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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Design and Characterization of Hierarchically-Strengthened, Cast Al-Ce-Ni-Mn-Sc-Zr Alloys for High-Temperature Applications

We characterize the microstructure and mechanical properties of cast Al-Ce-Ni-Mn-Sc-Zr alloys designed for structural use above 300 °C. We first report on the effect of Ni on the as-cast phase formation, where all alloys consist of fine Al11Ce3 and Ni-rich eutectic phases, but the identity of the Ni-rich phase varies with Ni content. Furthermore, these alloys contain four coarsening-resistant strengthening phases as determined from SEM and APT investigations: (i) Al11Ce3 and (ii) Ni-rich micron-scale platelets formed during eutectic solidification, (iii) L12-Al3(Sc,Zr) nanoprecipitates formed during secondary aging, and (iv) Mn solute in the α-Al matrix. The Al-Ce-Ni-Mn-Sc-Zr alloy possesses higher microhardness and creep resistance than compositionally simpler alloys containing fewer of the strengthening phases, indicating that these strengthening mechanisms synergize to provide increased ambient- and elevated-temperature strength.

Ekaputra, Clement↗
DOE OSTI2026

A modified scheil approach for nucleation-dependent solidification pathways

As-solidified microstructures of near-eutectic alloys often contain multiple primary phases that are not expected from equilibrium phase diagrams. Such microstructures are caused by cooling-rate-dependent solidification pathways, a factor not captured by the Scheil–Gulliver model or variations thereof. Here, we present a model and algorithm that incorporate the critical nucleation undercooling for each solid phase into the Scheil–Gulliver model. We hypothesize that the non-equilibrium microstructure formation is primarily governed by a nucleation-competition mechanism. This mechanism accounts for both stable/metastable phase selection and primary-phase formation within eutectic regions driven by asymmetric nucleation barriers. The model is validated against a hypereutectic Al-Fe alloy, where it successfully reproduces the observed microstructural constituents, revealing the key dependencies of solidification microstructure on nucleation kinetics. Applicability to multicomponent systems is demonstrated through a hypereutectic Al–Fe–Si ternary alloy, where the model successfully predicts divorced eutectic microstructures and the associated oscillatory solidification pathways along univariant lines. As a result, the proposed framework establishes a nucleation-dependent computational approach for interpreting and predicting solidification microstructures.

Alloy design↗
DOE OSTI2019

Elevated temperature microstructure evolution of a medium-entropy CrCoNi superalloy containing Al,Ti

A new medium-entropy superalloy was produced based on the compositions of equiatomic CrCoNi and Ni-base superalloy Inconel 740H. Initial alloy design was performed using Thermo-Calc. The aging response and microstructural stability were assessed following heat treatment at temperatures between 600 and 900 °C and durations up to 100 h. Aging from a fully recrystallized state resulted in negligible grain growth and produced γ’ and σ phases. The same phases were present after aging from a cold-rolled state, but partially recrystallized microstructures resulted in multi-modal size distributions and heterogeneous spatial arrangements. Finally, room temperature hardness measurements were used to correlate aging conditions with quantitative precipitate measurements and mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Efficient Reversible Operation and Stability of Novel Solid Oxide Cells

This project aims to develop and test novel Reversible Solid Oxide Cells (ReSOCs) specifically designed to yield low area specific resistance, as required to achieve high round trip efficiency in reversible operation at high current density. A key objective is to improve long-term reversible SOC durability; mechanistic degradation models will be used to predict long-term durability using input data from accelerated testing that combines electrochemical life testing with quantitative microstructural and chemical evaluation. The modeling tasks seek to design low-loss, low-cost ReSOC systems using input data from the experimental cell studies. A key challenge is to develop a thermal management strategy that maintains thermally self-sustaining operation while operating the stack at a potential lower than the thermo-neutral steam electrolysis voltage of ~1.3 V, to maintain high efficiency.

08 HYDROGEN↗
DOE OSTI2021

Domain knowledge-informed, process-mapping AI graph for designing Fe-based alloys

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Continuous improvement in efficiency of a power plant relies on designing materials for use at increasingly higher temperature and/or pressure, for 100,000s hours of operation. Due to complexity, non-linearity and high-dimensionality of the problem, traditional Machine Learning (ML) approaches require unreasonably large datasets for the data-driven model development. Science-based material and process engineering complements hard data with, sometimes soft and intuitive, empirical domain knowledge. Artificial Intelligence (AI) was used in this study to incorporate such knowledge into computational graph architecture (process-mimicking artificial neuron design, causal layer and graph structures, ensemble modeling of latent states) and learning procedures (variable transformation, fuzzy physics pre-training and freezing of deep layers, virtual microstructure representation, and adversarial multi-objective optimization). The first alloys design pathways suggested by the AI tool (pyroMind) passed a preliminary engineering review on soundness and transparency.</span>

Romanov, Vyacheslav↗
DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evolution of Microstructure and Surface Characteristics of FeCrAl alloys when Subjected to Flow Boiling Testing

FeCrAl alloys are candidate materials for manufacturing accident-tolerant fuel (ATF) cladding intended for light water reactors to increase fuel reliability and safety during design-basis and beyond-design-basis accident scenarios. The evolution of the materials' surface characteristics, microstructure, and mechanical properties when exposed to the Critical Heat Flux (CHF) in flow boiling testing is crucial for safety analysis while providing insights into their thermal-hydraulic performance in nuclear reactors. After CHF, the surface chemistry of two FeCrAl alloys, APMT and C26M, was studied to understand their evolution at the early stage of high-temperature excursions in short time periods. The results indicated a thin layer composed of oxides and hydroxides of Al, Cr, and Fe with varying proportions at different depths in the layer, as indicated by X-ray photoelectron spectroscopy (XPS) and depth profiling. The cross-sections prepared by focused ion beam (FIB) revealed the growth of an oxide layer, in the range of 90-180 nm thick, on the alloys' surfaces. The evolution of the materials' surface chemistry also led to a noticeable post CHF excursion increase in their wettability, with a slight increase in roughness. Further, the investigation of the materials' mechanical properties indicated a modest increase in hardness by 10-15% as well as an increase in their yield strength, as evidenced by the microindentation and ring compression tests conducted before and after CHF testing. Scanning electron Microscopy (SEM) and X-ray diffraction (XRD) were used to investigate microstructural features of the materials and their changes after CHF treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Materials informatics: From the atomic-level to the continuum

We report that in recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. This relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, polymer dielectrics, etc. Its practitioners employ the methods of multivariate statistics and machine learning in conjunction with standard computational tools (e.g., density-functional theory) to, for example, visualize and dimensionally reduce large data sets, identify patterns in hyperspectral data, parse microstructural images of polycrystals, characterize vortex structures in ferroelectrics, design batteries and, in general, establish correlations to extract important physics and infer structure-property-processing relationships. In this Overview, we critically examine the role of informatics in several important materials subfields, highlighting significant contributions to date and identifying known shortcomings. We specifically focus attention on the difference between the correlative approach of classical data science and the causative approach of physical sciences. From this perspective, we also outline some potential opportunities and challenges for informatics in the materials realm in this era of big data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

An electronic-structure-centered perspective is presented on permanent-magnet (PM) design, highlighting two key levers, that is, saturation magnetization (M s ), governed by 3d-band filling and exchange physics, and magnetocrystalline anisotropy energy (MAE), arising from spin-orbit coupling (SOC) on anisotropic orbital populations. Reviewing current practices, including DFT-based MAE/J ij extraction, atomistic-spin and micromagnetic modeling, and high-throughput machine learning (ML) pipelines, three bottlenecks limiting predictive discovery is identified that is i) electronic-structure accuracy for small MAE (sensitive to functional choice, Hubbard U, and many-body effects), ii) finite-temperature and kinetic realism (phonon/magnon renormalization, ordering kinetics), and iii) descriptor and multiscale decoupling (lack of SOC-weighted and orbital-resolved fingerprints). Deep dives into the electronic-structure of Nd─Fe─B and Fe─N show how these fingerprints govern magnetic performance, motivating DFT- and quantum-mechanics-based descriptors for discovery. Unbiased, structure-driven exploration, coupled with high-throughput simulations, ML, generative AI, and reasoning models, accelerates candidate identification and propagates insights across scales. Addressing supply-chain risks, on future needs of designing “critical-element-free” magnets with tailored microstructure and high energy products is emphasized. By integrating electronic fingerprints, AI reasoning, and multiscale modeling, a practical roadmap is provided for rare-earth-lean or rare-earth free, high-performance, sustainable PMs.

Singh, Prashant [Ames Laboratory, and Iowa State U↗
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 OSTI2026

Oxygen Effects and Selective Oxidation of Group IV Alloying Additions in Group V-Based Refractory Multi-principal Element Alloys

Refractory multi-principal element alloys (RMPEAs) hold significant potential for advancing ultra-high-temperature technologies due to their remarkable strength retention under these conditions. However, the beneficial effects of alloying on microstructural evolution and mechanical properties are often obscured by the presence of interstitial alloying elements. The intricate interactions between substitutional alloying elements of Groups IV, V, and VI and interstitial content, whether intentional or incidental, remain poorly understood. In this study, we investigate the impact of dissolved oxygen on the microstructural evolution and stability of RMPEAs primarily composed of Group V elements, Nb and V, along with Group IV elements Ti, Zr, and Hf. We show that the strong affinity of oxygen for Group IV elements Zr and Hf leads to the internal precipitation of oxides during heat treatment, which competes with the homogenization of the as-cast dendritic microstructure. Here, we demonstrate that careful control and design of oxygen content and Group IV alloying additions are essential to minimize micro-segregation and prevent the detrimental precipitation of internal oxides. To address these challenges, we propose employing non-equilibrium solidification calculations to predict and optimize as-cast solidification structures, thereby reducing the reliance on homogenization processes.

Alloy design↗
DOE OSTI2023

3D Printed eutectic aluminum alloy has facility for site-specific properties

Additive manufacturing (AM) has the ability to print structures with site-specific properties. Existing AM approaches for site-specific properties are, however, based on complex processing-microstructure relationships in conventional alloys that were not designed for this purpose. Here, in this work, we report a straightforward approach for achieving site-specific properties that takes advantage of eutectic solidification characteristics. We demonstrate that the yield strength of a eutectic Al-Cu-Ce-Zr alloy can be tuned by varying the laser scan speed in concert with hatch spacing in laser powder bed fusion AM. A faster speed increases solidification rate resulting in finer eutectic spacing and higher strength. The hatch spacing is reduced at faster speeds to ensure overlap between melt pools which become smaller with increasing scan speed. The yield strength and its anisotropy relative to build direction are further tunable with a heat treatment. The scan speed-eutectic spacing-strength relationship is successfully applied to print a complex pattern of site-specific hardness in the alloy. The generalized principle of using AM for a eutectic alloy to create site-specific properties and anisotropy in properties is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2019

High-throughput simulations for insight into grain boundary structure-property relationships and other complex microstructural phenomena

High-throughput simulations can be a powerful tool in the discovery of new materials and behaviors. As part of a special issue on Rising Stars in Computational Materials Science, this paper uses the work of the author to show how high-throughput simulations have had an impact in grain boundary structure-property relationships and other complex microstructural phenomena. The work demonstrates how new tools designed to analyze large datasets produced by high-throughput simulations are enabling comprehensive grain boundary structure-property relationships to be obtained. High-throughput simulations are also used to demonstrate the impact descriptive and inferential statistics has had in extracting key aspects of deformation in metallic glasses. Finally, several different examples are used to show a balanced approach between simulations designed to survey and simulations designed for detailed analysis. Together, the two approaches provide a comprehensive picture of the variety of behaviors that exist, while ensuring that the physics underlying the behaviors are thoroughly understood.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A data-driven method for modelling dissipation rates in stratified turbulence

We present a deep probabilistic convolutional neural network (PCNN) model for predicting local values of small-scale mixing properties in stratified turbulent flows, namely the dissipation rates of turbulent kinetic energy and density variance, $\varepsilon$ and $\chi$ . Inputs to the PCNN are vertical columns of velocity and density gradients, motivated by data typically available from microstructure profilers in the ocean. The architecture is designed to enable the model to capture several characteristic features of stratified turbulence, in particular the dependence of small-scale isotropy on the buoyancy Reynolds number $Re_b:=\varepsilon /(\nu N^2)$ , where $\nu$ is the kinematic viscosity and $N$ is the background buoyancy frequency, the correlation between suitably locally averaged density gradients and turbulence intensity and the importance of capturing the tails of the probability distribution functions of values of dissipation. Empirically modified versions of commonly used isotropic models for $\varepsilon$ and $\chi$ that depend only on vertical derivatives of density and velocity are proposed based on the asymptotic regimes $Re_b\ll 1$ and $Re_b\gg 1$ , and serve as an instructive benchmark for comparison with the data-driven approach. When trained and tested on a simulation of stratified decaying turbulence which accesses a range of turbulent regimes (associated with differing values of $Re_b$ ), the PCNN outperforms assumptions of isotropy significantly as $Re_b$ decreases, and additionally demonstrates improvements over the fitted empirical models. A differential sensitivity analysis of the PCNN facilitates a comparison with the theoretical models and provides a physical interpretation of the features enabling it to make improved predictions.

42 ENGINEERING↗
DOE OSTI2025

Quantifying Impacts of Biomass Pelletization on Fast Pyrolysis Using a Single-Particle Reactor, X-ray Computed Tomography, and Computational Modeling

The pore structure and density of lignocellulosic feedstocks dictate intraparticle transport phenomena and thereby play an important role in thermochemical conversion processes such as fast pyrolysis for biofuel and biochemical production. Variations in microstructure are inherent from different biomass species and can be introduced by preprocessing techniques such as cutting and pelletization. Morphological changes also occur during conversion and lead to vastly different pore structures and behavior during pyrolysis, which impact required conversion times and product distributions. The current work presents a comprehensive comparison of fast pyrolysis of neat and pelletized pine feedstocks, which includes single-particle experiments, modeling, and 3D imaging by X-ray computed tomography (XCT). The particle-scale model included anisotropic heat and mass transport in a shrinking particle with pyrolysis reactions based on the CRECK mechanism with boundary conditions informed by reactor-scale simulations of the single-particle reactor. The models were validated by measurements of the temperature and mass loss from single-particle pyrolysis experiments of neat and pelletized pine. Quantitative analysis of XCT geometries revealed that pyrolytic conversion yielded chars with increased porosity and permeability compared to the unpyrolyzed materials, along with decreased tortuosity and anisotropy. Pelletization of the pine feedstock resulted in a much denser, less permeable material, which converted slower and produced more residual char after pyrolysis compared to neat pine. The results from particle modeling revealed that accounting for the dynamic and anisotropic heat and mass transport caused by differences in pore structure is critical to achieving agreement with experimental results. Overall, this study highlights the dramatic differences in conversion behavior imparted by pelletization and the importance of capturing microstructural attributes in computational models to guide the design and optimization of pyrolysis processes for specific biomass feedstocks.

09 BIOMASS FUELS↗
DOE OSTI2014

Role of microstructures on the M1-M2 phase transition in epitaxial VO2 thin films

Vanadium dioxide (VO2) with its unique sharp resistivity change at the metal-insulator transition (MIT) has been extensively considered for the near-future terahertz/infrared devices and energy harvesting systems. Controlling the epitaxial quality and microstructures of vanadium dioxide thin films and understanding the metal-insulator transition behaviors are therefore critical to novel device development. The metal-insulator transition behaviors of the epitaxial vanadium dioxide thin films deposited on Al2O3 (0001) substrates were systematically studied by characterizing the temperature dependency of both Raman spectrum and Fourier transform infrared spectroscopy. Our findings on the correlation between the nucleation dynamics of intermediate monoclinic (M2) phase with microstructures will open a new avenue for the design and integration of advanced heterostructures with controllable multifunctionalities for sensing and imaging system applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Spatially varying colloidal phase behavior on multi-dimensional energy landscapes

A method is reported to determine equilibrium concentration profiles and local phase behavior for colloids on multi-dimensional energy landscapes. A general expression is derived based on local particle concentration and osmotic pressure differences that are balanced by forces on colloids due to energy landscape gradients. This analysis is applied to colloidal particles in high frequency AC electric fields within octupolar electrodes, where the energy landscape can be shaped in two dimensions. These results are also directly applicable to any particles having induced dipoles in spatially non-uniform electromagnetic fields. Predictions based on modelling colloids with an effective hard disk equation of state indicate inhomogeneous solid and fluid states coexisting on different shaped energy landscapes including multiple minima. Model predictions show excellent agreement with time-averaged Brownian Dynamic simulations at equilibrium. Findings demonstrate a general approach to understand colloidal phase behavior on energy landscapes due to external fields, which could enable control of colloidal microstructure on morphing energy landscapes and the inverse design of fields to assemble hierarchically structured colloidal materials.

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

Less can be more: Insights on the role of electrode microstructure in redox flow batteries from two-dimensional direct numerical simulations

Understanding how to structure a porous electrode to facilitate fluid, mass, and charge transport is key to enhancing the performance of electrochemical devices, such as fuel cells, electrolyzers, and redox flow batteries (RFBs). Here, using a parallel computational framework, direct numerical simulations are carried out on idealized porous electrode microstructures for RFBs. Strategies to improve an electrode design starting from a regular lattice are explored. By introducing vacancies in the ordered arrangement, it is possible to achieve higher voltage efficiency at a given current density, thanks to improved mixing of reactive species, despite reducing the total reactive surface. Careful engineering of the location of vacancies, resulting in a density gradient, outperforms disordered configurations. Our simulation framework is a new tool to explore transport phenomena in RFBs, and our findings suggest new ways to design performant electrodes.

25 ENERGY STORAGE↗