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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High Temperature Thick Film Sensor Development Based on Refractory Oxide Semiconductors

Solid-state, high-temperature sensing devices are required for accurate temperature, strain/stress, and failure monitoring for a range of advanced manufacturing, transportation, and military applications. High temperature conditions limit sensing strategies, where typically traditional metal and semiconductor materials are unstable, and the sensing options are limited to optical spectroscopy methods. In addition, strategies to embed the sensors directly within active components and protective thermal refractory are of interest that permit near-environment sensing which is not possible with the traditional electronic materials and components. Rare-earth chromite semiconductors exhibit some properties of interest for high temperature sensing technologies, such as: high microstructure and chemical stability, relatively high electronic conductivity at high temperatures, and relative thermal expansion coefficient matching to refractory thermal protective insulation (such as high-zirconia and -alumina ceramics). In this work, n-type and p-type doped lanthanum chromites compositions were computationally modelled and prepared, and the electrical, thermoelectric, and thermomechanical properties were measured for the obtained material systems. A few compositions were then processed into thick film health and temperature sensors and tested at high temperatures. To establish relative stability under harsh environment conditions, post- mortem microstructure and chemical composition was characterized after extended and cycle testing at high temperatures.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Multiscale Modeling of Silicon Carbide Cladding for Nuclear Applications: Thermal Performance Modeling

The complex multiscale and anisotropic nature of silicon carbide (SiC) ceramic matrix composite (CMC) makes it difficult to accurately model its performance in nuclear applications. The existing models for nuclear grade composite SiC do not account for the microstructural features and how these features can affect the thermal and structural behavior of the cladding and its anisotropic properties. In addition to the microstructural features, the properties of individual constituents of the composites and fiber tow architecture determine the bulk properties. Models for determining the relationship between the individual constituents’ properties and the bulk properties of SiC composites for nuclear applications are absent, although empirical relationships exist in the literature. Here, a hierarchical multiscale modeling approach was presented to address this challenge. This modular approach addressed this difficulty by dividing the various aspects of the composite material into separate models at different length scales, with the evaluated property from the lower-length-scale model serving as an input to the higher-length-scale model. The multiscale model considered the properties of various individual constituents of the composite material (fiber, matrix, and interphase), the porosity in the matrix, the fiber volume fraction, the composite architecture, the tow thickness, etc. By considering inhomogeneous and anisotropic contributions intrinsically, our bottom-up multiscale modeling strategy is naturally physics-informed, bridging constitutive law from micromechanics to meso-mechanics and structural mechanics. The effects that these various physical attributes and thermo-physical properties have on the composite’s bulk thermal properties were easily evaluated and demonstrated through the various analyses presented herein. Since silicon carbide fiber-reinforced SiC CMCs are also promising thermal–structural materials with a broad range of high-end technology applications beyond nuclear applications, we envision that the multiscale modeling method we present here may prove helpful in future efforts to develop and construct reinforced CMCs and other advanced composite nuclear materials, such as MAX phase materials, that can service under harsh environments of ultrahigh temperatures, oxidation, corrosion, and/or irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Enhancing the accuracy and generality of the Debye–Grüneisen Model: Optimizing the volume dependence for accurate predictions across varied compositions

In this work, we have introduced an optimized Debye-Grüneisen model that revolutionizes the determination of the Debye temperature and Grüneisen parameters. Unlike conventional methods, our model requires only the 0 K energy volume data for a material as input, eliminating the need to determine the bulk modulus and its pressure derivative, which often pose challenges due to numerical uncertainties. This unique feature sets our model apart from existing approaches and streamlines the process, enabling accurate predictions of thermal expansion behavior across various materials. To demonstrate its effectiveness, we showcase its excellent agreement with measured coefficients of thermal expansion (CTE) for the nickel-cobalt-chromium-aluminum-yttrium (Ni-Co-Cr-Al-Y) bond-coating system. Additionally, we apply our approach by conducting a high-throughput search for potential bond-coating materials among 90,000 compositions within the aluminum-cobalt-chromium-iron-nickel (Al-Co-Cr-Fe-Ni) system. From this extensive search, four compositions are synthesized, and the measured CTE values agree very well with theoretical predictions, hence validating our approach. In conclusion, the current optimized Debye-Grüneisen model combined with Density Functional Theory (DFT)-based thermodynamic database enables reliable and efficient high-throughput calculations of CTE of of a material without expensive phonon calculations.

Bond coating materials↗
DOE OSTI2021

AI to Predict Glass Compositions Satisfying Property and Cooling Rate Criteria

This project aimed to develop a predictive, artificial intelligence/machine learning-based model to identify glass compositions satisfying specified property requirements. Such a model would provide a systematic approach for narrowing down the nearly infinite range of possible compositions for glasses and minimize unnecessary experimental trial and error. A large empirical data set for training and testing the algorithm was obtained from the SciGlass database. It contains glass compositions and corresponding property data from a wide range of literature sources. However, the currently available form of this data, recently released under an open database license, is not conducive to easy querying and use. The data structure was deciphered and a customized parsing code developed to make this data more usable for the current and future work. Neural network models were developed and trained on viscosity data from the database and demonstrated potential for improving prediction accuracy over a traditional regression model.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Graphene oxide exposure alters gut microbial community composition and metabolism in an in vitro human model

Graphene oxide (GO) nanomaterials have unique physicochemical properties that make them highly promising for biomedical, environmental, and agricultural applications. Despite the increasing interest and the use of GO, assessments of its nanotoxicity have largely not interrogated its potential impact on the gut microbiome. This study addresses an important knowledge gap by investigating the impact of GO exposure- both at low (25 ppm) and high (250ppm) doses and fed (nutrient rich) and fasted (nutrient deplete) conditions- on the gut microbial community structure and function, using an in vitro human colon bioreactor model. 16S rRNA amplicon sequencing revealed that GO exposure resulted in a restructuring of community composition. 25 ppm GO induced a marked decrease in the Bacteroidota phylum and increased the ratio of Firmicutes to Bacteroidota (F/B). Untargeted metabolomics on the supernatants indicated that 25 ppm GO impaired microbial utilization and metabolism of substrates (amino acids, carbohydrate metabolites) and reduced production of beneficial microbial metabolites such as 5-hydroxyindole-3-acetic acid and GABA. Exposure to 250 ppm GO resulted in community composition and metabolome profiles that were very similar to the controls that lacked both GO and digestive enzymes, suggesting that high concentrations of GO may interact with digestive enzymes to form protein coronas, causing their depletion in the gut environment. Differential abundance analyses revealed that 3 genera from the phylum Bacteroidota (Bacteroides, Dysgonomonas, and Parabacteroides) were more abundant after 250 ppm GO exposure, irrespective of feed state. Integrative correlation network analysis indicated that the phylum Bacteroidota showed strong positive correlations to multiple microbial metabolites including GABA and 3-indoleacetic acid, are much larger number of correlations compared to other phyla. These results show that GO exposure has a significant impact on gut microbial community composition and metabolism and different mechanisms are at play for low and high GO concentrations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Voltage Mining for (De)lithiation-Stabilized Cathodes and a Machine Learning Model for Li-Ion Cathode Voltage

Advances in lithium-metal anodes have inspired interest in discovery of Li-free cathodes, most of which are natively found in their charged state. This is in contrast to today's commercial lithium-ion battery cathodes, which are more stable in their discharged state. In this study, we combine calculated cathode voltage information from both categories of cathode materials, covering 5577 and 2423 total unique structure pairs, respectively. The resulting voltage distributions with respect to the redox pairs and anion types for both classes of compounds emphasize design principles for high-voltage cathodes, which favor later Period 4 transition metals in their higher oxidation states and more electronegative anions like fluorine or polyanion groups. Generally, cathodes that are found in their charged, delithiated state are shown to exhibit voltages lower than those that are most stable in their lithiated state, in agreement with thermodynamic expectations. Deviations from this trend are found to originate from different anion distributions between redox pairs. In addition, a machine learning model for voltage prediction based on chemical formulas is trained and shows state-of-the-art performance when compared to two established composition-based ML models for material properties predictions, Roost and CrabNet.

25 ENERGY STORAGE↗
DOE OSTI2022

High‐Pressure and High‐Temperature Single‐Crystal Elasticity of Cr‐Pyrope: Implications for the Density and Seismic Velocity of Subcontinental Lithospheric Mantle

Abstract Single‐crystal X‐ray diffraction and Brillouin spectroscopy experiments were performed on a natural Cr‐pyrope (Prp 71.0 Alm 12.6 Sps 0.7 Grs 3.5 Uvr 12.2 ) at high pressure and high temperature up to 11.0 GPa and 800 K. Fitting the collected data to the third‐order finite strain equation yields bulk modulus ( K S 0 ), shear modulus ( G 0 ), their pressure ((∂ K S /∂ P ) T and (∂ G /∂ P ) T ) and temperature (( ∂K S /∂T ) P and ( ∂G/∂T ) P ) derivatives, K S 0 = 167.7(8) GPa, G 0 = 91.5(5) GPa, (∂ K S /∂ P ) T = 4.3(1), (∂ G /∂ P ) T = 1.4(1), ( ∂K S /∂T ) P = − 0.0175(1) GPa/K and ( ∂G/∂T ) P = − 0.0073(1) GPa/K. Using the obtained results, we examined whether the elastic properties of the Cr‐pyrope can be accurately calculated from those of endmembers including pyrope, almandine, grossular, and uvarovite assuming a linear relationship between elastic properties and composition (end‐member model). The results indicate that the end‐member model provides a sufficient approximation for the elastic properties of Cr‐pyrope in calculating the density and velocity of the subcontinental lithospheric mantle (SCLM). We modeled the densities and velocities of three typical types of SCLM (Archon, Proton, and Tecton) in order to investigate how the variation of chemical composition influences the SCLM. We obtained that the compositional change from the Archon to the Tecton increases the density of the SCLM significantly, which can be an important prerequisite for SCLM delamination. However, the compositional variation only slightly changes the velocity of the SCLM and the change is within the uncertainty of the calculation. Moreover, in comparison to the velocity, ρ / V P and ρ / V S are much more sensitive to the compositional change of the SCLM.

Xu, Jingui↗
DOE OSTI2026

Long-Length SiC/SiC Tube Irradiation Experiment at LWR Temperatures Inserted into HFIR

This report describes the pre-irradiation characterization and successful assembly of a High Flux Isotope Reactor (HFIR) irradiation experiment designed to assess radiation-induced lateral bowing of silicon carbide fiber–reinforced, silicon carbide matrix composite (SiC/SiC) components under a radial fast neutron flux gradient at representative light-water reactor (LWR) temperatures of approximately 300 °C. The over-arching goal of this work is to validate thermomechanical models of SiC/SiC composite fuel assembly distortion used to predict the extent of lateral bowing and dose at which bowing reaches a maximum value. Ultimately, these models are used to ensure that fuel assembly distortion does not block coolant channels, interfere with control rod/blade movements, and/or impact local reactivity. The experiment contains six cladding tube specimens that are approximately 558.5 mm long with representative pressurized water reactor (PWR) outer and inner diameters of 9.5 mm and 7.5 mm, respectively. The specimens were thoroughly characterized prior to irradiation using traditional dimensional inspection and surface profilometry so that these measurements can later be compared with similar measurements that will be made post-irradiation to determine radiation-induced deformations. Furthermore, fine engraving markers were inscribed along the outer surfaces of the specimen and mapped using analytic photography and a three-dimensional stage. This technique allowed for accurate measurements of the marker spacings, which can be compared with similar measurements that will be made post-irradiation to provide local radiation-induced strain mapping. A light curtain optical micrometer was also employed to measure the pre-irradiation bowing of each specimen. The experiment was successfully assembled and inserted into position VXF-19 for HFIR cycle 516, which began May 12, 2026. The experiment will be removed after cycle 519, which is currently scheduled to end in December 2026.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Development and Integration of Predictive Models for Manufacturing and Structural Performance of Carbon Fiber Composites in Automotive Applications (Final Report)

The goal of this project is to develop integrated, state-of-the-art, computational modeling tools based on an integrated computational materials engineering (ICME) methodology that are critically needed to enable structural carbon fiber (CF) applications in automobiles. These tools are designed to predict the manufacturing and structural performance of CF composites, including stochastic effects. During the first phase of the project, the manufacturing and structural performance tools, including a stochastic driver, were developed, calibrated, and validated against coupon and component level test results. For this project, from the manufacturing side, the development efforts were focused on high-pressure resin transfer molding (HP-RTM), a potentially game-changing manufacturing technology that is capable of reaching the 3-5-minute cycle time that is necessary for volume manufacturing of composites. On the structural performance side, the crashworthiness of CF structures was studied. Accordingly, computational tools were developed and validated. The resulting differences between the numerical predictions and the experimental results in the first phase of the project were required to be less than 15%. During the second phase of the project, the manufacturing and performance tools were integrated by mapping the manufacturing outcomes (e.g., fiber angles, residual stresses, degree of cure, and defects) into the structural models. Also, using the tools developed in this project, an automotive assembly currently manufactured in steel (2016 GM Malibu) was redesigned to be manufactured using advanced CF composites with the objective of comparing the performance of the CF assembly with the developed ICME model predictions. The weight, performance and cost of the CF assembly were compared with the baseline steel assembly as well. The CF underbody design was shown to be 30% lighter than the corresponding steel design. The CF underbody assembly withstood the side pole impact with less than half the intrusion for the steel assembly. Excellent correlations within 10% for both peak impact load and intrusion were observed between the numerical predictions and experimental results. Based on the cost models developed in the project, the cost increase per Kg saved was determined to be $22. The project team has prepared a total of 28 publications in various journals and conferences and broadly communicated the project results for the benefit of the composite industry. The project team has also prepared a total of 10 patent applications that were submitted to the US Patent and Trademark office.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hanford Site Composite Analysis: Recharge and Inventory Sensitivity Analyses for the A Trenches, BC Cribs and Trenches, and PUREX Area Vadose Zone Models

The main objectives of the composite analysis (CA) compliance case vadose zone (VZ) facet are to simulate transport of radionuclide releases from the surface to the water table, and to provide radionuclide transfer rates to groundwater for inclusion into the Plateau-to-River (P2R) saturated zone (SZ) model (CP-57037, Model Package Report: Plateau to River Groundwater Model, Version 8.3). The CA compliance case approach to address the scale and distribution of contaminant sources in the VZ facet is to subdivide the Hanford Site Central Plateau into 26 models that contain contaminant sources and liquid discharges likely to commingle during migration through the VZ to the water table

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Comparison of Side-on Peak Overpressure Predictions and Measurements for Type IV Composite Overwrapped Pressure Vessel Catastrophic Failure

This development of empirical data to support realistic and science-based input to safety regulations and transportation standards is a critical need for the hazardous material (HM) transportation industry. Current regulations and standards are based on the TNT equivalency model. However, real world experience indicates that use of the TNT equivalency model to predict composite overwrapped pressure vessel (COPV) potential energy release is unrealistically conservative. The purpose of this report is to characterize and quantify rupture events involving damaged COPV’s of the type used in HM transportation regulated by the Department of Transportation (DOT). This was accomplished using a series of five tests; 2 COPV tests for compressed natural gas (CNG), 2 COPV tests for hydrogen, and 1 COPV test for nitrogen. Measured overpressures from these tests were compared to predicted overpressures from a TNT equivalence model and blast curves. Comparison between the measurements and predictions shows that the predictions are generally conservative, and that the extent of conservatism is dominated by predictions of the chemical contribution to overpressure from fuel within the COPVs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The Misrepresentation of the Southern African Easterly Jet in Models and Its Implications for Aerosol, Clouds, and Precipitation Distributions

The southern African easterly jet (AEJ-S) is an important midtropospheric feature critical to understanding the tropical convective system over central Africa and the aerosol–cloud interactions over the southeast Atlantic Ocean. However, it remains unclear how well models represent the AEJ-S and its influence on aerosol transport, clouds, and precipitation distribution. Here, in this work, we use ground- and satellite-based observations and reanalysis datasets to assess the representation of AEJ-S in the Coupled Model Intercomparison Project phase 6 (CMIP6) models between September and October during the peak of midtropospheric winds, aerosol transport, clouds, and precipitation. We find that most CMIP6 models have difficulty accurately simulating the strength, position, and spatial distribution of the AEJ-S. Specifically, the AEJ-S is relatively weaker and at a slightly lower altitude in the ensemble of CMIP6 models than represented by observation and reanalysis datasets. To assess the influence of the misrepresented the AEJ-S on CMIP6-simulated aerosol, clouds, and precipitation distributions, we performed composite analyses using models with low and high biases based on the estimates of their midtropospheric easterly wind speed. We find that the misrepresentation of the AEJ-S in CMIP6 models is associated with the overestimation of clouds and precipitation over central Africa, the underestimation of clouds over the southeast Atlantic Ocean, and the limitation of aerosol transport over the continent or the deviation of its spatial distribution from the typical zonal transport over the Atlantic Ocean. Because aerosols, clouds, and precipitation are important components of the regional climate system, we conclude that accurate representation of the AEJ-S is essential over central Africa and the southeast Atlantic Ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

High Density Thermal Energy Storage by Highly Stable Glauber’s Salt Composites with Low Supercooling for Building-Scale Application

Phase change materials (PCM) are a promising candidate for thermal energy storage in building infrastructure, enabling grid-integrated peak load shaving by utilizing energy production in off-peak hours. Glauber’s salt (Na2SO4.10H2O, Sodium sulfate hydrate) are highly attractive due to their high energy storage capacity and low cost but Glauber’s salt suffers from long-standing challenges including high supercooling ( > 15oC) and low thermal cyclic stability, which is a major setback for practical building-scale energy storage applications. Here, we developed a unique ionic molecular nucleating agent for Glauber’s salt which reduced the supercooling temperature to less than 5oC. By combining this nucleated SSD with a polymer, phase segregation of salt hydrate for over 100 thermal cycles without degradation was achieved. Key was that the polymer confined the SSD crystals, preventing phase separation. In addition, we applied our stable Glauber’s salt composite in a miniatured building model and demonstrate that our PCM composite can be utilized for real-world building infrastructure.

additives↗
DOE OSTI2025

Fully‐Printed Ion Sensor Arrays for Measuring Agricultural Nitrogen and Potassium Concentrations Using Nernstian and AI Models

Abstract The chemical composition of growing media is a key factor for plant growth, impacting agricultural yield and sustainability. However, there is a lack of affordable chemical sensors for ubiquitous nutrient ion monitoring in agricultural applications. This work investigates using fully printed ion‐sensor arrays to measure the concentrations of nitrate, ammonium, and potassium in mixed‐electrolyte media. Ion sensor arrays composed of nitrate, ammonium, and potassium ion‐selective electrodes and a printed silver‐silver chloride (Ag/AgCl) reference electrode are fabricated and characterized in aqueous solutions in a range of concentrations that encompass what is typical for agricultural growing media (0.01 m m –1 m ). The sensors are also tested in mixed‐electrolyte solutions of NaNO 3 , NH 4 Cl, and KCl of varying concentrations, and the recorded potentials are input into Nernstian and artificial neural network models to compare the prediction accuracy of the models against ground truth. The artificial neural network models demonstrated higher accuracy over the Nernstian model, and the model using only ion‐sensor inputs is 7.5% more accurate than the Nernstian model under the same conditions. By enabling more precise and efficient fertilizer application, these sensor arrays coupled to computational models can help increase crop yields, optimize resource use, and reduce environmental impact.

Goodrich, Payton [University of California Berkele↗
DOE OSTI2022

Hanford Site Composite Analysis Special Analysis: Inventory and Solid Waste Release Modeling for the LLBG Sensitivity Case - UCAQ-22-01 Inventory Discrepancies for 218-E-12B, 218-W-3A, and 218-W-3AE in the Hanford Site Composite Analysis

This environmental calculation file (ECF) documents the methodologies, assumptions, and results of four sensitivity analyses that reevaluate the representativeness of solid-waste radionuclide inventory and release rate from three solid waste sites included in the recently completed Hanford Site Composite Analysis (CA) (DOE-RL-2019-52, Composite Analysis for Low-Level Waste Disposal in the Hanford Site Central Plateau (FY 2020), Rev. 1) (hereinafter called the CA Update). Specifically, this ECF reevaluates the representativeness of the base case inventory and radionuclide waste release rates from three solid waste sites (i.e., 218-E-12B, 218-W-3A, and 218-W-3AE) and two radionuclides (i.e., carbon-14 [C-14] and technetium-99 [Tc-99]). These three waste sites and two radionuclides were identified as being the most significant contributors to groundwater contamination and dose in the CA Update for the Inner Area boundary at times periods after the compliance period. This ECF first evaluates the representativeness of the C-14 and Tc-99 inventory and second, radionuclide release rates for the three waste sites1. If the inventory and release rates assumed in the CA Update are determined to be not representative, as hypothesized in the CA Update, then this ECF evaluates the impact of more representative inventories and release rates on the transfer of radionuclides to the vadose zone. The four analyses described in this ECF are as follows: 1) 218-E-12B C-14 Inventory and Release Rate Sensitivity Case – Determine the representativeness of the C-14 inventory and associated waste release rate from the 218-E-12B waste site assumed in the CA Update. If the inventory and associated waste release are more appropriately characterized as being different from the assumptions in the CA Update, then update the predicted C-14 release rate and compare the results to the results presented in the CA Update (DOE/RL-2019-52); 2) 218-W-3A C-14 Inventory and Release Rate Sensitivity Case – Determine the representativeness of the C-14 inventory and associated waste release rate from the 218-W-3A waste site assumed in the CA Update. If the inventory and associated waste release are more appropriately characterized as being different from the assumptions in the CA Update, then update the predicted C-14 release rate and compare the results to the results presented in the CA Update (DOE/RL-2019-52); 3) 218-W-3AE Tc-99 Release Rate Sensitivity Case – Determine the representativeness of the Tc-99 inventory and associated waste release rate from the 218-W-3AE waste site assumed in the CA Update. If the inventory and associated waste release are more appropriately characterized as being different from the assumptions in the CA Update, then update the predicted Tc-99 release rate and compare the results to the results presented in the CA Update (DOE/RL-2019-52); 4) 218-W-3AE Tc-99 Release Footprint Sensitivity Case – Determine the representativeness of the Tc-99 waste area footprint for the 218-W-3AE waste site assumed in the CA Update. If the footprint of the waste is more appropriately characterized as being different from the assumptions in the CA Update, then update the predicted Tc-99 release rate and compare the results to the result presented in the CA Update (DOE/RL-2019-52).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multiscale Concurrent Atomistic-Continuum (CAC) modeling of multicomponent alloys

We report strengthening in complex multicomponent systems such as solid solution alloys is controlled primarily by the dynamic interactions between dislocation lines and heterogeneously distributed solute species. Modeling of extended defect length scales in such multicomponent systems becomes prohibitively expensive, motivating the development of reduced order approaches. This work explores the application of the Concurrent Atomistic-Continuum (CAC) method to model dislocation mobility in random alloys at extended length scales. By employing recently developed average-atom interatomic potentials, the average “bulk” material response in coarse-grained regions interacts with true random solute species in the atomistic-scale domain. We demonstrate that spurious stresses in domain resolution transition regions are eliminated entirely due to the CAC formulation. Simultaneously, the key details of local stress fluctuation due to randomness in the dislocation core region are captured, and fluctuating stress smoothly decays to the long-range dislocation stress field response. Dislocation mobility calculations, for line lengths over 400 nm, are computed as a function of alloy composition in the model FeNiCr system and compared to full molecular dynamics (MD). The results capture the composition-dependent trends, while reducing degrees of freedom by nearly 40%. This approach can be readily extended to any system described by an EAM potential and facilitates the study of large-scale defect dynamics in complex solute environments to support computational alloy design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Dark QCD: the Next Frontier in Dark Matter

There has been a surge of interest in hidden valley models with new, strong forces, sometimes called "dark QCD". These models propose asymmetric, composite dark matter in the form of "dark hadrons" that would evade direct and indirect bounds as well as typical collider DM searches for large missing transverse momentum accompanied by radiation. However, evidence of these models can still be found in collider datasets by targeting their unique phenomenological signatures, which include semi visible jets, emerging jets, and soft unclustered energy patterns. We will present the first experimental results for all of these signatures, which have made significant strides in exploring the vast space of dark QCD models. We will further discuss the prospects for dramatic improvements in sensitivity using machine learning.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗