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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

SEM Milestone Report for MARMOT Validation Project

MARMOT is a meso-scale fuel code under development by NEAMS designed to predict the microstructure evolution of fuel materials to determine updated thermal and mechanical property models. These updated models can be used by engineering scale codes such as BISON. To support its development and validation the MARMOT Validation Project (MVP) was initiated and consists of extensive microstructure and thermal characterization of U-10Zr fuel material before and at discreet levels of irradiation. This report summarizes the SEM and EDS characterization of the lowest burnup samples (1.5% FIMA) and how they compare to pre-irradiation SEM and EDS.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2025

Micromechanical Surrogate Machine Learning Model for Creep Deformation Modeling

Process variability during the manufacture of gas turbine engine hot section components can significantly affect the material’s resulting microstructure. In casting, for instance, geometric variation within a component (thin sections versus thick sections, radial location) influences cooling rates and the resulting grain size. The high temperature creep response is known to be sensitive to grain size owing to a diffusional creep mechanism which occurs more readily along grain boundaries. Microstructural variation correspondingly drives mechanical behavior which propagates into component scale performance uncertainty. These factors are essential when planning inspection, maintenance, and repair strategies within a reliability framework. These benefits provide opportunities to increase overall energy efficiency through refined margins. Critically, there is an opportunity to bolster existing data-driven reliability models using physics-driven process-structure-property relations. Here we present recent work establishing a framework for evaluating the probabilistic creep performance of high-temperature materials. A novel microstructure-sensitive crystal plasticity finite element model is established that captures both grain boundary and crystallographic deformation effects. The computationally expensive physics model is calibrated using a statistical approach and this high-fidelity model is subsequently used to train a computationally efficient machine learning surrogate model. The surrogate model is essential for sampling a large ensemble of simulated structure-property pair results. The ensemble data are then mined to extract salient trends to be incorporated into a microstructure-sensitive reliability model. The proposed approach represents a novel way to capture microstructure-sensitive trends from physics-based models within a modern reliability framework.

Fernandez-Zelaia, Patxi [ORNL]↗
DOE OSTI2025

Chemo-Mechanical Behavior and Stability of High-Loading Cathodes in Solid-State Batteries

Solid-state batteries can offer higher energy density and improved safety compared to lithium ion batteries, which use flammable liquid electrolytes. Increasing the ratio of cathode active materials in composite cathodes enhances the energy density and reduces manufacturing costs. Changes in the ratio of cathode active materials alter the microstructure and chemo-mechanical response of a cathode during operation. Understanding the relationship between composition, microstructure, and chemo-mechanical interactions is critical for optimizing solid-state cathodes. Here, in this study, we engineered composite cathodes with varying ratios of LiNi 0.8 Co 0.1 Mn 0.1 O 2 and Li 6 PS 5 Cl to systematically investigate the role of microstructural evolution in long-term chemo-mechanical transformations. Chemo-mechanical stresses resulting from the volume changes of the cathode active materials led to degradation mechanisms, such as fracture and interfacial delamination. Active material fracture and delamination led to underutilization of active material and significant capacity decay during cycling. Coatings that suppress active material-active material interactions during cycling may aid in suppressing the generation of local stress hotspots.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructural features and deuterium diffusion in lithium penta-aluminate pellets under He + and D + ion irradiation

Lithium (Li) penta-aluminate (LiAl 5 O 8 ) is investigated as a potential tritium (T) breeding material, with a focus on microstructural response to ion irradiation and deuterium (D) diffusion behavior. Under high-fluence ion irradiation (2 x 10 17 (He + +D + )/cm 2 ) at 773 K, LiAl 5 O 8 exhibits significant disorder on the Li sublattice, as revealed by atomic-resolution scanning transmission electron microscopy, while the Al and O sublattices remain stable, demonstrating strong resistance to structural amorphization. Irradiation induces the formation of platelet-shaped antiphase boundaries (APBs), which may serve as effective D trapping sites. Atom probe tomography suggests the presence of 6 LiD clusters in the mass spectra, though definite conclusions regarding APB composition are hindered by signal overlap and limited data statistics. Time-of-flight secondary ion mass spectrometry reveals that D retention approaches to saturation at 3 x 10 17 (He + +D + )/cm 2 . Isothermal and isochronal annealing studies determine an average diffusivity of 1.6 x 10 -13 at 773 K and an effective activation energy of 0.8 ± 0.1 eV for D migration. Compared to γ-LiAlO 2 , LiAl 5 O 8 demonstrates superior irradiation resistance, minimal Li loss, and enhanced D retention, underscoring its potential as a durable breeder material for T production. In conclusion, these findings provide key insights into the microstructural evolution, defect dynamics, and D retention mechanisms in LiAl 5 O 8 under reactor-relevant conditions.

42 ENGINEERING↗
DOE OSTI2026

A User-Friendly GUI Tool for Automated Microstructural Analysis of Fiber-Reinforced Composites and Porous Structures

Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
DOE OSTI2025

Toward Standardized Microscale Tensile Testing for Two‐Photon Polymerization‐Fabricated Materials in Liquid

Two-photon polymerization (TPP) enables the fabrication of intricate 3D microstructures with submicron precision, offering significant potential in biomedical applications like tissue engineering. In such applications, to print materials and structures with defined mechanics, it is crucial to understand how TPP printing parameters impact the material properties in a physiologically relevant liquid environment. Herein, an experimental approach utilizing microscale tensile testing (μTT) for the systematic measurement of TPP-fabricated microfibers submerged in liquid as a function of printing parameters is introduced. Using a diurethane dimethacrylate-based resin, the influence of printing parameters on microfiber geometry is first explored, demonstrating cross-sectional areas ranging from 1 to 36 μm 2 . Tensile testing reveals Young's moduli between 0.5 and 1.5 GPa and yield strengths from 10 to 60 MPa. The experimental data show an excellent fit with the Ogden hyperelastic polymer model, which enables a detailed analysis of how variations in writing speed, laser power, and printing path influence the mechanical properties of TPP microfibers. The μTT method is also showcased for evaluating multiple commercial resins and for performing cyclic loading experiments. Collectively, this study builds a foundation toward a standardized microscale tensile testing framework to characterize the mechanical properties of TPP printed structures.

mechanical characterization↗
DOE OSTI2026

Equilibrium and non-equilibrium effects in high pressure phase transformations of carbon

The behavior of carbon in the range 1–100 GPa and 1–10 kK is central to problems in planetary interiors, inertial confinement fusion targets, and high-pressure synthesis of carbon-based materials, but experiments in this regime are difficult and often provide only indirect constraints on phase behavior. As a result, phase boundary loci, structure, and limits of metastability at high pressure remain uncertain. In this work, machine-learning enhanced atomistic simulations are used to address this knowledge gap. We determine the melt line up to 100 GPa, the graphite-diamond phase boundary up to the melt line, and analyze structure of the coexisting phases. We show that the coexisting liquid evolves smoothly with pressure without evidence for a first-order liquid–liquid transition. Orientation-resolved graphite melting simulations indicate that basal-plane interfaces develop a dewetting layer and undergo layer-by-layer melting, producing kinetic hysteresis and an apparent orientation dependence of the melt line. Non-equilibrium quenches from the melt are used to construct a kinetically limiting graphite–diamond phase boundary for rapid quenches from above the melt line, and show that graphite is metastable at pressures of up to ≈ 25 GPa. These results provide bounds on equilibrium and metastable behavior in carbon relevant for interpreting high-pressure experiments and for designing synthesis pathways to specific carbon microstructures.

Lyu, Yanjun [Department of Materials Science and E↗
DOE OSTI2025

An Initial Microstructurally Informed Model of High Burnup Structure Formation in UO 2 Fuel

The microstructure of a UO 2 fuel pellet changes as burnup increases, impacting fuel performance. Predicting and characterizing high burnup structure (HBS) and dark zone formation is a key part of supporting burnup limit extensions for light water reactors. This paper describes a model developed through fitting radially resolved pellet data obtained from recently published microstructural characterization data. The model predicts grain size and grain character, in addition to pore density and size, with fitting dependencies on power history variables. Separately fitting power history variables to microstructural parameters allows for insight into the underlying physical phenomena for future model development. Additionally, experimental data have been correlated to an HBS fraction to facilitate the development of a model capable of predicting a total fuel restructured fraction at the engineering scale. In conclusion, this two-step approach provides a coupling from reactor power history to microstructural data to fractional HBS and creates a basis to model HBS-dependent parameters in a fuel performance code.

High burnup structure↗
DOE OSTI2024

Real-space visualization of a defect-mediated charge density wave transition

Here, we study the coupled charge density wave (CDW) and insulator-to-metal transitions in the 2D quantum material 1T-TaS 2 . By applying in situ cryogenic 4D scanning transmission electron microscopy with in situ electrical resistance measurements, we directly visualize the CDW transition and establish that the transition is mediated by basal dislocations (stacking solitons). We find that dislocations can both nucleate and pin the transition and locally alter the transition temperature T c by nearly ~75 K. This finding was enabled by the application of unsupervised machine learning to cluster five-dimensional, terabyte scale datasets, which demonstrate a one-to-one correlation between resistance—a global property—and local CDW domain-dislocation dynamics, thereby linking the material microstructure to device properties. This work represents a major step toward defect-engineering of quantum materials, which will become increasingly important as we aim to utilize such materials in real devices.

4D-STEM↗
DOE OSTI2025

Performance Evaluation of LPBF Manufactured 316H Components

This work represents the continuation of a benchmark study that includes modeling, fabrication and characterization as demonstration to support industry’s adoption of advanced manufacturing processes in a variety of structures. This comprehensive study investigated the feasibility of using additive manufacturing (AM) technologies, specifically Laser Powder Direct Energy Deposition (LP-DED) and Laser Powder Bed Fusion (LPBF), to produce complex nuclear microreactor components using 316H stainless steel. The research focused on manufacturing an expanded elbow pipe component with transitioning sections, which are traditionally difficult and costly to produce through conventional manufacturing methods. The overall study’s primary objectives are therefore demonstrating AM viability for nuclear applications, optimizing process parameters, developing comprehensive material characterization protocols, validating computational modeling approaches, and establishing manufacturing guidelines for complex geometries. Although the initial work included the phased approach of cubical, upscaled cylindrical components, it is to enable to obtain more knowledge for the printing of the expanded elbow structure. The project achieved significant progress in process development by successfully optimizing LP-DED parameters to achieve 99.16-99.97% relative density in 316H stainless steel components. Through systematic evaluation of sixteen cube samples with varied laser powers (400-700W) and scan speeds (600-900 mm/min), optimal processing windows were identified at 500-550W with 600-700 mm/min or 650-700W with 650-900 mm/min scan speeds. The DED manufactured 316H demonstrated mechanical properties comparable or superior to wrought materials, with Young's modulus ranging from 153-208 GPa and controlled microstructural characteristics including greater than 95% face-centered cubic (FCC) phases and engineered cellular structures with sizes between 3.23-6.17 µm.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2025

Phase-field predictions of the influence of cooling rates during AM on the Evolution of Microstructures in Nickel-Based Single Crystal Superalloys

Additive manufacturing of single crystals made of Ni-based superalloys offers major cost savings for gas turbine engines with the inclusion of internal cooling channels. However, the lack of understanding of the effect of transient thermal conditions on solidification grain structure during additive manufacturing hinders the potential for process control to maintain the single crystal quality. The use of high-fidelity simulations through high performance computing to predict the evolution of the solidification microstructure will enhance the abilities to tailor the microstructures through process optimization. Phase field simulations are used to determine the effect local thermal conditions and defects on the stability of the solidification morphology, specifically with respect to the onset of columnar-to-equiaxed transition that results in the loss of the single crystal. The results are expected to be instrumental for developing future surrogate models to speed up the integration of design and manufacturing of turbine blades under the harsh in-service conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Hierarchical microstructural parameters governing electrical and thermal conductivity in Al-based eutectic systems

Enhancing conductivity properties of aluminum alloys can drive superior performance and efficiency across diverse engineering applications, yet currently commercially available Al casting alloys are severely limited in electrical and thermal conductivity. There is a general understanding that the presence of any defects and alloying elements within Al will inevitably lower conductivity properties. However, there is a lack of comparative studies evaluating the intricate interplay between the microstructural parameters and conductivity properties across cast Al-based eutectic systems. Therefore, this study bridges this gap by conducting a comparative study across hypoeutectic alloy compositions consisting of 50 % primary Al and 50 % eutectic region to elucidate the key parameters that dictate conductivity properties. This research contributes to advancing the understanding of alloy design opportunities for high conductivity, high strength castable Al alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Clustering Algorithm for AM Parts using GSH and EDT with Autoencoder

SAND2025-10103O The Clustering Algorithm for AM Parts Using GSH and (EDT With Autoencoder is a software tool. It uses a clustering algorithm for additive manufacturing (AM) parts using generalized spherical harmonics (GSH) and Euclidean distance transform (EDT) with an autoencoder to quantify material microstructure. The tool offers improved sensitivity to microstructural changes compared to traditional approaches. The tool integrates multiple microstructural properties, such as grain morphology, crystallographic orientation, and material phase information, to provide a comprehensive analysis of material microstructures. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rodgers, Theron [Sandia National Lab. (SNL-CA), Li↗
DOE OSTI2024

Unraveling electrochemo-mechanical aspects of core–shell composite cathode for sulfide based all-solid-state batteries

All-solid-state lithium batteries (ASSLBs) are emerging as promising next-generation batteries for electric vehicles owing to their high energy densities and safety features. However, challenges such as inadequate material percolation and low cathode utilization often hinder their potential. This paper presents a core–shell approach to optimize the cathode active material (CAM) utilization. The resultant CAM composite showed high ionic conductivity, a highly dense microstructure with <10% porosity, and minimal stack pressure changes during electrochemical cycling. The maximum CAM utilization was achieved while effectively mitigating electrochemo-mechanical side reactions by applying a uniformly coated Li 6 PS 5 Cl solid electrolyte layer (≈500 nm) and a LiNbO 3 buffer layer (≈10 nm) onto LiNi 0.8 Mn 0.1 Co 0.1 O 2 particles (LPSCl@LNO@NMC). The engineered LPSCl@LNO@NMC composites, which incorporated a 5 wt% LPSCl coating on LNO@NMC powders, exhibited a dense microstructure that enhanced the mechanical stability at the cathode. Sulfide-based solid electrolyte (SSE)/SSE contact provided better ionic pathways within the composite and increased CAM utilization. Thus, an enhanced reversible capacity (197 mA h g -1 ) and exceptional high-rate cycling performance (86.3% capacity retention after 1000 cycles at 2C) were observed. These findings pave the way for the advancement and commercialization of high-performance ASSLBs.

25 ENERGY STORAGE↗
DOE OSTI2026

A physically based mechanical model for Mullins effect in thermoplastic polyurethanes

Despite decades of research, connecting the chemical and physical structure of thermoplastic polyurethanes to their mechanical properties remains highly challenging. Of particular note are their large-deformation and rate-dependent behaviors, which vary greatly with molecular chemistry, including the type and relative content of soft and hard segments. In this work, we develop a physically motivated mechanical theory for predicting the behavior of thermoplastic polyurethanes. The theory incorporates a representation of microstructural evolution during mechanical deformation, which captures the signatures of stress softening over cyclic loading (commonly referred to as the Mullins effect). There are only eight physically motivated fitting parameters, including a direct dependence on the hard segment fraction. The model predicts that increasing the hard segment fraction leads to higher stiffness and greater energy dissipation, in quantitative agreement with published experimental data. Furthermore, we provide a comprehensive analysis of the model and validate its predictions across several independent datasets focused on mechanical characterization. Direct comparisons to experimental data demonstrate its predictive capability on the effect of loading rate, cyclic deformations, and applied tension or compression. Altogether, this work establishes a predictive framework that connects polymer chemistry and microstructure to emergent mechanical behaviors.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Texture development in magnetostrictive Fe-Ga alloys processed by laser powder bed fusion

Iron-gallium (Fe-Ga, Galfenol) alloys are promising magnetostrictive materials for actuators, sensors, and energy harvesting, but their performance is highly sensitive to microstructure and texture. Additive manufacturing by laser powder bed fusion (LPBF) offers a pathway to engineer texture and integrate functional materials into complex geometries. Here, we fabricate Fe-Ga alloys (Fe 82.2 Ga 17.8 ) by LPBF of gas-atomized powders and systematically optimize laser power and scan speed to maximize density and control texture. Nearly full-density parts (up to 99.6 %) are achieved within a narrow processing window. Electron backscatter diffraction (EBSD) reveals a strong <100> fiber texture aligned with the build direction and columnar grains up to 1 mm long. Magnetostriction measurements show saturation magnetostriction of 190 ppm in the build direction. Correlating texture data with macroscopic magnetostriction, we estimate intrinsic magnetostriction constants (λ 100 = 228 ppm, λ 111 = 12 ppm), closely matching single crystal-derived values. These results demonstrate the critical interplay between processing, texture, and functional performance in additively manufactured Fe-Ga alloys and establish LPBF as a viable route for high-performance magnetostrictive materials.

Additive manufacturing↗