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265 records · Page 15

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DOE OSTI2024

A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations

The structure of the magnetic reconnection boundary, particularly the presence of slow-mode shocks in the near-Earth magnetotail was studied by using magnetospheric multiscale (MMS) observations and 2.5D hybrid simulations. A total of 51 crossings of MMS from 2017 to 2021 were analyzed. We found that the detection percentage of slow-mode shocks in the near-Earth magnetotail is 41%–55%. Previous studies have only reported one slow-mode shock event in the near-Earth magnetotail and a slow-mode shock detection percentage of 10% or lower in the mid-to-distant magnetotail. It was observed that if the high-energy beam region data is removed from the slow-mode shock downstream observations then the detection of slow-mode shocks reduces, implying that the kinetic effects play an important role in the detection of slow-mode shocks. For the crossings where the interface was not identified as a slow-mode shock, it was found that the turbulence in those crossings can change the mass flux values and disrupt the detection of slow-mode shock. However, the macroscopic slow-mode shock-like structure stably exists around the magnetic reconnection interface, as most of the conditions for slow-mode shocks were satisfied. This result suggests that slow-mode shocks are a general feature of magnetic reconnection geometry. We find that the lack of detection of slow-mode shocks in previous observations and simulations can be explained by taking into account the kinetic structure of slow-mode shocks and the presence of turbulence.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Machine-learning-assisted deciphering of microstructural effects on ionic transport in composite materials: A case study of Li 7 La 3 Zr 2 O 12 -LiCoO 2

The effective diffusivity of ionic species in multiphase materials is critical for the design and function of composite materials for electrochemical energy storage. In practice, effective diffusivity depends sensitively not only on the intrinsic diffusivities of constituting materials but also on their topological arrangement; nevertheless, these coupled contributions are oversimplified in most analytical models. Here, we combine atomistically informed mesoscale modeling and machine learning (ML) analysis to unravel how such features affect effective diffusivity in two-phase composites. Using the Li 7 La 3 Zr 2 O 12 -LiCoO 2 composite solid-state battery cathode as a model system, we compute effective diffusivity for 600 distinct dense polycrystalline microstructures with different topological configurations of grains, grain boundaries, and heterointerfaces. We verify that in addition to atomic-scale variabilities, microstructural feature diversity can significantly impact effective transport properties. Across the ensemble of test microstructures, this often results in bimodal distributions of effective diffusivity that encompass two qualitatively distinct operating mechanisms, which we identify via flux analysis. An ML approach reveals that the most critical determining factors for effective diffusivity are the connectivity of bulk phases and their heterointerfaces. The role of ionic mobility at the heterointerfaces is also discussed. These insights highlight the combined importance of microstructure and interface engineering in tuning the transport properties of ionic species in composite materials. In conclusion, our framework can also be extended for understanding generic microstructure-property relationships in other complex multiphase materials.

25 ENERGY STORAGE↗

DOE OSTI2024

Machine Learning for Joint Quality Control

The use of lightweight material combinations has been highly demanded in manufacturing automotive structures. However, making robust dissimilar material joints of such lightweight materials is still challenging. A significant barrier to achieving high-quality and repeatable joint performance is a deficient understanding of the relationship between the welding process, joint attributes, and joint performance. In this context, welding factors refer to material, equipment, environment, and process parameters, while joint features comprise specific microstructural attributes of the weld such as nugget size, heat affected zone (HAZ) topology, intermetallic layer thickness, and sheet thickness reduction. Joint performance is quantified in terms of strength (e.g., tensile shear, coach peel, cross-tension), weld size, and hardness, among other factors. While there have been many attempts to establish this process-structure-property relationship by developing a model derived from the associated physics and first principles, the complexity of the joining processes compounded by the complex interactions with different materials in an automotive assembly line environment, has hindered the usefulness of such attempts. The complexity is further exacerbated using different stacking materials, especially comprising dissimilar material combinations. In practice, the common approach has been the laborious process of creating welds, characterizing them, and then physically testing them through experimentation. With the emergence of artificial intelligence (AI) methods, an alternative pathway to eliciting the desired process-structure-property relationship at an accelerated pace is to use a data-driven approach by employing machine-learning (ML) techniques. This approach is benefitted by the availability of large streams of data, generated through years of research and testing by original equipment manufacturers, in the form of material, process, environmental, equipment, microstructural, and bulk-scale performance information from multimodal, multiscale sensors making measurements from laboratory-scale to production-scale processes. During Phase I efforts, which ended in fiscal year (FY) 2021, the Oak Ridge National Laboratory and Pacific Northwest National Laboratory (ORNL/PNNL) team demonstrated the effectiveness of different ML/AI frameworks in modeling complex relationships between resistance spot welding (RSW) process parameters, weld attributes, and joint properties using a subset of data from General Motors (GM). In FY 2022, the project team further refined and expanded their respective ML models to analyze additional welds with new weld stack-ups and materials to enhance the ML model predictive capability. ORNL extended its unified deep neural networks (DNN) ML training and prediction framework with new data streams of process parameters, and PNNL extended its model describing RSW process parameters’ associations with weld attributes. In FY 2023, the project team completed the development of the AI/ML architecture for analyzing aluminum/steel joints manufactured by GM via RSW and transitioned into the inline welding quality monitoring task for steel/steel RSW joints provided by GM.

36 MATERIALS SCIENCE↗

NASA NTRS2021

Mineral Dust Cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry Model (Monarch) Version 2.0

We present the dust module in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) version 2.0, a chemical weather prediction system that can be used for regional and global modeling at a range of resolutions. The representations of dust processes in MONARCH were upgraded with a focus on dust emission (emission parameterizations, entrainment thresholds, considerations of soil moisture and surface cover), lower boundary conditions (roughness, potential dust sources), and dust–radiation interactions. MONARCH now allows modeling of global and regional mineral dust cycles using fundamentally different paradigms, ranging from strongly simplified to physics-based parameterizations. We present a detailed description of these updates along with four global benchmark simulations, which use conceptually different dust emission parameterizations, and we evaluate the simulations against observations of dust optical depth. We determine key dust parameters, such as global annual emission/deposition flux, dust loading, dust optical depth, mass-extinction efficiency, single-scattering albedo, and direct radiative effects. For dust-particle diameters up to 20 µm, the total annual dust emission and deposition fluxes obtained with our four experiments range between about 3500 and 6000 Tg, which largely depend upon differences in the emitted size distribution. Considering ellipsoidal particle shapes and dust refractive indices that account for size-resolved mineralogy, we estimate the global total (longwave and shortwave) dust direct radiative effect (DRE) at the surface to range between about −0.90 and −0.63 W m−2 and at the top of the atmosphere between −0.20 and −0.28 W m−2. Our evaluation demonstrates that MONARCH is able to reproduce key features of the spatiotemporal variability of the global dust cycle with important and insightful differences between the different configurations.

Dust cycle↗

DOE OSTI2022

Moiré engineering in van der Waals heterostructures

Isolated atomic planes can be assembled into a multilayer van der Waals (vdW) heterostructure in a precisely chosen sequence. These heterostructures feature moiré patterns if the constituent 2D material layers are stacked in an incommensurable way, due to a lattice mismatch or twist. This design-by-stacking has opened up the promising area of moiré engineering, a term that can be understood in two different perspectives, namely, (i) structural—engineering a moiré pattern by introducing twist, relative strain, or defects that affect the commensurability of the layers and (ii) functional—exploiting a moiré pattern to find and tune resulting physical properties of a vdW heterostructure. The latter meaning, referring to the application of a moiré pattern, is seen in the literature in the specific context of the observation of correlated electronic states and unconventional superconductivity in twisted bilayer graphene. The former meaning, referring to the design of the moiré pattern itself, is present in the literature but less commonly discussed or less understood. The underlying link between these two perspectives lies in the deformation field of the moiré superlattice. In this Perspective, we describe a path from designing a moiré pattern to employing the moiré pattern to tune physical properties of a vdW heterostructure. We also discuss the concept of moiré engineering in the context of twistronics, strain engineering, and defect engineering in vdW heterostructures. Although twistronics is always associated with moiré superlattices, strain and defect engineering are often not. Here, in this work, we demonstrate how strain and defect engineering can be understood within the context of moiré engineering. Adopting this perspective, we note that moiré engineering creates a compelling opportunity to design and develop multiscale electronic devices.

2D materials↗

NASA NTRS2009

GPS Navigation for the Magnetospheric Multi-Scale Mission

In 2014. NASA is scheduled to launch the Magnetospheric Multiscale Mission (MMS), a four-satellite formation designed to monitor fluctuations in the Earth's magnetosphere. This mission has two planned phases with different orbits (1? x 12Re and 1.2 x 25Re) to allow for varying science regions of interest. To minimize ground resources and to mitigate the probability of collisions between formation members, an on-board orbit determination system consisting of a Global Positioning System (GPS) receiver and crosslink transceiver was desired. Candidate sensors would be required to acquire GPS signals both below and above the constellation while spinning at three revolutions-per-minute (RPM) and exchanging state and science information among the constellation. The Intersatellite Ranging and Alarm System (IRAS), developed by Goddard Space Flight Center (GSFC) was selected to meet this challenge. IRAS leverages the eight years of development GSFC has invested in the Navigator GPS receiver and its spacecraft communication expertise, culminating in a sensor capable of absolute and relative navigation as well as intersatellite communication. The Navigator is a state-of-the-art receiver designed to acquire and track weak GPS signals down to -147dBm. This innovation allows the receiver to track both the main lobe and the much weaker side lobe signals. The Navigator's four antenna inputs and 24 tracking channels, together with customized hardware and software, allow it to seamlessly maintain visibility while rotating. Additionally, an extended Kalman filter provides autonomous, near real-time, absolute state and time estimates. The Navigator made its maiden voyage on the Space Shuttle during the Hubble Servicing Mission, and is scheduled to fly on MMS as well as the Global Precipitation Measurement Mission (GPM). Additionally, Navigator's acquisition engine will be featured in the receiver being developed for the Orion vehicle. The crosslink transceiver is a 1/4 Watt transmitter utilizing a TDMA schedule to distribute a science quality message to all constellation members every ten seconds. Additionally the system generates one-way range measurements between formation members which is used as input to the Kalman filter. In preparation for the MMS Preliminary Design Review (PDR), the Navigator was required to pass a series of Technology Readiness Level (TRL) tests to earn the necessary TRL-6 classification. The TRL-6 level is achieved by demonstrating a prototype unit in a relevant end-to-end environment. The IRAS unit was able to meet all requirements during the testing phase, and has thus been TRL-6 qualified

Bamford, William↗

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 OSTI2022

Demonstrate Improved Ag Diffusion and Describe the Basis for Pd Penetration Modeling in SiC

In past work, an effective diffusivity coefficient was determined for Ag transport through the silicon carbide layer of a tristructural isotropic fuel particle. The effective diffusivity coefficient accounts for the microstructure of the silicon carbide and includes both bulk diffusion and grain boundary diffusion of Ag. In this report, the model has been improved by accounting for the enhanced concentration of vacancies in the bulk due to irradiation, which substantially influence bulk diffusivity at low temperatures. To improve the BISON model and make it fission rate dependent, effective diffusivity calculations have been performed that incorporate the radiation modified bulk diffusivity. The microstructure and irradiation-dependent effective diffusivity has also been implemented into BISON, and its predictions for Ag release from tristructural isotropic fuel have been successfully compared to AGR-1 post irradiation measurements. Moreover, a new feature has been developed in the Multiphysics Object-Oriented Simulation Environment (MOOSE) to account for different grain boundary types. The Ag diffusivity in 5 (210)/[001] grain boundaries has been computed and was found to be greater than in random high-angle grain boundaries. The presence of the fission product Pd can also have an important effect on the properties of the silicon carbide layer in tristructural isotropic particles. The penetration of Pd into the silicon carbide layer causes a corrosion reaction that can lead to the failure of the silicon carbide layer; however, this corrosion reaction is not well understood. To enable an improved understanding of the mechanism, ab-initio molecular dynamics simulations of Pd interaction with bulk silicon carbide have been performed. The improved understanding of the reaction will form a basis for future improvements to the BISON’s Pd penetration failure model

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Disruption of histone acetylation homeostasis reveals multilayered chromatin regulation for transcriptional resiliency

Background Epigenetic modifications, nucleosome occupancy, and three-dimensional chromatin architecture collectively create a multi-layered, highly interactive regulatory system for controlling genomic functionality. Dysregulation of epigenetic processes leads to a plethora of abnormalities including disease states. Therapies focused on epigenetic modulation can alter gene expression to correct dysfunction, though the perpetuation of these states and the relationships among chromatin regulatory layers is not well understood. Results Here, we investigated global and local chromatin structural and functional responses after acute histone deacetylase inhibitor treatment (suberoylanilide hydroxamic acid) in lung cancer cells across time. Treatment substantially increased global histone acetylation resulting in a pervasive but not distinctive signature. The spread of acetylation did not significantly impact global chromatin accessibility, and nucleosome remodeling largely occurred at finer scales in functionally relevant genomic regions. Indeed, both H 3 K 4 trimethylation, a mark of active transcription, and gene expression changes were altered in a controlled locus-specific manner, suggesting aberrant acetylation indirectly leads to balanced and bidirectional gene expression profiles from tighter regulation of other chromatin features. HDACi treatment induced (13%) genomic rearrangement in chromatin compartmentalization and moderate weakening of topologically associating domains. Conclusions Continuous wavelet analysis of these features demonstrates that scale-dependent, locus-specific factors influence the relationship between chromatin architecture and functional output, suggesting that regulation of transcription and nucleosome remodeling is not entirely (nor linearly) dependent upon large scale compartment exchange. Structural and functional responses are most pronounced early after treatment with partial persistence of differential local chromatin features and expression later in time; this highlights the plasticity of chromatin regulation, which may have implications for the efficacy of epigenetic treatments. These results demonstrate the effectiveness of multi-layered regulation of transcription: in resilient systems, disruption of one chromatin feature does not distort the regulation of other features in supporting a transcriptional program that allows for survival.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2002

Analysis of High Order Difference Methods for Multiscale Complex Compressible Flows

Accurate numerical simulations of complex multiscale compressible viscous flows, especially high speed turbulence combustion and acoustics, demand high order schemes with adaptive numerical dissipation controls. Standard high resolution shock-capturing methods are too dissipative to capture the small scales and/or long-time wave propagations without extreme grid refinements and small time steps. An integrated approach for the control of numerical dissipation in high order schemes with incremental studies was initiated. Here we further refine the analysis on, and improve the understanding of the adaptive numerical dissipation control strategy. Basically, the development of these schemes focuses on high order nondissipative schemes and takes advantage of the progress that has been made for the last 30 years in numerical methods for conservation laws, such as techniques for imposing boundary conditions, techniques for stability at shock waves, and techniques for stable and accurate long-time integration. We concentrate on high order centered spatial discretizations and a fourth-order Runge-Kutta temporal discretizations as the base scheme. Near the bound-aries, the base scheme has stable boundary difference operators. To further enhance stability, the split form of the inviscid flux derivatives is frequently used for smooth flow problems. To enhance nonlinear stability, linear high order numerical dissipations are employed away from discontinuities, and nonlinear filters are employed after each time step in order to suppress spurious oscillations near discontinuities to minimize the smearing of turbulent fluctuations. Although these schemes are built from many components, each of which is well-known, it is not entirely obvious how the different components be best connected. For example, the nonlinear filter could instead have been built into the spatial discretization, so that it would have been activated at each stage in the Runge-Kutta time stepping. We could think of a mechanism that activates the split form of the equations only at some parts of the domain. Another issue is how to define good sensors for determining in which parts of the computational domain a certain feature should be filtered by the appropriate numerical dissipation. For the present study we employ a wavelet technique introduced in as sensors. Here, the method is briefly described with selected numerical experiments.

Sjoegreen, Bjoern↗

NASA NTRSDate not supplied

Toughening of Boron Carbide Composites by Hierarchical Microstructuring

Due to a unique combination of properties including high hardness, low density, chemical and thermal stability, semi-conductivity, and high neutron absorption, boron carbide (B4C) is a potential candidate for various applications involving extreme environment. However, B4C’s current application is limited because of its low fracture toughness. In this study, a hierarchical microstructure design with features including TiB2 grains and graphite platelets was used to toughen B4C by simultaneously utilizing multiple toughening mechanisms including crack deflection, bridging, and micro-crack toughening. Using field-assisted sintering technology (FAST), B4C composites with dense and hierarchical microstructure were fabricated. Previously, the fracture toughness of fabricated B4C composites was measured at micro-scale using micro-indentation to have up to 56% improvement. In this work, the B4C composites’ fracture toughness was characterized at macro-scale using four-point bending methods and compared with previous results obtained at micro-scale. Micromechanics modeling of fracture behaviors for B4C-TiB2 composites was also performed to evaluate the contributions from experimentally observed toughening mechanisms. From four-point bending tests, B4C composites reinforced with both TiB2 grains (~15 vol%) and graphite platelets (~8.7 vol%) exhibited the highest fracture toughness enhancement from 2.38 to 3.65 MPa∙m^(1/2). The measured values were lower than those obtained using micro-indentation but maintained the general trends. The discrepancy between the indentation and four-point bending test results originated from the complex deformation behaviors triggered by the high contact load during indentation tests. Through micromechanics modeling, introduced thermal residual stress due to thermal expansion mismatch between B4C and TiB2, and weak interphases at B4C-TiB2 boundaries were identified as the main causes for experimentally observed toughness enhancement. These results proved the effectiveness of hierarchical microstructure designs for B4C toughening and can provide reference for the future design of B4C composites with optimized microstructures for further fracture toughness enhancement.

composites↗

DOE OSTI2025

Laser Powder Directed Energy Deposition of Steels for Nuclear Applications

This comprehensive investigation examines the structure–property relationships in two nuclear alloy systems—Alloy 709 (A709) austenitic stainless steel and Grade 92 (G-92) ferritic/martensitic (F/M) steel—manufactured via directed energy deposition (DED) for sodium-cooled fast reactor applications. This study establishes the fundamental mechanisms for controlling microstructures for optimizing the mechanical performance of additively manufactured nuclear materials through systematic heat treatment optimization and multiscale characterization. As-deposited A709 steel develops a complex multiscale strengthening architecture consisting of a fine cellular solidification structure with diameter of 2-3 µm within10–50 µm grains, elevated dislocation densities from rapid thermal cycling, and grain boundary precipitates that activate concurrent Hall–Petch, dislocation, and precipitation hardening mechanisms to achieve exceptional properties [yield strength (YS): 603 MPa, ultimate tensile strength (UTS): 844 MPa, Vickers hardness: 220 HV] that achieve a 44% superior strength compared to that of the wrought material. Heat treatments produce different results. Solution annealing (SA) dissolves the cellular structure and reduces the hardness to 190 HV. Precipitation treatment (PT) keeps the cellular structure but adds carbides, allowing the hardness to reach 205 HV. The best approach combines both treatments (SA+PT) and creates uniform precipitate distributions with M 23 C 6 carbides at the grain boundaries and MX carbonitrides in the matrix, achieving a hardness of 195 HV. However, directional differences persist, with a 12%–15% strength variation between orientations due to the inherited layered microstructural architecture that survives aggressive heat treatment. While tensile testing at 550°C demonstrates 40%–50% thermal softening with dynamic strain aging, DED A709 steel still maintains a 71% higher YS than that of the wrought material. Ion irradiation studies (100–400 dpa) of DED A709 steel reveal progressive radiation damage with increasing void density and radiation-induced segregation causing nickel enrichment and chromium depletion, which will ultimately compromise mechanical properties. As-deposited G-92 exhibits exceptional strength (UTS: 1650–1700 MPa, 430 HV) through a complex microstructure containing both ferrite and martensite phases, a high geometrically necessary dislocation (GND) density (17.04×10 14 /m 2 ), and fine carbides. Heat treatments create distinct changes. Normalizing produces fresh martensite with the highest hardness (460 HV) and an increased GND density (20.23×10 14 /m 2 ). Tempering develops dual precipitation systems and reduces the hardness to 290 HV. The optimal approach uses sequential normalizing plus tempering, achieving balanced properties with the lowest hardness (250 HV) and a reduced GND density (11.01×10 14 /m 2 ). A processing-dependent anisotropy is observed: horizontal specimens achieve superior ductile behavior, while vertical specimens exhibit brittle failure. A tempering heat treatment successfully mitigates this anisotropic behavior by transforming the hard martensitic as-deposited structure into tempered martensite enabling both horizontal and vertical specimens to exhibit similar stress–strain characteristics with visible necking behavior. Remarkably, testing at 550°C reveals a reversal in the anisotropy, where as-deposited specimens achieve near isotropy with superior thermal stability (a 15%–20% strength reduction), while tempered specimens develop an orientation dependence with a 25%–30% strength reduction. Both alloy systems demonstrate that DED processing creates specimens with a superior strength through refined microstructural features, though with distinct strengthening mechanisms—austenitic through cellular structures and precipitates versus F/M through phase transformations and precipitates. Heat treatment optimization requires alloy-specific approaches, with A709 benefiting from controlled precipitation while G-92 requires careful phase transformation control. The results show that DED manufacturing can produce nuclear materials with exceptional performance, but directional effects and temperature-dependent behavior must be carefully considered for reactor component design and qualification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Design, fabrication, and testing of a water-cooled tungsten-foam high-heat flux plasma-facing module

Here, we present a comprehensive study detailing the design, fabrication and testing a new modular “finger” component, featuring a hexagonal tungsten foam armor tile bonded to the top surface of the water-cooled finger unit. The mechanical, thermal, and particle transport properties of the tungsten foam armor were determined for a wide range of porosity and cell size, and then combined in an equivalent solid material with “effective” properties suitable for large-scale simulations. A series of 3-D multiphysics simulations for coupled thermal and mechanical behavior of the “finger” module are used to optimize the design before fabrication. The simulation results show that, for the expected experimental conditions, a safe range of heat flux and foam porosity is possible for both water-cooling and helium-cooling. Based on these multiphysics simulations, an optimum finger cooling module has been fabricated at Ultramet, Inc. A series of experiments were designed to study the thermomechanical performance of the “finger” unit under steady-state and transient thermal loading at surface heat flux up to 16 MW/m 2 in the High Energy Flux Test facility (HEFTY) at UCLA. Testing conditions included low-power, steady-state plasma exposure at 6 MW/m 2 , followed by severe high heat flux cycles at 16 MW/m 2 . Results of these tests are explained through comparisons with multiphysics simulations. To eliminate the effects of thermomechanical damage carried out on a module on its thermo-mechanical response on a subsequent test, three different yet identically designed and fabricated finger modules were tested. The results show that the current design is capable of cooling the walls of fusion devices at surface heat flux up to 6 MW/m 2 without failure, and that the failure heat flux limit is below 16 MW/m 2 .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗