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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 OSTI2021

Microstructure evolution, enhanced aging kinetics, and mechanical properties of AA7075 alloy after friction extrusion

In the present study we utilized Friction Extrusion (FE) a solid phase processing technique to produce fully consolidated dense 5 mm rods of AA7075 alloy. The combination of large shear stresses and temperatures at the tool-billet interface during the FE process resulted in the formation of dynamically recrystallized ~2.0 μm equiaxed grains and fine uniformly distributed stable η (MgZn 2 ) precipitates ~25–100 nm in size. Formation of such a microstructure resulted in lower solutionizing temperature and times (flash annealing) as compared to the conventionally extruded counterparts. Here we demonstrate for the first time that the solutionizing times for the T6 heat treatment of AA7075 can be reduced by three times using this FE process. In addition to being an energy efficient process, FE also serves to improve the performance of AA7075 alloys by retaining their strength while enhancing the ductility of the material. The tensile data for samples that were flash annealed and artificially aged after FE processing showed exceptional increase in ultimate tensile strength by over 19% and yield strength by over 59%, compared with an as-FE-processed sample.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Composition-dependent fracture energy in metallic glasses

The interplay between metallic glasses (MGs) mechanical properties, fracture energy (G), and glass-forming ability (GFA) and their dependence on alloy composition remain poorly understood. Here, we perform molecular dynamics simulations to investigate the intrinsic composition-dependence of G in Cu x Zr 100-x MGs (x = 20, 30, 40, 50, 64). The results indicate that the value of G increases with Cu content. In addition, it is revealed that MGs with higher G values display higher Poisson’s ratio (ν) and GFA, suggesting a close correlation between fracture toughness, mechanical properties, and GFA. This correlation between G, ν and GFA can be understood based on the fragility (m) of supercooled liquids, which is directly related to the structural heterogeneity in MGs. Larger m values are related to dynamic slowdown and supercooled liquid stabilization, which enhance GFA and the formation of pronounced structural heterogeneity, comprised of loosely packed regions that favor β-relaxation and the activation of shear transformation zones. Those concurrently promote the expansion of the plastic zone at the crack tip, enhancing the observed value of G. Furthermore, these simulation results shed light on the intrinsic relationship between fracture toughness, mechanical properties, and alloy composition in MGs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A machine learning framework for elastic constants predictions in multi-principal element alloys

On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in alloy design due to large compositional space, whereas on the other, they have presented enormous computational challenges for theory-based materials design, especially density functional theory (DFT), which is inherently computationally expensive even for traditional dilute alloys. In this paper, we present a machine learning framework, namely PREDICT (PRedict properties from Existing Database In Complex alloys Territory), that opens a pathway to predict elastic constants in large compositional space with little computational expense. The framework only relies on the DFT database of binary alloys and predicts Voigt–Reuss–Hill Young’s modulus, shear modulus, bulk modulus, elastic constants, and Poisson’s ratio in MPEAs. We show that the key descriptors of elastic constants are the A–B bond length and cohesive energy. The framework can predict elastic constants in hypothetical compositions as long as the constituent elements are present in the database, thereby enabling property exploration in multi-compositional systems. We illustrate predictions in a FCC Ni-Cu-Au-Pd-Pt system.

Linton, Nathan (ORCID:0000000315485613)↗
DOE OSTI2023

Shear-aligned large-area organic semiconductor crystals through extended π–π interaction

Small molecule-based organic semiconductors are of broad interest in organic field-effect transistors (OFETs) due to their potential for high crystallinity and electrical performance. The 2D molecule, TIPS-peri-pentacenopentacene (TIPS-PPP), which is the vertical extension of the 1D TIPS-pentacene (TIPS-PEN) molecule, offers a lower bandgap, higher aromaticity, and an enhanced π–π interaction with neighboring molecules in the solid state when compared to TIPS-PEN. However, an in-depth understanding of the relationship between the molecule structure, solid-state molecular packing, and the electronic properties has not been reported due to poor control over the TIPS-PPP crystallite size. In this work, we successfully engineered highly oriented large-area TIPS-PPP crystals through the solution shear coating technique. Additionally, compared with narrow ribbon-like TIPS-PEN crystals, TIPS-PPP crystals can grow centimeters long and over 500 μm wide. TIPS-PPP molecules are less susceptible to forming metastable polymorphs than TIPS-PEN molecules upon fast evaporation. The crystal structure of TIPS-PPP is also thermally stable at 250 °C. Notably, the anisotropic charge carrier mobility of TIPS-PPP crystals is resolved through fabricating bottom-gate top-contact devices, with a hole mobility of 3.1 cm 2 V -1 s -1 along the preferred packing direction. Further device optimization using top-gate bottom-contact devices improved the mobility up to 6.5 cm 2 V -1 s -1 , which is among the highest for pentacene-derivative-based organic semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Alfvén mode spectrum in the advanced steady-state tokamak scenario with negative triangularity

In the earlier studies [Zheng et al. Fundam. Plasma Phys. 10, 100051 (2024)], the negative triangularity tokamak was shown to have a unique and outstanding feature for the steady-state confinement. In addition to relieving the divertor heat load concern, it can reach a much higher beta limit in the nearly steady-state confinement, about twice the Troyon limit in the positive triangularity case. In this work, we extend the investigation to the Alfvén spectrum and toroidal Alfvén eigenmodes (TAEs). This helps to evaluate the property of energetic particle confinement in this advanced scenario of a negative triangularity tokamak. We found that, because of the large safety factor and reversed magnetic shear in the core plasma region near the magnetic axis, the TAE gap tends to be closed or have only a short radial extension between the continua. in the on-axis core region and open primarily in the off-axis region, where TAEs are shown to be present. This indicates that energetic particle confinement in the core region near the magnetic axis, where nuclear fusion reactions prevail, is not seriously affected by the excitation of TAEs in this scenario. The configuration with off-axis TAEs helps the confinement of energetic particles as compared to the configuration with near-axis TAEs. The results show that the advanced steady-state tokamak scenario with negative triangularity, because of the rapid variation of the safety factor, also has a favorable feature for energetic particle confinement.

Fourier analysis↗
DOE OSTI2020

Plastic deformation of single crystals of the equiatomic Cr-Mn-Fe-Co-Ni high-entropy alloy in tension and compression from 10 K to 1273 K

In this report the plastic deformation behavior of single crystals of the quinary, equiatomic Cr-Mn-Fe-Co-Ni high-entropy alloy (HEA) with the face-centered cubic structure has been investigated in tension and compression as a function of crystal orientation and temperature from 10 K to 1373 K. The critical resolved shear stress (CRSS) for {111}<110> slip at room temperature is 42-45 MPa. It does not depend much on crystal orientation (i.e., the Schmid law holds true) and the sense (tension vs. compression) of the applied load. The CRSS for {111}<110> slip increases with the decrease in temperature, without showing any significant inertia effects at cryogenic temperatures below 77 K. Extrapolation from the measured yield stresses down to 10 K yields a CRSS value at 0 K of 168 MPa. At cryogenic temperatures, the measured strain-rate sensitivity of flow stress is consistent with a very small activation volume. The concept of stress equivalence holds true both for the temperature dependence of CRSS and the stress dependence of activation volume, indicating that solid-solution hardening is the major strengthening mechanism. Deformation twinning occurs at 77 K but not at room temperature, resulting in higher tensile elongation to failure at 77 K than at room temperature. Deformation twinning at 77 K occurs at a shear stress of 378 MPa on conjugate ($\bar1\bar11$) planes in the form of Lüders deformation after large plastic strain (about 85%) achieved by the stage I (easy glide) and stage II (linear work-hardening) deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Studying QGP with flow: A theory overview

I review recent developments in the phenomenological study of the quark-gluon plasma (QGP) transport properties based on a personal selection of results that were presented at Quark Matter 2019. Additionally, the constraints on the temperature dependence of QGP shear and bulk viscosity are summarized. I discuss new theory advancements towards more realistic 3D dynamical simulations of heavy-ion collisions at finite baryon density. The challenges and opportunities of applying hydrodynamics to small collision systems are highlighted.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Shear-Induced Conformations of Salt-Free Polyelectrolytes in Semidilute Solutions

Here, we present coarse-grained molecular dynamics simulations of salt-free polyelectrolyte chains in semidilute solutions under simple shear flow, with full hydrodynamic interactions and explicit dipolar solvent. At equilibrium, chain orientation statistics follow a pseudo-Voigt distribution, and the structural correlation length and chain end-to-end vector autocorrelation function exhibit scaling behavior consistent with theoretical predictions for polyelectrolytes. Under shear, chains transition from coiled to stretched states and the end-to-end vector autocorrelation function reveals oscillatory dynamics at high Weissenberg numbers. Analysis of the gyration tensor and shear strain distributions identified three distinct chain populations with directional alignment along and against the shear gradient. Compared with their neutral polymer counterparts, polyelectrolytes exhibit stronger shear thinning and enhanced chain alignment under the same Weissenberg number, which is attributed to electrostatic interactions and shear-induced counterion release. These findings provide molecular insight into the distinct flow response of charged polymers and have implications for tailoring the rheological properties of polyelectrolyte-based materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Investigation of the Mechanical Degradation of Zinc-Based Cold Spray Coatings for Steel Pipelines

Internal corrosion in wet natural gas is a big challenge in the oil and gas industry due to corrosive constituents such as carbon dioxide (CO2), hydrogen sulfide (H2S), other forms of sulfur, and water in the gas stream. To mitigate internal corrosion, zinc-based cold spray coatings were designed for use in natural gas pipelines to increase the lifespan of the pipeline network. However, one of the requirements in designing internal coatings is the resistance of the coatings to mechanical forces applied on the pipeline's internal wall during pigging operations. These forces are primarily compressive and shear/friction forces. This study examines material properties that must be considered when evaluating mechanical considerations. To determine the viability of the developed coatings with respect to the shear/friction forces, the shear adhesion and wear resistance of the materials have been evaluated. Shear adhesion testing was performed with modified clevises to determine the shear stress required to cause adhesion failure at the coating/substrate interface. Scanning electron microscopy was utilized to characterize the interfaces after failure. Wear testing was performed on coatings and pipeline materials utilizing pin-on-drum testing. These tests display the stress limitations and wear that the coatings can tolerate from pigging.

mechanical degradation↗
DOE OSTI2022

Halo Properties and Mass Functions of Groups/Clusters from the DESI Legacy Imaging Surveys DR9

Abstract Based on a large group/cluster catalog recently constructed from the DESI Legacy Imaging Surveys DR9 using an extended halo-based group finder, we measure and model the group–galaxy weak-lensing signals for groups/clusters in a few redshift bins within redshift range 0.1 ≤ z < 0.6. Here, the background shear signals are obtained based on the DECaLS survey shape catalog, derived with the F ourier _Q uad method. We divide the lens samples into five equispaced redshift bins and seven mass bins, which allow us to probe the redshift and mass dependence of the lensing signals, and hence the resulting halo properties. In addition to these sample selections, we also check the signals around different group centers, e.g., the brightest central galaxy, the luminosity-weighted center, and the number-weighted center. We use a lensing model that includes off-centering to describe the lensing signals that we measure for all mass and redshift bins. The results demonstrate that our model predictions for the halo masses, biases, and concentrations are stable and self-consistent among different samples for different group centers. Taking advantage of the very large and complete sample of groups/clusters, as well as the reliable estimations of their halo masses, we provide measurements of the cumulative halo mass functions up to redshift z = 0.6, with a mass precision at 0.03 ∼ 0.09 dex.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Bulk nanocrystalline Al–Mg–Y alloys with amorphous grain boundary complexions display high strength and compressive plasticity

Although nanocrystalline alloys regularly exhibit high strengths, their use in structural applications often face challenges due to sample size limitations, unstable microstructures, and the limited ability to plastically deform. The incorporation of amorphous grain boundary complexions has been proposed to address these issues, by simultaneously stabilizing nanocrystalline grain structures for scale-up processing and improving alloy toughness. In the present study, the mechanical behavior of bulk nanocrystalline Al–Mg–Y is examined with macroscale compression testing, probing a length scale that is relevant to real-world structural applications. Bulk samples were fabricated via a simple powder metallurgy approach, with different hot-pressing temperatures and durations employed for consolidation in order to investigate microstructural and property evolution. All of the specimens contained primary face-centered cubic Al and secondary Al 4 C 3 and Al 3 Y phases, with the Al 3 Y particles exhibiting two populations of small equiaxed and larger elongated particles. Appreciable plasticity was measured along with high ultimate stresses over 800 MPa due to the presence of amorphous grain boundary complexions. Microstructural characterization of fracture surfaces revealed that the area fraction of dimpled regions increased with longer hot-pressing time. Most importantly, the elongated Al 3 Y particles formed regular cellular patterns with increasing hot-pressing time, delaying shear localization and significantly enhancing plasticity. The hierarchy present in the microstructure of the Al–Mg–Y alloy, from amorphous grain boundary complexions to secondary phases, gives rise to excellent bulk mechanical properties, which are attractive for structural applications.

Bulk nanocrystalline alloy↗
DOE OSTI2022

The High-Frequency Signature of Slow and Fast Laboratory Earthquakes

Tectonic faults fail through a spectrum of slip modes, ranging from slow aseismic creep to rapid slip during earthquakes. Understanding the seismic radiation emitted during these slip modes is key for advancing earthquake science and earthquake hazard assessment. In this work, we use laboratory friction experiments instrumented with ultrasonic sensors to document the seismic radiation properties of slow and fast laboratory earthquakes. Stick-slip experiments were conducted at a constant loading rate of 8 μm/s and the normal stress was systematically increased from 7 to 15 MPa. We produced a full spectrum of slip modes by modulating the loading stiffness in tandem with the fault zone normal stress. Acoustic emission data were recorded continuously at 5 MHz. We demonstrate that the full continuum of slip modes radiate measurable high-frequency energy between 100 and 500 kHz, including the slowest events that have peak fault slip rates <100 μm/s. The peak amplitude of the high-frequency time-domain signals scales systematically with fault slip velocity. Stable sliding experiments further support the connection between fault slip rate and high-frequency radiation. Experiments demonstrate that the origin of the high-frequency energy is fundamentally linked to changes in fault slip rate, shear strain, and breaking of contact junctions within the fault gouge. Our results suggest that having measurements close to the fault zone may be key for documenting seismic radiation properties and fully understanding the connection between different slip modes.

58 GEOSCIENCES↗
DOE OSTI2020

Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks

Materials representation plays a key role in machine learning-based prediction of materials properties and new materials discovery. Currently both graph and three-dimensional (3D) voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD-based 3D convolutional neural networks (CNNs) for predicting the elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2170 $Fm\bar3m$ face-centered-cubic materials show that our ECD-based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Materials-Agnostic Platform for Informatics and Exploration features and ECD descriptors achieved the best fivefold cross-validation performance. More importantly, we showed that our ECD-based CNN models can achieve significantly better extrapolation performance when evaluated over nonredundant data sets, where there are few neighbor-training samples around test samples. As an additional validation, we evaluated the predictive performance of our models on 329 materials of space group $Fm\bar3m$ by comparing to density functional theory calculated values, which shows a better prediction power of our model for bulk modulus than shear modulus. Because of the unified representation power of ECD, it is expected that our ECD-based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Understanding Twinning and Deformation in High Entropy Alloys

On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in alloy design due to large compositional space, whereas on the other, they have presented enormous computational challenges for theory-based materials design, especially density functional theory (DFT), which is inherently computationally expensive even for traditional dilute alloys. In this project, we developed a machine learning framework, namely PREDICT ( PR edict properties from E xisting D atabase I n C omplex alloys T erritory), that opens a pathway to predict elastic constants in large compositional space with little computational expense. The framework only relies on the DFT database of binary alloys and predicts Voigt–Reuss–Hill Young’s modulus, shear modulus, bulk modulus, elastic constants, and Poisson’s ratio in MPEAs. We show that the key descriptors of elastic constants are the A–B bond length and cohesive energy. The framework can predict elastic constants in hypothetical compositions as long as the constituent elements are present in the database, thereby enabling property exploration in multi-compositional systems. We illustrate predictions in a FCC Ni-Cu-Au-Pd-Pt system.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Rheological and Structural Properties of k‐Carrageenan/Xanthan Gum Gummies Architected With a New Natural Additive

ABSTRACT Biohybrids (BHs)based on bentonite and anthocyanins can be produced as alternatives to synthetic colorants. However, no information is available on the interaction of biohybrid with food system. This research studies the rheological properties of gummies containing k‐carrageenan and xanthan gum incorporated with a BH. The incorporation of BH (≤ 2% w/w) increased the flow consistency index and shear thinning effect in melted gummies at 85°C. During cooling from 90°C to 25°C, the presence of BH altered the sol–gel transition temperature typical in carrageenan systems, probably by the formation of carrageenan complexes. At 25°C, all gummies had a solid‐like response with elastic modulus ( G ') higher than viscous modulus ( G "). G ' values increased linearly with BH concentration, and the linear viscoelastic region and breaking strain decreased with BH concentration, indicating that this natural additive acted as a reinforcing material.

Valencia, German Ayala [Department of Chemical and↗
DOE OSTI2021

Enhanced Visibility of Subduction Slabs by the Formation of Dense Hydrous Phase A

Abstract Phase A (Mg 7 Si 2 O 8 (OH) 6 ) is one of the important dense hydrous magnesium silicates in subducting slab, because it forms after the breakdown of antigorite serpentine and could be the dominant hydrous phase in the upper‐mantle deep slab for the water transportation into deep Earth. In this study, the compressional ( P ) and shear ( S ) wave velocities of phase A were measured at simultaneous pressure and temperature conditions up to 11 GPa and 1073 K. Combined with elastic properties of olivine and pyroxenes, we calculate the hydration effect on the velocities of harzburgite lithology in cold subduction zones throughout the depth ranges where phase A is thermodynamically stable. Our calculations suggest that the hydration increases both P ‐ and S ‐wave velocities of harzburgite; at ∼5 wt% hydration, its seismic detectability is enhanced by 1%–1.5% in velocity contrasts relative to its anhydrous counterpart.

Cai, Nao↗
DOE OSTI2021

Revealing extreme twin-boundary shear deformability in metallic nanocrystals

Metals containing abundant coherent twin boundaries (TBs) are able to sustain substantial plastic deformation without fracture due to shear-induced TB migration and sliding. Retaining ductility in these metals, however, has proven difficult because detwinning rapidly exhausts TB migration mechanisms at large deformation, whereas TB sliding was only evidenced for loading on very specific crystallographic orientations. Here, we reveal the intrinsic shear deformability of twins in nanocrystals using in situ nanomechanical testing and multiscale simulations and report extreme shear deformability through TB sliding up to 364%. Sliding-induced plasticity is manifested for orientations that are generally predicted to favor detwinning and shown to depend critically on geometric inhomogeneities. Normal and shear coupling are further examined to delineate a TB orientation-dependent transition from TB sliding to TB cracking. These dynamic observations reveal unprecedented mechanical properties in nanocrystals, which hold implications for improving metal processing by severe plastic deformation.

36 MATERIALS SCIENCE↗
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↗