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DOE OSTI2020

Continued Validation Studies using the MOOSE Framework for Plasma Simulation with Electromagnetics

Resources and tools for the modeling and simulation of low-temperature plasma (LTP) discharges are increasingly vital to progress in the field in order to properly characterize and study complex source designs and plasma chemistries beyond the scope of traditional diagnostics. Open-source software provides powerful platforms for this work and can enable community-driven LTP R&D. Within the Multiphysics Object-Oriented Simulation Environment (MOOSE) open-source framework [1], capabilities have been demonstrated in the areas of plasma fluids (Zapdos [2]), plasma chemistry (CRANE [3]), and general electromagnetic wave theory (Electromagnetic Library for Kinetics & fluids [ELK] [4]). ELK has since been coupled to Zapdos/CRANE to enable fully coupled electromagnetic plasma simulations. This talk will detail the continued validation efforts and discuss Zapdos-ELK-CRANE code coupling with various-low temperature plasma sources. The impact of fully coupled electromagnetics on process parameters (e.g., temperature and electron/ion energy) versus an electrostatic description will also be discussed. [1] Permann et al., SoftwareX, 11 (2020) 100430. [2] Lindsay et al., J. Phys. D: Appl. Phys. 49 (2016) 235204. [3] Keniley et al., Talk, GEC Session DT3.00002 (2019). [4] Icenhour et al., Talk, GEC Session RR1.00006 (2019). Acknowledgements: INL Graduate Fellowship Program, NSF SI2 Grant 1740300, U.S. Dept. of Energy Office of Science Graduate Student Research Program

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Role of pH and Eh in geothermal systems: Thermodynamic examples and impacts on scaling and corrosion

The global demand for sustainable and clean renewable energy sources is rapidly increasing to meet growing energy demands while curtailing the CO2 emissions from fossil fuels. Harnessing the natural heat or geothermal energy of the Earth's interior can simultaneously address both issues by offering a clean and renewable energy source. Enhanced geothermal systems require the movement and cycling of fluid through a porous rock media. Further, this process requires understanding the chemistry occurring at the fluid-rock and fluid-metal interfaces both in the deep subsurface and surface, where fluid is circulated for energy transfer or storage. In this review paper, we evaluate the current state of research on how two key variables, pH and Eh, control the fluid-rock reactions controlling mineral scaling and the steel corrosion reactions associated with geothermal systems. These variables are frequently used in geochemical modeling to predict chemical reactions in geothermal systems. Prediction of the fluid-rock behavior can be utilized to formulate new strategies for extending the lifespan and efficiency of heat extraction and utilization and maximizing the safety of geothermal systems.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Supramolecular dynamic binary complexes with pH and salt-responsive properties for use in unconventional reservoirs

Hydraulic fracturing of unconventional reservoirs has seen a boom in the last century, as a means to fulfill the growing energy demand in the world. The fracturing fluid used in the process plays a substantial role in determining the results. Hence, several research and development efforts have been geared towards developing more sustainable, efficient, and improved fracturing fluids. Herein, we present a dynamic binary complex (DBC) solution, with potential to be useful in the hydraulic fracturing domain. It has a supramolecular structure formed by the self-assembly of low molecular weight viscosifiers (LMWVs) oleic acid and diethylenetriamine into an elongated entangled network under alkaline conditions. With less than 2 wt% constituents dispersed in aqueous solution, a viscous gel that exhibits high viscosities even under shear was formed. Key features include responsiveness to pH and salinity, and a zero-shear viscosity that could be tuned by a factor of ~280 by changing the pH. Furthermore, its viscous properties were more pronounced in the presence of salt. Sand settling tests revealed its potential to hold up sand particles for extended periods of time. In conclusion, this DBC solution system has potential to be utilized as a smart salt-responsive, pH-switchable hydraulic fracturing fluid.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Mineral and fluid transformation of hydraulically fractured shale: case study of Caney Shale in Southern Oklahoma

This study explores the geochemical reactions that can cause permeability loss in hydraulically fractured reservoirs. The experiments involved the reaction of powdered-rock samples with produced brines in batch reactor system at temperature of 95 °C and atmospheric pressure for 7-days and 30-days respectively. Results show changes in mineralogy and chemistry of rock and fluid samples respectively, therefore confirming chemical reactions between the two during the experiments. The mineralogical changes of the rock included decreases of pyrite and feldspar content, whilst carbonate and illite content showed an initial stability and increase respectively before decreasing. Results from analyses of post-reaction fluids generally corroborate the results obtained from mineralogical analyses. Integrating the results obtained from both rocks and fluids reveal a complex trend of reactions between rock and fluid samples which is summarized as follows. Dissolution of pyrite by oxygenated fluid causes transient and localized acidity which triggers the dissolution of feldspar, carbonates, and other minerals susceptible to dissolution under acidic conditions. The dissolution of minerals releases high concentrations of ions, some of which subsequently precipitate secondary minerals. On the field scale, the formation of secondary minerals in the pores and flow paths of hydrocarbons can cause significant reduction in the permeability of the reservoir, which will culminate in rapid productivity decline. This study provides an understanding of the geochemical rock–fluid reactions that impact long term permeability of shale reservoirs.

58 GEOSCIENCES↗

DOE OSTI2020

Machine Learning-Based Upscaling of Finite-Size Molecular Dynamics Diffusion Simulations for Binary Fluids

Molecular diffusion coefficients calculated using molecular dynamics (MD) simulations suffer from finite-size (i.e., finite box size and finite particle number) effects. Results from finite-sized MD simulations can be upscaled to infinite simulation size by applying a correction factor. For self-diffusion of single-component fluids, this correction has been well-studied by many researchers including Yeh and Hummer (YH); for binary fluid mixtures, a modified YH correction was recently proposed for correcting MD-predicted Maxwell–Stephan (MS) diffusion rates. In this study we use both empirical and machine learning methods to identify improvements to the finite-size correction factors for both self-diffusion and MS diffusion of binary Lennard-Jones (LJ) fluid mixtures. Using artificial neural networks (ANNs), the error in the corrected LJ fluid diffusion is reduced by an order of magnitude versus existing YH corrections, and the ANN models perform well for mixtures with large dissimilarities in size and interaction energies where the YH correction proves insufficient.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

A sui generis whipping-instability-based self-sequencing multi-monodisperse 2D spray from an anisotropic microfluidic liquid jet device

Well-defined aerosols pave the way for versatile basic and applied research. Here, we demonstrate a unique whipping instability that generates from a high-aspect-ratio microfluidic device resulting in a unique steady-state gas-focused whipping jet (WJ) without any need for electrification. This WJ device emanates a multi-monodisperse whipping spray jet with a two-dimensional (2D) profile. We demonstrate this phenomenon based on various fluidic parameters theoretically and experimentally. The 2D WJ’s unique behavior is derived using analytical fluid dynamics to explain jet diameter, whipping regime, and spreading angle. The phenomenon is further characterized experimentally by measuring the angle with respect to the flow rate, the distances between droplets, the droplet shapes, and the reproducibility of these parameters. We also explain the precise fabrication of such inexpensive devices. Lastly, we highlight these devices’ potential use as sample environments in versatile applications ranging from cryoelectron microscopy over mass spectrometry to drug formulation and structural studies at X-ray free-electron lasers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores

Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid–wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R 2 ) of ~0.99 and a mean squared error of ~2.9 × 10 –5 . Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Probing Pore Size and Connectivity in Porous Silicas Using 13C MAS NMR Spectroscopy of Supercritical Methane

Measuring the pore size and pore-size distributions and exploring the fluid-exchange dynamics between different types of pores in porous materials remains a significant experimental challenge but is critical to understanding catalysis, chromatography, nutrient cycling, and a whole range of geochemical phenomena, including shale gas and tight gas extraction. Here, we present the results of 1D 13 C NMR and 2D exchange spectroscopy (EXSY) NMR investigations of a porous silica using supercritical methane (scCH 4 ) as a direct probe of pore size and fluid exchange between pore types. The results show that the 13 C chemical shift of scCH 4 adsorbed in nanometer-scale silica pores becomes more negative with increasing pore diameter, in agreement with trends reported for gas hydrates, zeolites, MOFs, and clays and other microporous (<10 nm pores) geochemical materials. These 13 C chemical shifts follow a natural log–linear trend with pore size in the vacuum-dried porous silicas studied here, allowing one to predict pore size based on the 13 C chemical shift in dry silica nanopores. The EXSY and 1D 13 C NMR results both show that CH 4 exchanges between pore and bulk fluid environments over rate scales from 0.01 to 1 kHz in vacuum-dried samples and that exchange dynamics must be considered when interpreting 13 C NMR of pore-adsorbed CH 4 . Introducing H 2 O to the system causes the 13 C chemical shift to become more negative with increasing H 2 O content as a result of H 2 O preferentially filling small pores, forcing CH 4 to occupy larger pores that are better connected to the bulk environment. Likewise, the 1D 13 C data show a decreased distribution of exchange rates between pore-adsorbed and bulk CH 4 in the presence of H 2 O.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Local Rearrangement in Adsorption Layers of Nanoconfined Ethane

Fluids under nanoconfinement control many environmental and engineering processes. In particular, nanoconfined ethane commonly occurs in hydrocarbon production and gas separation applications. While previous research has measured the pore critical properties and self-diffusivity of nanoconfined ethane, the relationship between molecular organization and phase behavior in nanoporous media persists as a knowledge gap. Through experimental adsorption measurements and molecular dynamics simulations, we report the impact of pore wall proximity, pore size, and temperature on the structure of nanoconfined ethane. Finally, we offer evidence for an energetically favorable rearrangement of ethane molecules in the adsorption layer that may contribute to more accurate gas-in-place estimates and more efficient design of gas separation membranes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A simulation-based parametric study of CLOU chemical looping reactor performance

Chemical looping with oxygen uncoupling (CLOU) is a variant on chemical looping combustion in which the oxygen carrier releases gaseous O 2 in the fuel reactor, making it well-suited for solid fuels, since the released gaseous O 2 readily reacts with solid char. This work presents several computational fluid dynamic (CFD) simulations of copper-based CLOU in a dual fluidized bed system, each with different operating conditions. The modeling predicted that coal particle sizes as large as 1000 μm did not significantly affect performance. Increased oxygen carrier copper loading resulted in an excess of gaseous oxygen in the product gas stream. Decreasing the oxygen carrier bed mass as well as reducing the air reactor fluidizing velocity did not supply enough oxygen to the fuel reactor to complete combustion of the coal. This generated a failure state in which the temperature continued to decrease in the fuel reactor from the lack of combustion, which in turn reduced the O 2 equilibrium partial pressure, further lowering the amount of combustion possible. Sufficient O 2 can be maintained in the fuel reactor by ensuring a high enough air reactor velocity and a large enough supply of oxygen carrier inventory to handle the chosen coal feed rate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Investigation of countercurrent flow profile and liquid holdup in random packed column with local CFD data

Liquid holdup and mass transfer area are critical parameters used in packed column design and CO 2 capture efficiency prediction. Compared to experimental methods, 3D computational fluid dynamics (CFD) can provide high-resolution data for studying countercurrent flow hydrodynamics in a packed column. In this paper, a framework was established for modeling the liquid–gas countercurrent flow in a random packed column with pall rings. Besides the column-averaged information, the radial pall ring distribution, velocity, and liquid holdup profiles are obtained in the simulations. The radial two-phase velocity and liquid holdup profiles show significant fluctuations caused by the non-uniform pall ring distribution. The entrance effect and wall influence are also observed in the packed column. By using local countercurrent flow information, the packing specific area $a_p$ and liquid velocity $u_L$ range are significantly expanded for liquid holdup hL prediction. This enables us to develop the liquid holdup correlation with one packing size and six CFD runs only. The proposed power law of the liquid holdup correlation $h_L~u_L^{0.44}$ indicates the random packed column falls in a viscous to turbulent transition regime with a Reynolds Number range of [6.7–40.2]. The derived liquid holdup correlation is in good agreement with existing correlations that are developed using the column-averaged experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Numerical simulation for the design of induction heating based radio frequency reactor for ethylene production

Ethylene is a vital petrochemical compound produced in vast amounts yearly by manufacturers that have enough scale to overcome the inherent thermodynamic inefficiencies of the process. In order to address the inefficiencies that prevent smaller scale or intermittent production ethylene, investigation of new production methods are required. In this work, we investigate the use of a radio frequency (RF) based reactor system that generates heat internally as opposed to applying heat externally via steam or direct combustion of fossil fuels. In order to guide the design of an electromagnetic based reactor system, we have created a macroscale model capable of capturing heat generation at the susceptors from a produced electromagnetic field and subsequent chemical reactions. Several susceptor and induction coil designs were investigated to better understand the performance of the system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Multimodal study of the impact of stimulation pH on shale pore structure, with an emphasis on organics behavior in alkaline environments

The tight nature of shale formations calls for hydraulic fracturing techniques able of altering the pore architecture to facilitate the production of hydrocarbon resources. The composition, pH, salinity, density and chemistry of Hydraulic Fracturing Fluids (HFF) vary substantially depending on reservoir characteristics. pH is among the most important stimulation fluid properties, ranging from very acidic, for carbonate reservoirs, to basic, in the case of clay-rich formations. pH regulates what geochemical reactions take place between the stimulation fluid and shale, as well as pore architecture alteration. The dissolution of carbonates and pyrite, and the precipitation of common minerals such as barite, gypsum and iron oxides in acidic environments have been extensively documented. In contrast, alkali stimulation environments and the role of organic components have received less attention. This research provides insight into the role of both minerals and organic components during alkaline stimulation, and the resulting pore architecture alterations. Here, a set of reactive experiments are performed using three different shales, with varying organic matter (OM) content, at different pH (3, 6, 8, 10, 12). The analysis of pore architecture alteration induced by the reaction was performed via nitrogen (N2) gas adsorption and Time-Domain Nuclear Magnetic Resonance (TD-NMR). Inductively Coupled Plasma - Optical Emission Spectrometry (ICP-OES) was used to measure the composition of the stimulation fluid post-reaction. The system with a larger impact on the pore architecture (pH 12) underwent a more extensive analysis through the application of Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and x-ray Diffraction (XRD). Results show that mineral dissolution contributed to the creation of secondary porosity, mainly through dissolution of clays and silicates. We show that the abundance and distribution of organic matter both play a significant role in changes in pore architecture. Most samples showed an increase in inter-organic porosity, likely due to the organic acids within kerogen reacting with the stimulation fluid. Samples with a larger amount and more widespread distribution of organic matter show the most significant alterations. Finally, we also highlight the dissolution process of iron-rich minerals, including framboidal pyrite, as another important source of secondary porosity.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2020

Crossover from Rouse to Reptation Dynamics in Salt-Free Polyelectrolyte Complex Coacervates

Considerable interest in the dynamics and rheology of polyelectrolyte complex coacervates has been motivated by their industrial application as viscosity modifiers. A central question is the extent to which classical Rouse and reptation models can be applied to systems where electrostatic interactions play a critical role on the thermodynamics. By relying on molecular simulations, we present a direct analysis of the crossover from Rouse to reptation dynamics in salt-free complex coacervates as a function of chain length. This crossover shifts to shorter chain lengths as electrostatic interactions become stronger, which corresponds to the formation of denser coacervates. To distinguish the roles of Coulomb interactions and density, here we compare the dynamics of coacervates to those of neutral, semidilute solutions at the same density. Both systems exhibit a universal dynamical behavior in the connectivity-dominated (subdiffusion and normal diffusion) regimes, but the monomer relaxation time in coacervates is much longer and increases with increasing Bjerrum length. This is similar to the cage effect observed in glass-forming polymers, but the local dynamical slowdown is caused here by strong Coulomb attractions (ion pairing) between oppositely charged monomers. Our findings provide a microscopic framework for the quantitative understanding of coacervate dynamics and rheology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

MOSCATO Development and Integration in Fiscal Year 2024

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

MOSCATO Development and Integration in Fiscal Year 2025: Implementation of Multiphase, Multiphysics Modeling Capabilities for Molten Salt Systems

MOSCATO (Molten Salt Chemistry and Transport) is a multiphysics code that provides high-fidelity, coupled simulations of fluid flow, heat transfer, mass transfer, chemistry, electrochemical phenomena, and alloy corrosion for molten salt systems. In FY25, significant developments were made to the code package, enhancing its capabilities for modeling all relevant phenomena within operating moltens salt reactors (MSRs). The developments and activities in FY25 included: 1. Implementation of Level-Set methods to enable modeling of single-bubble behavior in molten salts. In FY25, the Level-Set two-phase flow modeling implementation was improved to simulate single bubble behavior with molten salt media. The large density and viscosity ratios between typical gases and molten salt liquids present challenges for these types of numerical solvers. With enhancements to the pressure projection method, MOSCATO’s Level-Set solver was able to be successfully validated to experiments related to helium bubble rise in stagnant molten salt. The simulated bubble rising velocity showed reasonable good agreement with experimental measurements. The bubble shape and dynamics were also visually compared with experimental snapshots, demonstrating a good qualitative match. 2. Generation of mass transfer correlations for multiphase flow systems. To enable calculations of the tritium transport across the interface between gas bubbles and salt, we modeled high- Schmidt-number mass transfer around a sphere across a broad range of Reynolds numbers. The mesh near the sphere surface was highly refined to resolve steep concentration gradients caused by the low diffusion coefficient. Literature-based mass transfer correlations were compared with the numerical results, and modifications were proposed to improve agreement, particularly at higher Schmidt numbers. These mass transfer correlations were subsequently provided to other national laboratories to help enable high quality mass transfer simulations using lower-order solvers under development within the NEAMS program. 3. Preliminary implementation of a bubbly flow solver. To model bubbly flow in molten salt, we implemented a bubbly flow solver for void fractions less than 5%. To do so, an algebraic relative velocity model that assumes small bubbles with rapid momentum equilibration was added to MOSCATO to compute bubble velocities. Preliminary comparisons with experimental data showed reasonable agreement, and further improvements are underway. 4. Generation of mass transfer correlations for MSRE subchannel The Molten-Salt Reactor Experiment (MSRE) was a landmark historical project that demonstrated the feasibility of molten-salt reactor technology. The MSRE campaign also generated a significant body of experimental data and reports that continue to support molten-salt–related research. In this report, the MSRE core subchannel was used as the reference geometry for a mass transfer study performed with MOSCATO. The geometry and computational mesh were adapted from a previous study, providing adequate resolution for the relatively low Reynolds number in this case. Additional mesh refinement was applied to reach higher Schmidt numbers, enabling the derivation of a reliable mass-transfer correlation for the present scenario. 5. Simulations of oxygen ingressions into molten salt. In the previous fiscal year, we initiated a study to simulate oxygen ingression in stagnant salt. As oxygen enters the salt through its surface, it reacts with Ce 3+ to form solid CeO 2 and other reaction products. To more fully capture the complex diffusion-convection-reaction mechanisms, capabilities for modeling natural convection in the salt vessel were added. These were needed as the flow of the ingressed gas induced flow in the salt caused by surface shear and non-isothermal effects. With these updated physics in place, we were able to successfully reproduce the experimental results for the rate of change of CeCl 3 concentrations versus time. 6. Flow corrosion model validation. In FY24, MOSCATO’s corrosion model was validated against static corrosion experiments. In FY25, this work was extended to a flow corrosion experiment, where FLiNaK salt was driven by natural convection, with initial salt impurities to initiate corrosion. Despite uncertainties in parameters such as elemental diffusion coefficients in the alloy and unknown H + concentrations, the simulations achieved good agreement with experimental results, especially in predicting sample mass losses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗