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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Electromagnetic levitation containerless processing of metallic materials in microgravity: rapid solidification

Space levitation processing allows researchers to conduct benchmark tests in an effort to understand the physical phenomena involved in rapid solidification processing, including alloy thermodynamics, nucleation and growth, heat and mass transfer, solid/liquid interface dynamics, macro- and microstructural evolution, and defect formation. Supported by ground-based investigations, a major thrust is to develop and refine robust computational tools based on theoretical and applied approaches. This work is accomplished in conjunction with experiments designed for precise model validation with application to a broad range of industrial processes.

42 ENGINEERING↗
DOE OSTI2024

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics–Informed Neural Networks

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. Furthermore, the improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Probing multiscale dissolution dynamics in natural rocks through microfluidics and compositional analysis

Mineral dissolution significantly impacts many geological systems. Carbon released by diagenesis, carbon sequestration, and acid injection are examples where geochemical reactions, fluid flow, and solute transport are strongly coupled. The complexity in these systems involves interplay between various mechanisms that operate at timescales ranging from microseconds to years. Current experimental techniques characterize dissolution processes using static images that are acquired with long measurement times and/or low spatial resolution. These limitations prevent direct observation of how dissolution reactions progress within an intact rock with spatially heterogeneous mineralogy and morphology. We utilize microfluidic cells embedded with thin rock samples to visualize dissolution with significant temporal resolution (100 ms) in a large observation window (3 × 3 mm). Here we injected acidic fluid into eight shale samples ranging from 8 to 86 wt % carbonate. The pre- and postreaction microstructures are characterized at the scale of pores (0.1 to 1 µm) and fractures (1 to 1,000 µm). We observe that nonreactive particle exposure, fracture morphology, and loss of rock strength are strongly dependent on both the relative volume of reactive grains and their distribution. Time-resolved images of the rock unveil the spatiotemporal dynamics of dissolution, including two-phase flow effects in real time and illustrate the changes in the fracture interface across the range of compositions. Moreover, the dynamical data provide an approach for characterizing reactivity parameters of natural heterogeneous samples when porous media effects are not negligible. The platform and workflow provide real-time characterization of geochemical reactions and inform various subsurface engineering processes.

58 GEOSCIENCES↗
DOE OSTI2022

A wide-field micro-computed tomography detector: micron resolution at half-centimetre scale

Ideal three-dimensional imaging of complex samples made up of micron-scale structures extending over mm to cm, such as biological tissues, requires both wide field of view and high resolution. For existing optics and detectors used for micro-CT (computed tomography) imaging, sub-micron pixel resolution can only be achieved for fields of view of <2 mm. This article presents a unique detector system with a 6 mm field-of-view image circle and 0.5 µm pixel size that can be used in micro-CT units utilizing both synchrotron and commercial X-ray sources. A resolution-test pattern with linear microstructures and whole adult Daphnia magna were imaged at beamline 8.3.2 of the Berkeley Advanced Light Source. Volumes of 10000 × 10000 × 7096 isotropic 0.5 µm voxels were reconstructed over a 5.0 mm × 3.5 mm field of view. Measurements in the projection domain confirmed a 0.90 µm measured spatial resolution that is largely Nyquist-limited. This unprecedented combination of field of view and resolution dramatically reduces the need for sectional scans and computational stitching for large samples, ultimately offering the means to elucidate changes in tissue and cellular morphology in the context of larger, whole, intact model organisms and specimens. This system is also anticipated to benefit micro-CT imaging in materials science, microelectronics, agricultural science and biomedical engineering.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Extending Interfaces in 3D to Achieve Superior Nanoscale Strength in Ti/Nb Nanolaminates

Tuning the atomic-level structure of nanolaminates enables high strength, increased deformability, and the ability to absorb and mitigate damage due to varied crystalline defects, including dislocations. Here, we present the enhanced strength of Ti/Nb nanolaminates containing thick 3D interfaces (3DIs), relative to their chemically abrupt 2D counterparts. We examine the effects of crystallographic alignment and compositional gradients on mechanical behavior via experiments and phase-field-dislocation dynamics (PFDD) modeling. Mechanical testing reveals that nanolaminates containing thicker 3DIs demonstrate a 28% hardness enhancement compared with sharp-interface nanolaminates. PFDD modeling shows that the critical resolved shear stress (CRSS) increases with the 3DI thickness. Gradual compositional transitions in 3DIs were confirmed via scanning transmission electron microscopy and high-resolution transmission electron microscopy, showing sharp crystallographic transitions and a heightened interface topography. The findings establish a positive function between the 3DI thickness and mechanical robustness for hexagonal-closest-packed-containing composites, emphasizing the role of defect–interface interactions in tailoring the mechanical performance and providing a foundation for future interfacial engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reactive boride infusion stabilizes Ni-rich cathodes for lithium-ion batteries

Engineered polycrystalline electrodes are critical to the cycling stability and safety of lithium-ion batteries, yet it is challenging to construct high-quality coatings at both the primary- and secondary-particle levels. Here, we present a room-temperature synthesis route to achieve full surface coverage of secondary particles and facile infusion into grain boundaries, thus offering a complete “coating-plus-infusion” strategy. Cobalt boride metallic glass is successfully applied to Ni-rich layered cathode LiNi 0.8 Co 0.1 Mn 0.1 O 2 . Here, it dramatically improves the rate capability and cycling stability, including under high-discharge-rate and elevated-temperature conditions and in pouch full cells. The superior performance originates from simultaneous suppression of microstructural degradation of intergranular cracking and side reactions with electrolyte. Atomistic simulations identified the critical role of strong selective interfacial bonding, which offers not only a large chemical driving force to ensure uniform reactive wetting and facile infusion but also lowered the surface/interface oxygen activity, contributing to the exceptional mechanical and electrochemical stabilities of the infused electrode.

25 ENERGY STORAGE↗
DOE OSTI2022

Volumetric energy density impact on mechanical properties of additively manufactured 718 Ni alloy

The 718 Ni-based superalloy has gained enormous attention in the additive manufacturing community for its great weldability, allowing for complex geometries to be formed, and its superb mechanical strength. Here we explore the influence of a wide range of major build parameters on microstructures and mechanical properties of 718 Ni alloys. Volumetric energy density appears to be one of the major parameters that captures the relationship between energy input and measured mechanical properties and microstructures of the alloys. A threshold energy density was identified below which the properties of the additively manufactured specimen degrade substantially. Finally, the influence of energy density on the quality of the built specimen simulated using Flow3D AM correlates well with the experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microscale Imaging of Thermal Conductivity Suppression at Grain Boundaries

Abstract Grain‐boundary engineering is an effective strategy to tune the thermal conductivity of materials, leading to improved performance in thermoelectric, thermal‐barrier coatings, and thermal management applications. Despite the central importance to thermal transport, a clear understanding of how grain boundaries modulate the microscale heat flow is missing, owing to the scarcity of local investigations. Here, thermal imaging of individual grain boundaries is demonstrated in thermoelectric SnTe via spatially resolved frequency‐domain thermoreflectance. Measurements with microscale resolution reveal local suppressions in thermal conductivity at grain boundaries. Also, the grain‐boundary thermal resistance – extracted by employing a Gibbs excess approach – is found to be correlated with the grain‐boundary misorientation angle. Extracting thermal properties, including thermal boundary resistances, from microscale imaging can provide comprehensive understanding of how microstructure affects heat transport, crucially impacting the materials design of high‐performance thermal‐management and energy‐conversion devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

In situ melt pool measurements for laser powder bed fusion using multi sensing and correlation analysis

Laser powder bed fusion is a promising technology for local deposition and microstructure control, but it suffers from defects such as delamination and porosity due to the lack of understanding of melt pool dynamics. To study the fundamental behavior of the melt pool, both geometric and thermal sensing with high spatial and temporal resolutions are necessary. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the evolution of the melt pool shape, keyhole, vapor plume, and thermal evolution in Ti–6Al–4V and 410 stainless steel spot melt cases. Aside from presenting the sensing capability, this paper develops an effective algorithm for high-speed X-ray imaging data to identify melt pool geometries accurately. Preprocessing methods are also implemented for the IR data to estimate the emissivity value and extrapolate the saturated pixels. Quantifications on boundary velocities, melt pool dimensions, thermal gradients, and cooling rates are performed, enabling future comprehensive melt pool dynamics and microstructure analysis. The study discovers a strong correlation between the thermal and X-ray data, demonstrating the feasibility of using relatively cheap IR cameras to predict features that currently can only be captured using costly synchrotron X-ray imaging. Such correlation can be used for future thermal-based melt pool control and model validation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Effect of void positioning on the detonation sensitivity of a heterogeneous energetic material

We show although it is well-established that voids profoundly influence the initiation and reaction behaviors of heterogeneous energetic materials such as polymer-bonded explosives (PBX) and propellants, there has been little study of how void location in different constituents in the microstructures of such materials affect the macroscale behavior. Here, we use three-dimensional (3D) mesoscale simulations to study how void placement within the reactive grains versus the polymer binder influences the shock-to-detonation transition (SDT) in a polymer-bonded explosive. The material studied here has a microstructure comprised of 75% PETN (pentaerythritol tetranitrate) grains and 25% HTPB (hydroxyl-terminated polybutadiene) polymer binder by volume. Porosities up to 10% in the form of spherical voids distributed in both the grains and polymer are considered. An Arrhenius reactive burn relation is used to model the chemical kinetics of the PETN grains under shock loading, thereby resolving the heterogeneous detonation behavior of the PBX. The influence of void location on the shock initiation sensitivity of the material is quantitatively ranked by comparing the predicted run distance to detonation (RDD) for each sample. The analysis includes inherent quantification of uncertainties arising from the stochastic variations in the microstructure morphologies and void distributions by using statistically equivalent microstructure sample sets (SEMSS), leading to probabilistic formulations for the RDD as a function of shock pressure. The calculations reveal that the location of voids in the composite microstructure significantly affects the RDD. Specifically, voids exclusively within the grains cause the PBX to be more sensitive (having shorter RDD) than voids in the polymer binder. Unique probabilistic relationships are derived to map the probability of observing RDD for each void location material case, allowing for prediction of initiation behavior anywhere in the shock pressure – RDD space. These findings agree with trends reported in the literature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Microstructure-Sensitive modeling of surface roughness and notch effects on extreme value fatigue response

Fatigue life knockdown due to surface roughness induces local stress concentrations. Physical fatigue experiments are crucial to qualify materials for use but are limited in their capability to examine the mechanisms underlying the fatigue crack formation driving force due to these stress concentrations. Microstructure-sensitive computational models can aid in this regard and are employed in this work to examine the effects of surface roughness in Al 7075-T6. Digital microstructure models are subjected to crystal plasticity finite element method (CPFEM) simulations to examine the combined effects of microstructure and grain scale asperities on the driving force for fatigue crack formation. Following elastic–plastic shakedown, mesoscale volume-averaged fatigue indicator parameters (FIPs) are computed within fatigue damage process zones of grains. An increase in the intensity of realistic surface roughness profiles corresponds accordingly to larger FIPs but it is difficult to precisely interpret the mechanisms and effects of this intensification. We thus parametrically investigate surface asperities that couple with microstructure and lack of constraint on slip at the free surface to describe microstructure-sensitive surface roughness knockdown effects on fatigue resistance. The depth of a single valley (i.e., notch) due to surface roughness is found to be more detrimental to fatigue resistance than the radius (i.e., notch width, not to be confused with notch acuity). Finally, we also find that different crystallographic textures, grain morphologies, and number of sampled grains have a limited effect on the depth of influence of these notches.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Shock recompaction of spall damage

Spall fracture is a high strain-rate damage phenomenon associated with shock or impulsive loading events. When a material that has been subjected to shock compression is allowed to release, rarefaction waves propagate into the sample and reduce the internal stress to zero. If multiple rarefaction waves intersect, they generate tension which, if sufficient, can nucleate voids in the material. It has been observed in several works investigating spall fracture that although the shock-wave profiles suggested spall occurred, imaging of the recovered sample revealed no voids or cracks. In this study, we aim to determine whether a second shock event could recompact existing spall damage, and if so, what form does the microstructure at the recompaction interface have? Through a series of gas-gun flyer-plate impact experiments, we demonstrate that modest shock stresses of 2 GPa–3 GPa are enough to both fully compact a damaged copper target back to a state of zero porosity and, furthermore, drive recrystallization of the interface such that there is a new bond formed where the free surfaces were brought together.

42 ENGINEERING↗
DOE OSTI2024

Benchmarking machine learning strategies for phase-field problems

Abstract We present a comprehensive benchmarking framework for evaluating machine-learning approaches applied to phase-field problems. This framework focuses on four key analysis areas crucial for assessing the performance of such approaches in a systematic and structured way. Firstly, interpolation tasks are examined to identify trends in prediction accuracy and accumulation of error over simulation time. Secondly, extrapolation tasks are also evaluated according to the same metrics. Thirdly, the relationship between model performance and data requirements is investigated to understand the impact on predictions and robustness of these approaches. Finally, systematic errors are analyzed to identify specific events or inadvertent rare events triggering high errors. Quantitative metrics evaluating the local and global description of the microstructure evolution, along with other scalar metrics representative of phase-field problems, are used across these four analysis areas. This benchmarking framework provides a path to evaluate the effectiveness and limitations of machine-learning strategies applied to phase-field problems, ultimately facilitating their practical application.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Direct-write laser-assisted patterning of form birefringence in wave plates fabricated by glancing-angle deposition

The development of optics that provide spatial control of birefringence could enable better control of laser beam polarization, but available solutions are limited. Here we demonstrate a method to locally modify the birefringence of wave plates fabricated by glancing-angle deposition. The method employs localized melting of the anisotropic microstructure in a vacuum environment to alter the local birefringence. We demonstrate that this process is only possible under high vacuum to avoid trapping air within the melt zone. The direct-write method presented here can be readily utilized for coatings exhibiting form birefringence of virtually any chemical composition, size, and format.

42 ENGINEERING↗
DOE OSTI2025

Machine learning approaches for intentional materials engineering

In this article, the development of nanoporous metals and metallic composites through dealloying processes presents significant opportunities in materials engineering. However, designing multicomponent precursor alloys and establishing corresponding processing methods that yield predictable compositions and nanostructures remain a complex challenge. This article explores how machine learning (ML)-augmented computational and experimental methodologies can tackle these challenges by predicting precursor alloy compositions, final nanoporous structures, and mechanical properties, while integrating ML-enabled autonomous experimentation for material design and quantification. We highlight recent advancements in applying ML to nanostructured materials design via dealloying and discuss how techniques from other nanomaterial designs can be adapted for improved control over morphological and compositional outcomes in nanoporous and nanocomposite materials. Furthermore, we explore the role of ML in autonomous synchrotron x-ray experimentation, enabling real-time feedback between modeling and experimental setups. ML-driven approaches to microstructure characterization and mechanical property prediction are also examined, with a focus on modeling and advanced imaging techniques such as three-dimensional nanotomography. Finally, this article outlines future directions for ML-enhanced materials science, emphasizing the exploration of high-dimensional parameter spaces and the incorporation of materials kinetics into processing and property evaluation, ultimately advancing the design of nanoporous structures and materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Impact of Hydrogen Combustion on the Oxidation-induced Degradation of Heavy-duty Diesel Engine Piston Materials

High temperature ferritic-martensitic steels are candidate materials for heavy-duty diesel engine pistons. The envisioned transition to hydrogen blended fuels is expected to alter the post-combustion atmosphere in the engines, primarily resulting in a higher water vapor content (> 20 vol%) and potentially higher exhaust gas temperatures. The oxidation resistance of existing and newly developed alloys will be a critical life-limiting mechanism under these conditions. In the present work, the oxidation behavior of candidate piston alloys was evaluated in air+10 vol.% H2O and air+30 vol.% H2O at 700°C. Thermal cyclic (1h cycle) exposures were conducted for two variants of commercial UNSS42200 ferritic-martensitic steel and two developmental alloy steels for up to 300h. The developmental alloys each have similar compositions but with one containing elevated Cu levels of 3 wt.%, A significant reduction in resistance to breakaway oxidation was observed for the commercial alloys in the higher water vapor atmosphere. Microstructural characterization (optical metallography, scanning electron microscopy and electron microprobe analysis) revealed the formation of thick Fe-rich oxides even for the high Cr (~12 wt.%) steels after an initial stage of protective oxidation with MnCr-rich spinels. The impact of the evaporation-induced loss of Cr on the time and temperature dependent compositional changes in the alloys was correlated with experimental findings. For the developmental alloys, Cu additions appear to play a role in significantly reducing oxidation kinetics in air+30 vol.% H2O at 700°C.

Pillai, Rishi [ORNL] (ORCID:0000000243688197)↗
DOE OSTI2023

In-Situ X-Ray Imaging High Strain Rate Compression of Laminate Al-Graphene Composite and Mechanical Property Characterization

Owing to the opaque nature of the laminated structures, traditional high-speed optical camera cannot be used to detect the dynamic process of sub-surface deformation. In this article, we report a study of using high speed X-ray imaging to study the high strain rate deformation in laminated Al structures. We used a Kolsky bar apparatus to apply dynamic compression and a high-speed synchrotron X-ray phase contrast imaging (PCI) setup to conduct the in situ X-ray imaging study. The in situ X-ray imaging captures the shock wave propagation in the laminated structures. After shock compression, we characterized the microstructures by using transmission electron microscopy (TEM), which demonstrates an increase of dislocation density. In conclusion, the micro-pillar compression tests show that the yield strength at 0.2% offset of laminated Al-graphene composite has a significant increase of 67%, from 30 to 50 MPa, compared to laminate Al after shock loading.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Excellent ballistic impact resistance of Al 0.3 CoCrFeNi multi-principal element alloy with unique bimodal microstructure

Multi-principal element alloys represent a new paradigm in structural alloy design with superior mechanical properties and promising ballistic performance. Here, the mechanical response of Al 0.3 CoCrFeNi alloy, with unique bimodal microstructure, was evaluated at quasistatic, dynamic, and ballistic strain rates. The microstructure after quasistatic deformation was dominated by highly deformed grains. High density of deformation bands was observed at dynamic strain rates but there was no indication of adiabatic shear bands, cracks, or twinning. The ballistic response was evaluated by impacting a 12 mm thick plate with 6.35 mm WC projectiles at velocities ranging from 1066 to 1465 m/s. The deformed microstructure after ballistic impact was dominated by adiabatic shear bands, shear band induced cracks, microbands, and dynamic recrystallization. The superior ballistic response of this alloy compared with similar Al x CoCrFeNi alloys was attributed to its bimodal microstructure, nano-scale L1 2 precipitation, and grain boundary B2 precipitates. Deformation mechanisms at quasistatic and dynamic strain rates were primarily characterized by extensive dislocation slip and low density of stacking faults. Deformation mechanisms at ballistic strain rates were characterized by grain rotation, disordering of the L1 2 phase, and high density of stacking faults.

36 MATERIALS SCIENCE↗