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DOE OSTI2024

Computational Analysis of Hydraulic Efficiency of Michigan DOT Cover C

Drainage structures are used to capture stormwater runoff in streets and highways in urban environments. These drainage structures, which typically consist of catch basins with grates, inlets, or combination grates/inlets, collect stormwater runoff and discharge through buried conveyance systems. They are strategically placed for public safety in curb and gutter systems to provide efficient drainage of water from roadways and thus reduce the risk of hydroplaning. The performance of drainage structures is measured in terms of hydraulic efficiency, which is defined as the percentage of flow captured by the basin as compared to the total flow drainage to the structure. Understanding of the performance of these drainage structures helps designers properly space inlets to promote an economic design that ensures the safety of the traveling public. The current design methodology used to determine drainage structure follows guidelines established in the current Michigan Department of Transportation (MDOT) Drainage Manual (2006). The guidelines in the MDOT Drainage Manual were modeled after the Federal Highway Administration’s (FHWA) Hydraulic Engineering Circular 22 “Urban Drainage Design” (HEC-22). HEC-22 includes empirically derived equations to calculate the interception capacity of drainage structures for several commonly used grate configurations, such as the parallel bar, curved vane and tilt bar grates, which are based on a research study performed by Burgi et al. in the 1970s. MDOT uses several drainage structures to capture runoff that are detailed as Standard Plans. Many of these drainage structures utilize sinusoidal type grates that are not described in HEC-22. Physical modeling of these structures has been limited, posing the need to have them analyzed to verify their capture efficiency. Current MDOT practice is to assume a similar sized reticuline grate, as described in HEC-22, for capture efficiencies. Until recently, evaluating the hydraulic performance of drainage structures was limited to physical modeling in a hydraulics laboratory. With advances in engineering software and computing power, computational fluid dynamics (CFD) modeling has become a more cost-effective alternative. The Federal Highway Administration (FHWA) provides states the option to evaluate their drainage structures using CFD through the Transportation Pooled Fund Program. This study, “Computational Analysis of Hydraulic Efficiency of Michigan DOT Cover C,” was carried out using the pooled fund. MDOT’s Cover C was chosen as the first test candidate, given its similar sinusoidal pattern to other MDOT grates, but it is typically used for high-volume, higher speed applications. A similar version, Cover CX, is used on interstate highways but does not have traverse bars for bicycle safety. Additional grates may be considered for evaluation in the future.

42 ENGINEERING↗

DOE OSTI2021

Proteomic Profiling of the Substantia Nigra to Identify Determinants of Lewy Body Pathology and Dopaminergic Neuronal Loss

Proteinaceous aggregates containing ?-synuclein protein called Lewy bodies in the substantia nigra is a hallmark of Parkinson’s disease. The molecular mechanisms of Lewy body formation and associated neuronal loss remain largely unknown. To gain insights on proteins and pathways associated with Lewy body pathology, we performed quantitative profiling of the proteome. We analyzed substantia nigra tissue from 51 subjects arranged into three groups: cases with Lewy body pathology, Lewy body-negative controls with matching neuronal loss and controls with no neuronal loss. Using label-free LC-MS/MS approach we quantified the 2,963 most abundant proteins. Statistical testing for differential protein abundance, followed by pathway enrichment and Bayesian learning of the causal network structure were performed to identify likely drivers of Lewy body formation and dopaminergic neuronal loss. The identified pathways include (1) Arp2/3 complex-mediated actin nucleation; (2) synaptic function; (3) poly(A) RNA binding; (4) basement membrane and endothelium; and (5) hydrogen peroxide metabolic process. According to the data, the endothelial/basement membrane pathway is tightly connected with both pathologies and likely to be one of the drivers of the neuronal loss. The poly(A) RNA-binding proteins, including the ones relevant to other neurodegenerative disorders (e.g. TDP-43 and FUS) have strong inverse correlation with Lewy bodies and may reflect alternative mechanism of nigral neurodegeneration.

Petyuk, Vladislav A.↗

DOE OSTI2024

The Challenge of Characterizing High-Concentration Electrolytes at the Molecular Level: A Perspective

High-concentration electrolytes (HCEs) are promising materials composed of highly concentrated salt solutions in organic solvents. HCEs have many desirable properties and are particularly important in the field of batteries. However, the number of ways in which these materials can be tuned is very large, which is crucial for tailored electrolyte design. Moreover, the molecular characterization of HCEs is challenging both experimentally and computationally, but it is necessary for their rational design. Therefore, currently the structure–property–performance relationship of these electrolytes has not been directly derived from their characterization. Here, in this Perspective, we present a brief overview of the HCEs and discuss the state-of-the-art characterization methods used to study them at the molecular level. We also address the challenges associated with these methods, including both experimental techniques and computational tools currently available. Emphasis is placed on methods aimed at understanding the physical phenomena that govern the molecular structure and dynamics occurring on the subnanosecond and nanometer time and length scales. Finally, we discuss new strategies for obtaining a comprehensive characterization of HCEs at the molecular level.

electrolytes↗

DOE OSTI2021

Impurity Control in Catalyst Design: The Role of Sodium in Promoting and Stabilizing Co and Co 2 C for Syngas Conversion

The design of supported heterogeneous catalysts requires a detailed understanding of the structure and chemistry of the active surface. Although the chemical components of the active phase, support material, and process feed are typically considered to be the most important factors governing catalyst structure and performance, many common commercial supports contain trace impurities, which can have profound effects on catalyst properties. In this work, we study silica–supported cobalt–based catalysts, which are widely used in syngas conversion to value–added products. Supported metallic Co is a commercial Fischer–Tropsch catalyst, whereas Co 2 C has shown promise for the direct conversion of syngas to higher oxygenates. This study examines the effects of Na, a commonly detected support impurity and a frequently used promoter, on the structure and reactivity of Co and Co 2 C. We show that trace Na impurities significantly decrease catalyst activity of supported metallic Co, and that high Na concentrations result in Co 2 C formation and a loss in Fischer–Tropsch activity. However, in Co 2 C catalysts, Na plays an important role in stabilizing the Co 2 C phase, but excess Na decreases catalyst activity. We use insitu X–ray absorption spectroscopy to study Co 2 C formation and decomposition in the Na–free catalyst under carburization and reaction conditions. Lastly, the work reveals the importance of carefully controlling alkali metal content, particularly at trace levels, in catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Conduction band convergence and local structure distortion for superior thermoelectric performance of GaSb-doped n-type PbSe thermoelectrics

Achieving high-stability thermoelectric materials with excellent average power factor and figure of merit is crucial for maximizing the output power density and conversion efficiency of thermoelectric devices. In this study, GaSb is added to PbSe as an n-type dopant to form stable solid solutions. Doping with GaSb flattens the conduction band and reduces the energy difference between the Σ and L conduction bands, thereby significantly improving the Seebeck coefficient. Herein, the Ga and Sb atoms co-occupy the vacant Pb sites, unlike in the case of traditional single-element doping, as is verified by density functional theory calculations. The resultant structural distortion is confirmed via transmission electron microscopy. This local structure distortion caused by GaSb doping reduces the lattice thermal conductivity. Consequently, the Pb 0.99875 (GaSb) 0.00125 Se sample exhibits a record-high average power factor of ~22.37 μW cm −1 K −2 and a high average figure of merit of ~0.94 in the temperature range of 300‒873 K. Furthermore, the introduction of interstitial Cu and discordant Zn atoms further reduces the lattice thermal conductivity. The Pb 0.99875 (GaSb) 0.00125 Zn 0.01 Se 1.01 -0.3%Cu sample exhibits a low lattice thermal conductivity of ~0.4 W m −1 K −1 at 873 K and a record-high average figure of merit of ~1.01 in the temperature range of 300‒873 K.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Effect of second Si–O vibrational overtones/combinations on quantifying water in silicate and silica minerals using infrared spectroscopy, and an experimental method for its removal

Infrared spectroscopy (IR) is the most widely used analytical tool to quantify trace water in silicate and silica minerals. A prerequisite for highly accurate IR measurements of trace water is a good understanding of the effect of the second Si–O vibrational overtones/combination bands (2nd Si–O VOCBs) on the water peaks. Silicate and silica minerals can be divided as isolated (Q 0 ), paired (Q 1 ), ring (Q 2 ), chain (Q 1 or Q 2 ), sheet (Q 3 ) and framework (Q 4 ) structures according to the polymerization of their SiO 4 tetrahedral units, and the 2nd Si–O VOCBs of these different structural types attain different vibrational features which are expected to affect the water peaks to different extents. Here, we selected olivine (Q 0 ) and α-quartz (Q 4 ) as two endmember-like structural examples, performed extensive IR measurements on both pristine and heat-treated thin sections prepared for these two minerals, and explored the vibrational features of the 2nd Si–O VOCBs. In this work, we have found that the 2nd Si–O VOCBs are well separated from the water peaks in olivine, but severely overlap with the water peaks in α-quartz, confirming the different roles that the 2nd Si–O VOCBs play in quantifying trace water in silicate and silica minerals with different structural polymerizations. To remove the influence of the 2nd Si–O VOCBs (or any other species rather than water), an experimental protocol has been successfully developed, as approved by some fundamental equations and verified by the data of α-quartz in the literature. This development should lead to significant accuracy improvement in quantifying trace amounts of water in Earth and planetary materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Instrumentation and methods for efficient time-resolved X-ray crystallography of biomolecular systems with sub-10 ms time resolution

Time-resolved X-ray crystallography has great promise to illuminate structure–function relations and key steps of enzymatic reactions with atomic resolution. The dominant methods for chemically-initiated reactions require complex instrumentation at the X-ray beamline, significant effort to operate and maintain this instrumentation, and enormous numbers (∼10 5 –10 9 ) of crystals per time point. We describe instrumentation and methods that enable high-throughput time-resolved study of biomolecular systems using standard crystallography sample supports and mail-in X-ray data collection at standard high-throughput cryocrystallography synchrotron beamlines. The instrumentation allows rapid reaction initiation by mixing of crystals and substrate/ligand solution, rapid capture of structural states via thermal quenching with no pre-cooling perturbations, and yields time resolutions in the single-millisecond range, comparable to the best achieved by any non-photo-initiated method in both crystallography and cryo-electron microscopy. Our approach to reaction initiation has the advantages of simplicity, robustness, low cost, adaptability to diverse ligand solutions and small minimum volume requirements, making it well suited to routine laboratory use and to high-throughput screening. We report the detailed characterization of instrument performance, present structures of binding of N -acetylglucosamine to lysozyme at time points from 8 ms to 2 s determined using only one crystal per time point, and discuss additional improvements that will push time resolution toward 1 ms.

Indergaard, John A. (ORCID:000000022367699X)↗

DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗

DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Steam-Assisted Selective CO 2 Hydrogenation to Ethanol over Ru–In Catalysts

Multicomponent catalysts can be designed to synergistically combine reaction intermediates at interfacial active sites, but restructuring makes systematic control and understanding of such dynamics challenging. In this work, we unveil how reducibility and mobility of indium oxide species in Ru-based catalysts crucially control the direct, selective conversion of CO 2 to ethanol. When uncontrolled, reduced indium oxide species occupy the Ru surface, leading to deactivation. With the addition of steam as a mild oxidant and using porous polymer layers to control In mobility, Ru–In 2 O 3 interface sites are stabilized, and ethanol can be produced with superior overall selectivity (70 %, rest CO). Our work highlights how engineering of bifunctional active ensembles enables cooperativity and synergy at tailored interfaces, which unlocks unprecedented performance in heterogeneous catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Crack Growth Rate and Fracture Toughness Tests on Irradiated Ex-Plant Materials

The performance of structural materials is critical for the safe and economic operation of light water reactors. Exposed to neutron irradiation during service, the reactor core internal materials can undergo significant microstructural and microchemical changes, leading to irradiation hardening and embrittlement. To ensure the structural integrity and functionality of nuclear reactor components during long-term operation, material degradation and damage mechanisms must be understood and managed adequately. In this work, irradiated materials harvested from the decommissioned Zorita reactor were studied for their cracking susceptibility and fracture resistance as a function of irradiation dose up to 47 displacement per atom (dpa). The material is a Type 304 stainless steel sectioned from the baffle plates of this pressurized water reactor with 38 years of service. Crack growth rate and fracture toughness J-resistance (J-R) curve tests were performed in low-corrosion-potential environments at ~315°C. All samples behaved similarly under cyclic loading, and no deteriorated corrosion-fatigue behavior was observed in the test environments. Under constant stress intensity factors, most samples did not show elevated crack growth rates, suggesting an adequate stress corrosion cracking resistance in the test environments. However, an unstable cracking behavior was observed in a 47-dpa sample, resulting in significantly higher crack growth rates than expected at high stress intensity factors. Crack instability was also observed in a 0.06-dpa sample but did not lead to a sustained high crack growth rate. The impact of neutron irradiation was more evident in the fracture toughness J-R curve tests. As the dose increased, the J-R curve declined considerably and became very shallow at high doses. A fully intergranular fracture morphology was also observed among the high-dose samples ruptured in an air atmosphere at room temperature. This brittle fracture mode in the absence of high temperature water environment confirmed a high degree of embrittlement of this material resulting from its service exposure to neutron irradiation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Te/CdTe and Al/CdTe Interfacial Energy Band Alignment by Atomistic Modeling

A synergistic approach that incorporates first-principles atomistic modeling with numerical device simulations is used to systematically evaluate the role of heterointerfaces within metal-chalcogenide-based photovoltaic technologies. Two interfaces involving either a tellurium back contact or aluminum back electrode combined with a cadmium telluride absorber layer within cadmium-telluride-based thin-film solar cells are investigated on an atomic scale to determine the mechanisms contributing to variations in device performance. Electronic structures and predicted charge transport behavior with respect to cadmium and tellurium termination of the absorber layer are studied along the polar oriented CdTe{111} facets. The computational methodology reveals a noticeable contrast between the Schottky barrier forming Al/CdTe interface versus the Type I Te/CdTe heterojunction. Greater band bending features are exhibited by the cadmium termination as opposed to the tellurium termination for each interface case. Subsequent device modeling suggests that 3.6% higher photovoltaic conversion efficiency is achievable for the cadmium termination relative to the tellurium termination of the Te/CdTe interface. Based strictly on an idealistic representation, both interface models show the importance of atomic-scale interfacial properties for cadmium telluride solar cell device performance with their bulk properties being validated in comparison to published experimental data. Here, the synergistic approach offers a suitable method to analyze solar cell interfaces through a predictive computational framework for the engineering and optimization of metal-chalcogenide-based thin-film photovoltaic technologies.

Green’s function↗

DOE OSTI2021

Multicolor Photonic Pigments for Rotation-Asymmetric Mechanochromic Devices

Photonic crystals are extensively explored to replace inorganic pigments and organic dyes as coloring elements in printing, painting, sensing, and anti-counterfeiting due to their brilliant structural colors, chemical stability, and environmental friendliness. However, most existing photonic-crystal-based pigments can only display monochromatic colors once made, and generating multicolors has to start with designing different building blocks. In this work, a novel photonic pigment featuring highly tunable structural colors in the entire visible spectrum, made by the magnetic assembly of monodisperse nanorods into body-centered-tetragonal photonic crystals, is reported. Their prominent magnetic and crystal anisotropy makes it efficient to generate multicolors using one photonic pigment by magnetically controlling the crystal orientation. Further, the combination of angle-dependent diffraction and magnetic orientation control enables the design of rotation-asymmetric photonic films that display distinct patterns and encrypted information in response to rotation. The efficient multicolor generation through precise orientational control makes this novel photonic pigment promising in developing high-performance structural-colored materials and optical devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Facilitating Ionic and Electronic Conduction in Radical Polymers through Controlled Assembly

The major objectives, research performed, and significant results associated with this effort agree with the originally proposed work. That is, we have made significant advances in terms of both the experimental and computational thrusts of this effort, and these key results have allowed for us to have significant impact in the materials chemistry community. As is usual, this has led to even greater tangible product generation in the final year of the work relative to the first two years. Importantly, we have observed that the materials created under this effort have promising electronic and ionic conductivity properties along these lines, and we have developed the initial structure-property-performance relationships that offer future promise for these open-shell materials in advanced energy applications. One metric that represents this success is the number of publications that appear in notable journals regarding the work performed. Additionally, the team has been invited to present at many conferences of leading societies and at top academic institutions due to the work associated with this award. Finally, we anticipate that this will be of interest to multiple communities in the materials chemistry realm, and they will have broader impact into related technologies as well.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Structural origin of size effect on piezoelectric performance of Pb(Zr,Ti)O 3

Grain size effect plays a vital role in piezoelectric performance from both scientific and technological view. However, the underlying structural mechanism related to grain size is still unclear. In the present study, the structural mechanism of grain size effect on piezoelectric performance has been revealed in the prototype Pb(Zr, Ti)O 3 system by using in-situ synchrotron X-ray diffraction. The miniaturization of grain size tends to favor the appearance of higher symmetric tetragonal phase, while a single monoclinic phase is determined in the coarse-grained ceramics. The direct structural evidence reveals that both tetragonal and monoclinic phases in the fine-grained ceramics are less sensitive to the electric field, corresponding to the inferior piezoelectric performance, while the single monoclinic phase in the coarse-grained ceramics is more active to be driven by the electric field, generating good piezoelectric behavior. Both domain switching ability and lattice strain are suppressed with decreasing grain size, which directly leads to the deterioration in piezoelectric performance. In conclusion, the current results will benefit the structural understanding of the size effect of piezoelectric and other related systems.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Microstructural heterogeneity of the buffer layer of TRISO nuclear fuel particles

Tristructural isotropic (TRISO) nuclear fuel particles contain a layered spherical shell designed to retain fission products; however, failure occurs in rare cases—commonly initiated in the porous pyrocarbon buffer layer. Achieving a comprehensive understanding of the buffer-initiated failure mechanisms requires detailed characterization of the buffer porosity and its heterogeneous distribution across multiple length scales. Here we performed FIB-SEM tomography across the buffer layer thickness (~100 µm) to produce 3D reconstructions of the buffer microstructure with 50 nm spatial resolution. We found an average overall porosity of ~14%, which does not solely account for the low density of the buffer (50% of the theoretical density). Additionally, the local porosity and its fluctuation increase from the kernel interface towards the inner pyrocarbon (IPyC) layer, which we attribute to the chemical vapor deposition process conditions during the TRISO particle fabrication. Detailed characterization of the porous microstructure—including analysis of the pore size, distribution, shape, and orientation—provides insight into the process-structure-property-performance relations of TRISO nuclear fuel particles and will inform multiscale models designed to predict the failure of TRISO particles under irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Regional-scale fault-to-structure earthquake simulations with the EQSIM framework: Workflow maturation and computational performance on GPU-accelerated exascale platforms

Continuous advancements in scientific and engineering understanding of earthquake phenomena, combined with the associated development of representative physics-based models, is providing a foundation for high-performance, fault-to-structure earthquake simulations. However, regional-scale applications of high-performance models have been challenged by the computational requirements at the resolutions required for engineering risk assessments. The EarthQuake SIMulation (EQSIM) framework, a software application development under the US Department of Energy (DOE) Exascale Computing Project, is focused on overcoming the existing computational barriers and enabling routine regional-scale simulations at resolutions relevant to a breadth of engineered systems. This multidisciplinary software development—drawing upon expertise in geophysics, engineering, applied math and computer science—is preparing the advanced computational workflow necessary to fully exploit the DOE’s exaflop computer platforms coming online in the 2023 to 2024 timeframe. Achievement of the computational performance required for high-resolution regional models containing upward of hundreds of billions to trillions of model grid points requires numerical efficiency in every phase of a regional simulation. This includes run time start-up and regional model generation, effective distribution of the computational workload across thousands of computer nodes, efficient coupling of regional geophysics and local engineering models, and application-tailored highly efficient transfer, storage, and interrogation of very large volumes of simulation data. This article summarizes the most recent advancements and refinements incorporated in the workflow design for the EQSIM integrated fault-to-structure framework, which are based on extensive numerical testing across multiple graphics processing unit (GPU)-accelerated platforms, and demonstrates the computational performance achieved on the world’s first exaflop computer platform through representative regional-scale earthquake simulations for the San Francisco Bay Area in California, USA.

58 GEOSCIENCES↗

DOE OSTI2026

A Full-Induction Magnetohydrodynamics Solver for Liquid Metal Fusion Blankets in Vertex-CFD

Multiphysics modeling of liquid metal fusion blankets, which produce tritium and convert energy of neutrons created via fusion reactions into heat, is crucial for predicting performance, ensuring structural integrity, and optimizing energy production. While traditional blanket modeling of liquid metal flows during normal steady operating conditions commonly employs the inductionless approximation of the magnetohydrodynamics (MHD) equations, transient scenarios, when the plasma-confining magnetic field varies on millisecond time scales, require a full-induction MHD approach that dynamically evolves the magnetic field via the time-dependent induction equation. This paper presents the formulation, implementation, and initial verification of a full-induction MHD solver integrated within the open-source Vertex-CFD framework, which aims to achieve tight multiphysics coupling, a flexible software design enabling easy extension and addition of physics models, and performance portability across computing platforms. The solver utilizes finite element spatial discretization, implicit Runge–Kutta time integration, and an inexact Newton method to solve the resulting discrete nonlinear system, leveraging Trilinos packages for efficient computation. Verification against selected benchmark problems demonstrates accuracy and robustness of the solver. Furthermore, when the solver is applied to an idealized blanket model in 2.5D and full 3D, results obtained with Vertex-CFD are in good agreement with recently published quasi-2D simulations. These findings establish a computational foundation for future simulations of transient MHD phenomena in liquid metal blankets with Vertex-CFD, and open avenues for future extensions and performance optimizations.

Endeve, Eirik [ORNL] (ORCID:0000000312519507)↗