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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling Cost of Offshore Carbon Storage in Saline Reservoirs

Poster presentation to the AAPG-SEG-SPE CCUS 2024 Conference. Offshore saline reservoirs provide a significant and accessible resource for geologic carbon storage (CS). The offshore environment requires distinct approaches to site selection, operations, monitoring, and risk that affect the technoeconomic assessment of offshore CS projects. The National Energy Technology Laboratory (NETL) has developed a CS cost model for offshore saline reservoirs known as CO2_S_COM_Offshore. Based on NETL’s widely used CO2_S_COM cost model for onshore saline CS, CO2_S_COM_Offshore enables technoeconomic analysis of CS in offshore areas. This model comprehensively incorporates multiple facets of offshore CS projects, from regional evaluation and site selection to permitting, transport, operations, monitoring, site closure, and decommissioning. Developed to model cost for offshore United States (US) Exclusive Economic Zones, aspects of this model can be adapted to international projects. Presented at the SPE/AAPG/SEG Carbon Capture Utilization and Storage Conference in Houston, TX, March 11-13, 2024.

Mark-Moser, Mackenzie K.↗
DOE OSTI2025

Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks

Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.

autoencoder neural networks↗
DOE OSTI2024

Machine learning for the identification of phase transitions in interacting agent-based systems: A Desai-Zwanzig example

Deriving closed-form analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In this paper, we propose a data-driven framework that pinpoints phase transitions for an ABM—the Desai-Zwanzig model—in its mean-field limit, using a smaller number of variables than traditional closed-form models. To this end, we use the manifold learning algorithm Diffusion Maps to identify a parsimonious set of data-driven latent variables, and we show that they are in one-to-one correspondence with the expected theoretical order parameter of the ABM. We then utilize a deep learning framework to obtain a conformal reparametrization of the data-driven coordinates that facilitates, in our example, the identification of a single parameter-dependent ordinary differential equation (ODE) in these coordinates. Additionally, we identify this ODE through a residual neural network inspired by a numerical integration scheme (forward Euler). We then use the identified ODE—enabled through an odd symmetry transformation—to construct the bifurcation diagram exhibiting the phase transition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Composite Analysis for Low-Level Waste Disposal in the Hanford Site Central Plateau (FY 2020) (Rev. 2)

This document provides an updated Hanford Site composite analysis (CA). A Hanford Site CA was prepared and issued in 1998 (PNNL-11800) with an addendum provided in 2002 (PNNL-11800 Addendum 1). The CA was approved in 2002 (Frei, 2002) and has been maintained (DOE/RL-2000-29 and subsequent revisions) to support low-level waste disposal performance assessments (PAs) and disposal authorizations for facilities at the Hanford Site, including the following: Continued operation of the Environmental Restoration Disposal Facility (ERDF) and the 200 East and 200 West Low-Level Burial Grounds; Construction of the Integrated Disposal Facility (IDF); Forthcoming closure of tank residual waste systems such as Waste Management Area (WMA) C. The CA maintenance program resulted in a determination in 2015 (DOE/RL-2015-66) that the Hanford Site CA needed an update for the following reasons: While the initial Hanford Site CA has been maintained since 2001, the accumulation of basis changes reported in the annual summary reports over the succeeding 14 years merit evaluation in an updated analysis; The U.S. Department of Energy (DOE) Headquarters requested in a memorandum in 2015 (Gilbertson and Marcinowski, 2015) that “as soon as the relevant PAs are complete, the CA will be revised to account for all of the new information.” This updated Hanford Site CA accounts for the following new information: 1. Inclusion of a detailed Hanford Site baseline disposition that projects remedial activities through site closure. There have been significant changes through decision making in the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA) process that were not available when the original CA was produced; 2. Inclusion of an updated inventory basis, new modeling capabilities, and new decisions reached in the associated record of decision (ROD) that was provided by issuance of DOE/EIS-0391 in fiscal year (FY) 2013. Development of a Hanford Site groundwater model from the baseline provided in a technical transfer of models for the Final Tank Closure and Waste Management Environmental Impact Statement (DOE/EIS-0391) commenced in FY 2014 and resulted in the plateau-to-river (P2R) groundwater flow model that is used in this updated Hanford Site CA; 3. Accounting for pump and treat systems, which were not evaluated in the initial CA. Pump and treat systems have had significant impact on groundwater flow system behavior, contaminant transport, and contaminant removal from Hanford Site groundwater. This process is accounted for in the use of the P2R model for this updated Hanford Site CA; 4. Inclusion of water-level data collected since the initial CA was completed. Data collected as the unconfined aquifer water levels continue to recede since the cessation of large liquid discharges in the late 1990s have led to marked improvement in understanding of the flow system for future conditions, particularly regarding northward flow potential in the critical Gable Gap area. The calibration in the latest version of the P2R model accounts for this information; 5. Inclusion of results from the revised ERDF PA completed in 2013 (WCH-5209) that account for updated inventory and expansion of the ERDF facility to about twice the size that was evaluated in the original CA; 6. Use of updated geoframeworks to provide the structural basis for numerical fate and transport models in the groundwater pathway of this updated CA. The geologic basis for groundwater models has continued to improve with additional data collection and interpretation with the creation and maintenance of the Hanford South Geoframework and the Central Plateau Vadose Zone Geoframework tools; 7. Incorporation of updated tank residual inventory estimates. Tank residual inventory estimates have improved with the incorporation of tank retrieval inventory data for those tanks that have completed retrieval. The CA inventory data package includes this updated information; 8. Incorporation of WMA C PA results. Two additional PAs for tank farm closure decisions are in preparation during the period required to prepare an updated Hanford Site CA: WMA C (FY 2016) and WMA A-AX (in preparation). The WMA C PA results are incorporated into the updated Hanford Site CA, and its grouted residuals model is used as the basis for a release model to account for the other tank farm systems modeling in this updated CA; 9. An update to the IDF PA was submitted in FY 2017 and has been reviewed and approved. The results of the IDF PA are incorporated into this updated CA; 10. Updated risk assessment scenarios. The risk assessment scenarios currently in use for Hanford Site CERCLA and Resource Conservation and Recovery Act of 1976 (RCRA) analyses differ from those evaluated in the initial Hanford Site CA. The representative person exposure scenario evaluated in this updated Hanford Site CA is consistent with recent PAs and CERCLA and RCRA analyses as the Hanford Site. This updated Hanford Site CA provides the following: A comparison of the updated Hanford Site CA all-pathway dose results with the performance measures during the compliance period, which is assumed to begin with site closure in calendar 2070 with the last scheduled disposal action and continue for 1,000 years postclosure (to calendar year 3069); A comparison of Hanford Site CA all-pathway dose results with the performance measures during the postcompliance period to address potential peaks beyond the compliance period. This is accomplished by evaluating dose in the period for 9,000 years following the compliance period (i.e., from calendar year 3070 to 12070).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Fluidization of Group A Glass Particles: Experiments and Preliminary Validation

This work is aimed at providing reliable high-quality data from fluidization experiments. Geldart Group A glass beads are used as bed material. The fluidizing medium is air at atmospheric pressure which enters the system at 40% relative humidity. The air inlet velocity is varied over a broad range from 2 U mf to 26 U mf , where the minimum fluidization velocity, U mf , is 0.608 cm/s. There is not a significant change in the mean values of differential pressure; however, there are noticeable trends in their standard deviation values. Preliminary validation studies are performed using the open-source software Multiphase Flow with Interphase eXchanges (MFiX) developed by the National Energy Technology Laboratory (NETL). The results from Eulerian-Eulerian (MFiX-TFM, two-fluid model) and Eulerian-Lagrangian (MFiX-PIC, particle-in-cell) formulations are compared. The work presented in this report highlights the applicability of MFiX-PIC for large-scale applications, and reveals that PIC simulation offers a suitable alternative to TFM simulation when effects due to multiple components or polydispersity are significant. PIC modeling provides a readily extendible framework for such systems. However, PIC models do rely on an empirical closure for inter-particle stress. Some solution discrepancies were evident in MFiX-PIC predictions which require further analysis. As such, the study underlines the need for a systematic approach to quantify the sources of numerical uncertainty within the PIC model. However, even with known empiricism, MFiX-PIC has demonstrated considerable potential in analyzing gas-solid systems. The results obtained are encouraging and warrant further investigation to improve the predictive capability of MFiX-PIC.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Aerosol-Ice Formation Closure Pilot Study (AEROICSTUDY) Field Campaign Report

The aerosol-ice formation closure pilot study (AEROICESTUDY) was a field campaign conducted at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility at the Southern Great Plains (SGP) observatory from October 7 to 28, 2019. The purpose of this campaign was to test a field observational approach for conducting an aerosol-ice formation closure study. In other words, the goal was to predict the number of ice nucleating particles (INPs) from the measurements of physical and chemical aerosol properties and compare those to measured INP number concentrations. This project was motivated by the fact that the aerosol community has widely conducted aerosol radiative closure, aerosol-cloud condensation nuclei (CCN), and CCN-droplet closure studies to test the physical models and parameterizations that cloud-resolving and climate models rely on to perform reliable simulations of the Earth system and energy budget. However, very few closure studies related to INPs have been conducted, and to our knowledge, none using robust, size-resolved, ambient aerosol composition measurements as model inputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantification of Physical and Numerical Mixing in a Coastal Ocean Model Using Salinity Variance Budgets

Numerical mixing, the spurious mixing primarily generated by the discretization of advection, is often significant in estuarine and coastal models due to sharp, energetic fronts. In this study, we compare on- and offline estimates of numerical mixing in a submesoscale-resolving realistic simulation of the ocean state over the Texas-Louisiana continental shelf. While offline estimates of numerical mixing differ from online estimates, offline methods may be the only analysis available. This study offers insight into the differences between the on- and offline methods. We use two methods to estimate numerical mixing offline, based on salinity squared $s^2$ and volume-mean salinity variance $s^{\prime^2}$. Numerical mixing estimated from the $s^{\prime^2}$ budget is generally within 60\% of the magnitude for the online method but captures the temporal variability well. However, the $s^2$ budget compares poorly due to larger truncation errors associated with the tendency and advection terms, which can be reduced by increasing the model output frequency. We also investigate the effects of horizontal resolution on numerical mixing using a two-way nested grid. The volume-integrated numerical mixing constitutes 57\% of the bulk physical mixing -- the mixing prescribed by the turbulence closure scheme -- in the coarse model and may exceed the physical mixing by half an order of magnitude. We find that numerical mixing is reduced by 35\% on average in the nested model, less than expected based on scaling of the numerical mixing for an upwind advection scheme, likely due to new dynamical processes that emerge in the nested simulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Toward Predictive RANS and SRS Computations of Turbulent External Flows of Practical Interest

In this work, we investigate the main challenges to prediction of turbulent external flows of practical interest with Reynolds-Averaged Navier–Stokes equations (RANS) and Scale-Resolving Simulation (SRS) models. This represents a crucial step toward further developing and establishing these formulations so they can be confidently utilized in engineering problems without reference data. The study initiates by identifying the major challenges to prediction. A literature review is performed to illustrate their effects in RANS and SRS computations. Afterward, we evaluate the impact of the challenges to prediction by analyzing representative statistically steady and unsteady flows with prominent RANS and SRS methods. These include multiple turbulent viscosity and second-moment RANS closures, and hybrid and bridging SRS models. The results demonstrate the potential of the selected SRS models to predict engineering flows. Yet, they also show the importance of considering the challenges to prediction during the setup and conduction of numerical experiments. These can suppress the advantages of using SRS formulations. The data also indicate that only SRS models can confidently predict statistically unsteady flows. In contrast, the results demonstrate that mean-flow quantities of statistically steady flows can be efficiently calculated with RANS closures, especially second-moment closures. Among the selected SRS methods, bridging models reveal better suited for prediction due to their ability to prevent commutation errors and enable the robust evaluation of numerical and modeling errors. This last property allows the use of a new validation technique that does not require reference data.

42 ENGINEERING↗
DOE OSTI2022

Development of machine learning framework for interface force closures based on bubble tracking data

Interfacial force closures in the two-fluid model play a critical role for the predictive capabilities of void fraction distribution. However, the practices of interfacial force modeling have long been challenged by the inherent physical complexity of the two-phase flows. The rapidly expanding computational capabilities in the recent years have made high-fidelity data from the interface-captured direct numerical simulation become more available, and hence potential for data-driven interfacial force modeling has prevailed. In this work, we established a data-driven modeling framework integrated to the HZDR multiphase Eulerian-Eulerian framework for computational fluid dynamics simulations. The data-driven framework is verified in a benchmark problem, where a feedforward neural network managed to capture the non-linear mapping between bubble Reynolds number and drag coefficient and reproduce the void distribution resulting from the baseline model in the test case. The second focus is on utilizing the bubble tracking data set to form a closure for the bubble drag in the turbulent bubbly flow, in which the drag coefficient is set to be correlated with the bubble Reynolds number and the Eötvös number. Pseudo-steady state filtering in the Frenet Frame was carried out to obtain the drag coefficient from the turbulent bubbly flow data. The performance of the data-driven drag model is also examined through a case study, where improvement of model’s prediction near-wall is regarded necessary. In conclusion, discussion and further plans of investigation are provided.

42 ENGINEERING↗
DOE OSTI2024

Determining Exact RANS Operators with the Macroscopic Forcing Method (Final Report)

This report contains a compilation of key results and findings from the ACT project “Determining Exact RANS Operators with the Macroscopic Forcing Method.” The Macroscopic Forcing Method (MFM), a numerical tool for determining closure operators, is used to measure eddy diffusivity moments in Rayleigh-Taylor (RT) instability. It is first applied to low-Atwood 2D RT instability; that work is then extended to 3D RT at different finite Atwood numbers. It is found that nonlocality is important for modeling the mean scalar transport closure operator in RT mixing. Additionally, work is done to improve the statistical convergence of MFM for chaotic problems like RT mixing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Carbon storage cost modeling for the offshore Gulf of Mexico

Groundbreaking for geologic carbon storage (GCS) projects in the offshore Gulf of Mexico is imminent, and there is great interest in utilizing this region for GCS projects. Offshore saline reservoirs provide a significant and accessible resource for GCS. However, conducting GCS in the offshore environment will pose distinct challenges pertaining to site selection, operations, infrastructure use, and monitoring compared to operating onshore that ultimately affect technoeconomic assessment of offshore GCS projects. Carbon storage and transport costs are critical to project developers looking to deploy carbon storage in the offshore environment. We present CO2_S_COM_Offshore, a model developed by the National Energy Technology Laboratory (NETL) as a screening-level offshore saline GCS cost modeling tool. Based on NETL’s widely used CO2_S_COM cost model for onshore saline CS, CO2_S_COM_Offshore enables technoeconomic analysis of GCS in offshore areas. This model comprehensively incorporates multiple facets of offshore GCS projects, from regional evaluation and site selection to permitting, transport, operations, monitoring, site closure, and decommissioning. In general, the model can explore the cost implications for potential offshore GCS project(s) by enabling the user to change several project operational and financial attribute configurations. Key inputs include offshore storage formation options, CO2 injection rate and duration, infrastructure types, monitoring intensity, project financing, and post-injection site care duration. Supporting cost algorithms within CO2_S_COM_Offshore were compiled utilizing S&P Global’ s QUE$TORTM cost estimation software alongside a variety of open-source scientific literature. In addition to reviewing key model components, we discuss several sensitivity analyses, input variabilities, and results on analysis of break-even CO2 price required by a project based on different regulation/policy and operational scenarios for the offshore Gulf of Mexico. These results indicate the value of modeling offshore GCS specifically, and the potential of offshore GCS within a decarbonization value chain. Presented at the 41st USAEE/IAEE North American Conference, 3-6 November 2024, Baton Rouge, LA, United States.

Mark-Moser, Mackenzie K.↗
DOE OSTI2024

Ten-moment fluid model for low-temperature magnetized plasmas

In this paper, a one-dimensional 10-moment multi-fluid plasma model is developed and applied to low-temperature magnetized plasmas. The 10-moment model solves for six anisotropic pressure terms, in addition to density and three components of fluid momentum, which allows the model to capture finite kinetic effects. The results are benchmarked against a 5-moment model, which assumes that the gas constituents follow a Maxwellian velocity distribution function (VDF), and a particle-in-cell Monte Carlo collision model that allows for arbitrary non-Maxwellian VDFs. The models are compared in a one-dimensional, low-temperature, partially magnetized plasma test case. The 10-moment results accurately reproduce the anisotropic temperature profile in low-temperature magnetized plasmas, where shear gradients exist due to the E×B drift. We discuss the mechanisms by which the anisotropic pressure can be generated in low-temperature magnetized plasmas. In addition, the importance of a self-consistent heat flux closure to the 10-moment model is studied, showing consistency with other models only when the assumptions of the underlying model are met. The 10-moment model allows for study of electron inertia effects and non-Maxwellian VDFs without the need for kinetic methods that are more computationally expensive.

Kuldinow, Derek Amur (ORCID:0000000319730196)↗
DOE OSTI2020

Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers

In this article, we utilize machine learning to dynamically determine if a point on the computational grid requires implicit numerical dissipation for large eddy simulation (LES). The decision making process is learnt through a priori training on quantities derived from direct numerical simulation (DNS) data. In particular, we compute eddy-viscosities obtained through the coarse-graining of DNS quantities and utilize their projection onto a Gaussian distribution to categorize areas that may require dissipation. If our learning determines that closure is necessary, an upwinded scheme is utilized for computing the non-linear Jacobian. In contrast, if it is determined that closure is unnecessary, a symmetric and second-order accurate energy and enstrophy preserving Arakawa scheme is utilized instead. This results in a closure framework that precludes the specification of any model-form for the small scale contributions of turbulence but deploys an appropriate numerical dissipation from explicit closure driven hypotheses. Here, this methodology is deployed for the Kraichnan turbulence test-case and assessed through various statistical quantities such as angle-averaged kinetic energy spectra and vorticity structure functions. Our framework thus establishes a link between the use of explicit LES ideologies for closure and numerical dissipation-based modeling of turbulence leading to improved statistical fidelity of a posteriori simulations.

42 ENGINEERING↗
DOE OSTI2025

Modeling and Simulation of Fuel Dispersal During the Loss-of-Coolant Accident

This document is the compilation of the milestone portion to a larger end of project NEUP report. The executive summary of the modeling portion is provided below: In the event of cladding rupture during a postulated LOCA in a pressurized water reactor, fuel particles, along with fission gases, can be expelled into the reactor core from the fractured fuel rod, a phenomenon referred to as fuel dispersal. The initial stage of fuel dispersal is strongly influenced by the high-pressure ejection of fuel fragments, the size and geometry of the ruptured cladding, and the depressurization history of the fuel rod during the postulated LOCA transient. Depending on the location of the burst orifice relative to the quench front, the dispersal event represents an intricate three-phase flow and heat transfer phenomenon, where high-temperature fuel particles carried by the fission gases interact with the coolant within the narrow subchannels of the fuel assemblies, inducing localized phase change. Given the unique multiphysics nature of this phenomena, the current study develops a dedicated computational framework to predict the mass distribution and cooling of dispersing fuel particles, facilitating post-accident assessment and management of the fuel assemblies. Considering the scale of nuclear reactor applications, a continuum three-fluid model is proposed for simulating the transport of solids within the reactor core. With high-temperature fuel fragments within the liquid media, nucleation sites inducing phase changes are dispersed within the flow domain. Coupled with the fact that the transient dispersal event occurs on different time scales than other three-phase flow applications, this study derives a time-averaged three-fluid flow model without losing generality. The assumptions regarding the continuum treatment of the solid phase and the modeling of fuel dispersal behavior are incorporated to simplify the governing equations and derive applicable closure relations. The computational validation of the model was conducted using adiabatic experimental results obtained from ongoing research at Oregon State University, focusing on characterizing fuel dispersal behavior during simulated LOCA conditions. Settlement characteristics of the solids, quantified by the probability distribution of equivalent particles, closely matched the probability density functions reported in experimental studies. The transport of fuel particles within a scaled 5 × 5 lattice of a pressurized-water reactor rod bundle geometry was modeled through a two-fluid Eulerian framework. The required boundary conditions were evaluated from the fuel performance code BISON in a postulated large-break LOCA scenario. The modeling framework considered solid fuel particles as granular matter, interacting with the gaseous dry steam phase and fission gases through the governing interfacial momentum exchange between the participating fluids. The simulation results provided the volume fraction of the solids obtained at the bottom surface of the enclosing tank geometry. Postulated LOCA leading to fuel dispersal phenomena involves the strong coupling between fuel thermomechanics, cladding deformation, thermal-hydraulics, and fuel particle transport. Incorporation of such a strong coupling in numerical simulation is performed by coupling the multiphysics solvers. In the case of fuel dispersal, a strong coupled simulation can be performed by coupling the BISON code for fuel performance, the TRACE code for system-level thermal hydraulics, and fuel particle transport in Multiphysics Object-Oriented Simulation Environment (MOOSE). For such intricate infrastructure, the MOOSE Framework eases the data transfer between codes. The recent version of MOOSE has incorporated the Navier-Stokes module for the fluid flow. An exploratory exercise was done to gain familiarity with finite volume capabilities in the MOOSE framework to incorporate the Spalart-Allmaras (SA) turbulence model. New finite-volume and auxiliary kernels were introduced to assemble the SA transport equation, compute turbulent viscosity, and evaluate wall distance and diagnostic turbulence terms, fully integrated with existing Navier-Stokes modules. A turbulent lid-driven cavity at a Reynolds number of approximately 10,000 is used for verification. MOOSE shows the robust solver convergence and produces the turbulent features. But it underpredicts the velocity profile and turbulent quantities, emphasizing the need to develop improved SA near-wall treatments (e.g., low-Re corrections or wall functions) as a key direction for future work.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Transport in Stochastic Media with Random Chord Length Distributions

Thermal radiation transport computations in binary Markovian random mixtures rely almost exclusively on the Levermore-Pomraning (LP) model which is obtained by applying a heuristic closure to the ensemble averaged random medium transport equation. The validity of this model has been extensively tested by comparing numerical results over a broad parameter range (material types and mixing parameters) against benchmark solutions in planar geometry. The conditions under which the LP-model provides useful results and when it breaks down are now well established, but work to date has been largely restricted to homogeneous mixing statistics, i.e., the mean chord lengths of both materials are taken to be spatially constant. In recent work, this limitation was relaxed by allowing the mean chord lengths and, in a consistent fashion, the volume fractions in the LP-model to vary spatially and in a follow-up investigation benchmark solutions were obtained by ensemble averaging results over material realizations sampled from a nonhomogeneous Poisson process (NHPP). Numerical experiments in rod geometry with specifically linear and quadratic spatial dependence of chord lengths showed that the material averaged radiation intensities vary nonmonotonically with depth into the medium, in stark contrast to solutions obtained assuming uniform chord lengths. Moreover, depending on the local optical thickness and strength of scattering, the LP-model results showed locally more nuanced deviations from the benchmark solutions than was the case with constant chord lengths. These limited numerical investigations highlight the nontrivial qualitative and quantitative consequences of nonhomogeneous mixing statistics, in particular that closure approximations may not be uniformly valid or invalid over the problem domain.

42 ENGINEERING↗
DOE OSTI2024

Modeling Microwave-Enhanced Chemical Vapor Infiltration Process for Preventing Premature Pore Closure

The chemical vapor infiltration (CVI) process involves infiltrating a porous preform with reacting gases that undergo chemical transformation at high temperatures to deposit the ceramic phase within the pores, ultimately leading to a dense composite. The conventional CVI process in composite manufacturing needs to follow an isothermal approach to minimize temperature differences between the external and internal surfaces of the preform, ensuring that reactive gases infiltrate internal pores before external surfaces seal. Here, this study addresses the challenge of premature pore closure in CVI processes through microwave heating. A frequency-domain microwave solver is developed in OpenFOAM to investigate volumetric heating mechanisms within the preform. Through numerical studies, we demonstrate the capability of microwave heating of creating an inside-out temperature inversion. This inversion accelerates reactions proximal to the preform center, effectively mitigating the risk of premature external pore closure and ensuring uniform densification. The results reveal a significant enhancement in temperature inversion when high-permittivity reflectors are incorporated to generate resonant waves. This microwave heating strategy is then coupled with high-fidelity direct numerical simulation (DNS) of reacting flow, enabling the analysis of resulting densification processes. The DNS includes detailed chemistry and realistic diffusion coefficients. The numerical results can be used to estimate the impact of microwave-induced temperature inversion on densification in productions.

42 ENGINEERING↗
DOE OSTI2024

Modeling Microwave-Enhanced Chemical Vapor Infiltration Process for Preventing Premature Pore Closure

The chemical vapor infiltration (CVI) process involves infiltrating a porous preform with reacting gases that undergo chemical transformation at high temperatures to deposit the ceramic phase within the pores, ultimately leading to a dense composite. The conventional CVI process in composite manufacturing needs to follow an isothermal approach to minimize temperature differences between the external and internal surfaces of the preform, ensuring that reactive gases infiltrate internal pores before external surfaces seal. This study addresses the challenge of premature pore closure in CVI processes through microwave heating. A frequency-domain microwave solver is developed in Open-FOAM to investigate volumetric heating mechanisms within the preform. Through numerical studies, we demonstrate the capability of microwave heating of creating an inside-out temperature inversion. This inversion accelerates reactions proximal to the preform center, effectively mitigating the risk of premature external pore closure and ensuring uniform densification. The results reveal a significant enhancement in temperature inversion when high-permittivity reflectors are incorporated to generate resonant waves. This microwave heating strategy is then coupled with high-fidelity direct numerical simulation (DNS) of reacting flow, enabling the analysis of resulting densification processes. The DNS simulation includes detailed chemistry and realistic diffusion coefficients. The numerical results can be used to estimate the impact of microwave-induced temperature inversion on densification in productions.

Ge, Wenjun↗
DOE OSTI2024

DECOVALEX-2023: Task F1 Final Report

DECOVALEX-2023 Task F is a comparison of models and methods for post-closure performance assessment (PA) of a deep geologic repository for radioactive waste. The general aims of Task F are to build confidence in the models, methods, and software used for PA and to stimulate additional research and development in PA methodologies. The task objectives are to motivate development of PA modelling skills and capabilities, to examine the influence of model choices on calculated repository performance, and to compare the uncertainties introduced by model choices to other sources of uncertainty. Task F involves no actual experiment or site. It is a PA modelling exercise that requires the conceptual development of hypothetical repository designs and geologic settings. Because three of the teams were interested in salt and the rest of the teams were interested in crystalline rock, Task F was split into two branches: Task F1 for crystalline rock and Task F2 for salt. This report is for Task F1, crystalline rock. Teams from seven countries (Canada, Czech Republic, Germany, Korea, Sweden, Taiwan, and United States) participated in Task F1. The teams worked together to define the features, events, and processes of the reference case repository and established a set of performance measures. In addition, they defined a set of benchmark problems designed to test and compare modelling capabilities for fracture flow and transport at different scales. The repository design and benchmark problems are documented in a Task Specification that evolved over time as the group honed the specifications. The benchmark problems verified that each team can aptly model flow and transport in fractured media in 1-, 2-, and 3-dimensions. Two general approaches were used for the 3-dimensional benchmarks: discrete fracture network (DFN) and equivalent continuous porous medium (ECPM). DFN modelling involves explicit meshing of each fracture while ECPM modelling aims to capture the effective porosity and directional permeability of each cell in a space-filling mesh as affected by intersecting fractures. In some models, a combination of the two is used, i.e., DFN for large known fractures and ECPM for the rest of the domain. Transport is solved by using either the advection-dispersion equation or particle tracking. Although some variation is observed among model breakthrough curves in the benchmark problems, there is strong agreement in breakthrough behaviour up to at least the 75 th percentile for all benchmarks. At the 90 th percentile, breakthrough results show larger differences, suggesting several models retain substantially higher fractions of tracer in regions of slower moving water. In addition to the flow and transport benchmarks, several teams completed the source term benchmark, verifying capabilities for modelling radionuclide decay and ingrowth, waste package breach, instant release fractions, fuel matrix degradation rates, and radionuclide solubility limitations. The reference case is conceptualized as a generic spent fuel repository at a depth of 450 m in fractured crystalline rock. The repository has 50 parallel backfilled drifts, each with 50 deposition holes 6 m apart. Each deposition hole contains a 4-PWR waste package and bentonite buffer. The rock domain is 5 km in length, 2 km in width, and 1 km in depth. It has 6 deterministic fractured deformation zones and a multitude of stochastic fractures. Teams generally used the ECPM approach for the entire rock or a hybrid approach in which the deterministic fracture zones are modelled with a DFN and the rest of the rock is modelled by ECPM. Of the reference case problems specified, only the results of the initial reference case problem are compared in this report. The initial problem focuses on transport from the deposition holes to the surface, i.e., it neglects waste package performance. Tracers are released at all waste package locations at time zero and tracked for their releases to the near field and ground surface. The water fluxes calculated at the ground surface entry and exit regions of the domain are similar for all models except for two that have considerably lower fluxes. For tracer transport, large differences are observed among models in the magnitude of tracer transported. Much of the difference appears to be due to how the repository is implemented and hence the different degrees of repository simplification. Models that exclude the drifts, buffer, and backfill from the domain tend to show greater release of tracers and radionuclides from the repository. The initial study presented here indicates that major differences in modelling important processes within the repository (e.g., diffusion through buffer and backfill) can produce broadly different release and transport results, especially when those processes are excluded. Even for the models that included all specified features, events, and processes, the results show significant differences and demonstrate the importance of examining multiple modelling approaches in performance assessment. The differences in results observed in this study are expected to motivate teams to either increase complexity in future versions of the reference case models or to improve methods to account for the effects of simplified features and processes. Either way, future improvements in these models are expected to produce results that more closely agree.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗