computational tools used to support HFIR conversion to low enriched fuel (LEU). Evaluation of scaling performance was driven by the increasing importance of graphics processing unit (GPU) usage in HPC, which is becoming the standard for modern supercomputers such as Summit. The desired results are to obtain a strong positive correlation between the computational resources dedicated to a problem and the relative speed-up of the simulation in comparison to a benchmark. This capability will allow substantially improvement in HFIR flow analytical capabilities, specifically when predicting turbulence properties at high Reynolds numbers. The study leverages previous simulation results performed with code PHASTA (finite element) on HPC platforms Cori (NERSC) and Theta (ALCF) [1] with computing options provided in the computing platform OpenFOAM (finite volume) at OLCF. Transitioning from PHASTA to OpenFOAM will (1) eliminate dependence on third-party software for mesh generation and manipulation, (2) reduce resource needs by employing modern architectures, and (3) build expertise for future modeling of HFIR-specific problems like heat transfer in involute geometry, entrance effects, flow structure in channel corners, and so on—all important issues when defining the available thermal margins in the transition to LEU. CPUs and GPUs differ significantly in their architecture and utilization, as discussed in the literature [2]. The most important differences are in the approach to computations and their memory. A single GPU contains a large quantity of cores, enabling it to perform with a much higher throughput than a CPU, but execution requires a different approach. GPU codes execute instructions using the Single-Instruction Multiple-Thread (SIMT) approach in which a single instruction is used for groups of threads called warps. A warp typically consists of 32 threads which must execute the same set of instructions, although on separate threads. Alternately, a CPU has far fewer cores that are much more flexible in their operation, excelling at quickly performing more complex serial computations. This is why GPUs have greater throughput when properly utilized. The second important difference is seen when comparing their memory spaces. Limited memory allocations and CPU–GPU communications cause a significant bottleneck in GPU-accelerated programs. Further study was required to properly take advantage of GPU resources. A comprehensive analysis of code performance and the model-specific features of turbulence constitutes the core of this work. In this study, a DNS simulation of HFIR channel turbulence was performed with the finite volume CFD code OpenFOAM v2112 and CUDA v11.0 on Red Hat Enterprise Linux v8.2. The OpenFOAM installation had AMGx integrated to enable GPU acceleration and utilizes the PETSc4FOAM library. The computational resources and the problem size were scaled on CPU and CPU + GPU architectures to gain a better understanding of the performance of a DNS problem on modern computing hardware. The study aimed to analyze the scaling of the code exclusively on CPUs and then to examine the scaling of the codes with GPU acceleration enabled. Scaling studies included CPU and GPU acceleration on a mesh of varying resolution to analyze the impact of problem size relative to computational resources. In the course of preparing the GPU configuration on Summit, mainly using the AMGX solvers, difficulties were encountered stemming from constant changes resulting from extensive ongoing development activities and the changing environment. This resulted in the inability to complete the GPU portion of the work. The code was compiled and tested, but production runs to assess acceleration were not performed because the used discretional compute time allocation expired as year-end approached. The Summit HPC platform is scheduled for decommissioning in 2024, making it unattractive for future use with Nvidia-based GPUs. Therefore, the work will be moved onto NERSC machines in FY24. An application was prepared and submitted, and sufficient node-hours were awarded to continue the research in the next calendar year. This report summarizes work performed thus far, which mostly focused on CPU OpenFOAM computing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Optimization of a Cyclone Using Multiphase Flow Computational Fluid Dynamics

The U.S. Department of Energy National Energy Technology Laboratory's (NETL) 50 kW th chemical looping reactor (CLR) has an underperforming cyclone, which was designed using empirical correlations. To improve the performance of this cyclone using computational fluid dynamics (CFD)-based modeling simulations, four critical design parameters including the vortex tube radius and length, barrel radius, and the inlet width and height were optimized. Here, NETL's open source multiphase flow with interphase exchange (MFiX) CFD code has been used to model a series of cyclones by systematically varying the geometric design parameters. To perform the optimization process, the surrogate modeling and sensitivity analysis followed by the optimization capability in nodeworks was used. The basic methodology for the process is to employ a statistical design of experiments (DOE) method to generate sampling simulations that fill the design space. Corresponding CFD models are then created, executed, and postprocessed. A response surface is created to characterize the relationship between input parameters and the quantities of interest (QoI). Finally, the CFD-surrogate is used by an optimization method to find the optimal design condition based on the objective and constraints prescribed. The resulting optimal cyclone has a larger diameter and longer vortex tube, a larger diameter barrel, and a taller and narrower solids inlet. The improved design has a predicted pressure drop 11 times lower than the original design while reducing the mass loss by a factor of 2.3.

42 ENGINEERING↗

DOE OSTI2021

Computational fluid dynamics modeling and analysis of silica nanoparticle synthesis in a flame spray pyrolysis reactor

Flame Spray Pyrolysis (FSP) is a method for large-scale production of nanoparticles and nanoscale powders employed in a wide range of industrial applications. Particle size and morphology are complex functions of the physicochemical phenomena occurring in the FSP reactor. An extensive study of FSP-related phenomena can be utilized to develop effective strategies for achieving desired particle size/morphology and scaling up the overall yield of an FSP system. In this work, a computational fluid dynamics (CFD) model of an FSP reactor is developed to simulate the coupling of key phenomena involved in the particle synthesis process: liquid spray breakup and evaporation, mixing, combustion, and particle formation/growth of silica nanoparticles. Herein, the particle sizes and their distributions from the CFD simulations are validated against experimental data. Subsequently, the simulations are utilized to investigate the impact of process parameters on the resultant flame dynamics and particle growth. Firstly, the CFD results show that the particle sizes are strongly correlated with the precursor concentration in the solvent. At lower precursor concentrations, the spread of the distribution is relatively insensitive to the value of the concentration. At higher concentrations, the spread is higher as the collision probability between particles is higher. Secondly, increasing the pilot flow rate increases the length of the pilot flames impacting the local ignition location of the spray flame. Lastly, it is shown that the dispersion gas flow rate strongly influences the spray flame shape. This shape can be used for control of particle growth as it helps determine the regions of high temperature and the residence time of the particles in the high temperature region enabling the design and process optimization of the FSP reactor.

42 ENGINEERING↗

DOE OSTI2020

Validation and application of a multiphase CFD model for hydrodynamics, temperature field and RTD simulation in a pilot-scale biomass pyrolysis vapor phase upgrading reactor

Accurate prediction of transport phenomena is critical for VPU reactor design, optimization, and scale-up. The current study focused on the validation and application of a multiphase CFD model within an open-source code MFiX for hydrodynamics, temperature field, and residence time distribution (RTD) simulation in a non-reacting circulating fluidized bed riser for biomass pyrolysis vapor phase upgrading (VPU). First, an Eulerian-Eulerian approach three-dimensional CFD model was employed to simulate the pilot-scale VPU riser on the supercomputer Joule. Excellent quantitative agreement between experimental and simulated results was achieved for pressure drops and temperature field in a range of operating conditions. Then the validated multiphase CFD model was applied to predict gas and solid residence time distributions (RTDs) since prediction and analysis of RTD is an important tool to study the complex multiphase flow behavior and mixing inside chemical reactors. The predictions show that solid mean residence time is 3.5 times the gas residence time; the solid RTD is more sensitive to the process gas flow rate than the solids circulation rate.

09 BIOMASS FUELS↗

DOE OSTI2022

Coupling coarse mesh CFD with fine mesh CFD for modeling for Molten Salt Reactors in the Virtual Test Bed VTB

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims at developing a simulation tool kit to accelerate the development and deployment of nuclear power technologies. NEAMS multiphysics tools have been designed to provide numerical simulation support for the design and licensing of GEN IV reactors. Pronghorn is NEAMS’s coarse-mesh computational fluid dynamics (CFD) tool, which is designed to run 3D core transients in GEN IV reactors at a reduced computational cost. To increase their accuracy, coarse-mesh CFD simulations require calibrated closure coefficients. One way of computing these coefficients is via the Nek5000, NEAMS’s high-fidelity CFD tool. This article discusses our current research lines in informing Pronghorn closure coefficients via Nek5000 to enable multiphysics simulations of the core cavity of the molten-salt fast reactor. We present an application in which Pronghorn mixing length turbulent viscosity has been calibrated from Nek5000 simulations. The resulting Pronghorn thermal-hydraulics model is then coupled to Griffin, the NEAMS neutron transport solver, to solve for the steady-state configuration of the molten-salt fast reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

TEAMER: Crossflow Turbine Fairing Geometry Optimization - Report and CFD Modeling Files

The dataset includes computational fluid dynamics (CFD) models and simulation files for crossflow turbines as well as a detailed project report. The report documents the project undertaken by the Ocean Renewable Power Company (ORPC) to design and optimize a modular fairing for the Modular RivGen Marine Hydrokinetic (MHK) turbine, which enhances the efficient deployment and operation of turbine arrays. The project focused on optimizing the hydrodynamic performance of the fairing using CFD, with an emphasis on two key geometric parameters: the fairing's cross-sectional shape and the spacing between the rotor and the fairing. The analysis aimed to maximize net power output while also assessing discretized loading to evaluate ultimate and fatigue loads on the turbine components. The numerical modeling was conducted using both the commercial CFD software Star-CCM+ and the open-source code openFOAM, with the latter utilizing the actuator line library, turbinesFOAM. This dual-code approach was intended to increase confidence in the results and demonstrate the viability of using open-source tools for high-fidelity marine energy modeling. This dataset includes all necessary files for actuator line simulations in openFOAM, as well as 2D blade-resolved CFD results, along with Python and Java scripts for setting up and post-processing simulations.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Coupling coarse-mesh CFD with fine-mesh CFD for modeling molten-salt reactors in the Virtual Test Bed (VTB)

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims at developing a simulation tool kit to accelerate the development and deployment of nuclear power technologies. NEAMS multiphysics tools have been designed to provide numerical simulation support for the design and licensing of GEN IV reactors. Pronghorn is NEAMS's coarse-mesh computational fluid dynamics (CFD) tool, which is designed to run 3D core transients in GEN IV reactors at a reduced computational cost. To increase their accuracy, coarse-mesh CFD simulations require calibrated closure coefficients. One way of computing these coefficients is via the Nek5000, NEAMS's high-fidelity CFD tool. This article discusses our current research lines in informing Pronghorn closure coefficients via Nek5000 to enable multiphysics simulations of the core cavity of the molten-salt fast reactor. We present an application in which Pronghorn mixing length turbulent viscosity has been calibrated from Nek5000 simulations. The resulting Pronghorn thermal-hydraulics model is then coupled to Griffin, the NEAMS neutron transport solver, to solve for the steady-state configuration of the molten-salt fast reactor. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Demonstration of CFD to support the coupled analysis of a reactor pressure vessel subjected to pressurized thermal shock

The structural components that comprise nuclear reactors and their supporting structures are subjected to harsh operating environments that can challenge their integrity, especially after exposure for extended duration or under accident condition. As one of the most significant components of a reactor, the Reactor Pressure Vessel (RPV) is exposed to an aggressive environment during the operation time (e.g. more than 40 years). Aging degradation mechanisms (e.g. thermo-fatigue) could grow initial defects up to a critical size, increasing the susceptibility to failure in the RPV. The conventional methods are mostly based on simple crack and structure geometries. Very limited studies consider the real conditions of the RPV subjected to a thermal shock due to a Loss of Coolant Accident (LOCA). During a LOCA event, the most severe conditions take place when the emergency core cooling (ECC) water is injected inside the cold legs filled initially with hotter water and/or steam. The rapid cooling of the down-comer and the internal RPV surface followed probably by re-pressurization of the RPV causes large temperature gradients and variation of pressure which induces thermal-mechanical stresses. In order to develop the model for integrity assessment of a reactor pressure vessel (RPV) subjected to pressurized thermal shock (PTS), a multi-physics simulation, which includes the thermo-hydraulic, thermo-mechanical and fracture mechanics analyses is necessary. The prediction of the temperature field is achieved by using computational fluid dynamics (CFD) simulation. In this report, a demonstration CFD standalone simulation is performed to support coupled analysis for Reactor Pressure Vessel (RPV) subjected to Pressurized Thermal Shock (PTS). The study use a simplified computational domain to represents a real RPV. The purpose of the study is to demonstrate the transient temperature response of RPV to ECC injection. The CFD model is built in a robust and efficient way for further coupled calculation. The next steps of this work, including the coupled thermal and tensor mechanics capabilities using Cardinal are expected to be complete by the end of FY21 for the demo problem. After this, into FY22, the capability will be demonstrated for a realistic RPV.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Development and Validation of Physics-Based Sub-Models of High-Pressure Supercritical Fuel Injection at Diesel Conditions Insert

The project sought to develop and validate accurate, physics-based, numerical submodels and implement them in computational fluid dynamics (CFD) software codes to enable accurate predictions of diesel sprays at pressures higher than the current systems. The combined experimental-computational effort has four main objectives: (1) acquire spatially and temporally resolved scalar measurements of fuel-oxidizer mixing in the near field of the fuel jet, (2) develop software modules to calculate thermo-physical properties of diesel surrogates and their mixtures with oxidizer of interest in real-gas operating regime, (3) integrate the real-fluid model into a commercial and an open-source CFD code to simulate fuel-air mixing in high-pressure diesel sprays, and (4) assess the robustness, accuracy, and uncertainty of the integrated CFD solvers in computing diesel sprays. Project accomplishments are consistent with the original goals of the project. Although we acquired spatially and temporally resolved scalar measurements, they were limited to refractive index difference and not to thermodynamic properties such as temperature and equivalence ratio. However, we successfully developed the theoretical background needed to acquire such measurements at diesel conditions and demonstrated the capability of the RSD for scalar measurements in a canonical turbulent jet flow replicating features of the diesel jet. Additional work will be needed to implement these procedures at high-pressure, high-temperature engine-like conditions of the CPFR. Mainly, we did not anticipate the need to find the liquid boundary and its statistical features or the state relationships between refractive index and thermodynamic properties, assuming the widely known Dale-Gladstone equation would suffice. Still, efforts devoted to these tasks were essential to fulfil the goals of the project and serves as the foundation on which many of the conclusions and key contributions of this project are based. Further assessment and application of the developed CFD codes is recommended to fully realize the benefits of this research.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗

DOE OSTI2022

Development and Validation of Resistance-Capacitance Model (RCM) for Phase Change Material (PCM) Embedded in 3D Periodic Structures

The low thermal conductivity of Phase Change Materials (PCM) can be improved with extended surfaces such as additively manufactured 3D periodic lattice structures. Three different aluminum alloy-based lattices (base sizes 10, 20, and 40 mm) with average porosity of 0.95 filled with paraffin wax, with a nominal phase change temperature of 55°C, were experimentally investigated. In this work, a computationally efficient 2D Resistance Capacitance-based model (RCM) was developed for predicting the thermal characteristics of these geometries. Non-uniform porosity in the PCM-metal domain was estimated using image processing and served as model input. The solver does not solve for higher-order physics as in CFD but still can provide a good prediction of thermal resistance and energy storage at a very low computational cost. The simulation-to-real-time factor for this geometry is of the order of 10-4, while CFD simulations typically have a real-time factor greater than 1. The model was validated against the experimental data for melting under three different heat fluxes (6250 W/m2, 12500 W/m2, and 18750 W/m2). The mean deviation of the predicted average PCM temperature was between 1.34 K-2.81 K for different cases. The maximum average temperature deviation of 5.45 K was observed for the 20 mm geometry at the highest heat flux test condition. The effects of natural convection were neglected in the model, but the predicted PCM temperature and energy storage still showed good agreement with the experimental data.

25 ENERGY STORAGE↗