Investigation of alloys of the ternary Ti-Nb-Cr system
Microstructural and X-ray phase analysis of alloys of titanium niobium chromium system
NIOBIUM ALLOY↗
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ML-based Micro-CT SOFC Microstructure Models (from Kent 2026 Microstructural Augmentation paper)
Overview -------------------------- This repository contains datasets from the manuscript **"Enhanced Generalizability to Deep-Learning Quantification of 3D Microstructural Characteristics through Microstructurally Aware Augmentation of Scarce Data"** (*William F. Kent, Rochan Bajpai, Rachel C. Kurchin, William K. Epting, Harry W. Abernathy, Paul A. Salvador. Submitted 2026*). The methods are also described in the dissertation **Data Intensive Analysis of Solid Oxide Cell Microstructures** (*Doctoral dissertation, Carnegie Mellon University, 2025*). The datasets here are trained convolutional neural network (CNN) models for predicting key microstructural properties of solid oxide cell (SOC) electrodes from low-res, 2-channel 3D images, as well as some helpful code. The parameters for input images are provided in the paper. Sample data is provided in the file `Combined_anode_aug_dual_1k_examples` - that particular data was used to train `anode_all_aug.pth` and will work most accurately with that model. Please familiarize yourself with all caveats on accuracy and applicability, as detailed in the associated paper. Usage -------------------------- The basic usage is as follows, assuming `model_fn` is the path to the .pth file, and `X` is 2-channel input image(s) of the proper dimensions (either one image of shape `[2,12,24,24]`, or a batch of N input images of shape `[N,2,12,24,24]`): from CNN_inferencer import load_model_for_inference model = load_model_for_inference(model_fn) y_predicted = model(X) The model object automatically handles input scaling and output de-scaling based on the way the models were trained - in other words, pass in a 2-channel micro-CT image, and it will output microstructural property values in real units. ## Other model object attributes Note that model has useful attributes other than its forward pass model(X). * `model.output_descaler` - returns the output descaler object. Model does the de-scaling when generating inferences, but you may want to re-use this de-scaler on other values to e.g. compare predictions to ground truth from already-scaled training data. * `model.prop_names` - Gives the property names of the predicted y values, in order. Only exists if there's an output scaler as part of the model object, which there will be in the models provided here. ## Usage with sample data Here is a short script to use with the included sample data. from CNN_inferencer import display_predictions, load_model_for_inference, calculate_mape, parity_plot import h5py import numpy as np model_fn = 'anode_all_aug.pth' data_fn = 'Combined_anode_aug_dual_1k_examples.h5' N_samples = 200 figure_outdir = '.' model = load_model_for_inference(model_fn) with h5py.File(data_fn,'r') as f: XX = f['X'] #These are the 2-channel 3D images yy = f['y'] #These are the ground-truth microstructural properties, but they have been scaled for training - need to de-scale below N = XX.shape[0] #How many images total in the input data file #Run inferences on N_samples random samples from XX. #Run in a batch, much more efficient than one at a time. ii = np.random.choice(N,N_samples,replace=False) ii.sort() y_pred = model(XX[ii]) #Get the original/true (but normalized/scaled) values from the training dataset... #Because they were normalized, they are not in real units yet. So let's also de-scale them using model.output_scaler. y_true = model.output_scaler.transform(yy[ii]) #Let's display actual values for just 5 random ones for i in np.random.choice(N_samples,5,replace=False): display_predictions(y_true[i], y_pred[i], model.prop_names) #Make parity plots for each property (ground truth vs predicted values) #Also label each plot with the mean abs. percent error (MAPE) of the predicted values for i,key in enumerate(model.prop_names): mape = calculate_mape(y_true[:,i], y_pred[:,i]) parity_plot(y_true[:,i], y_pred[:,i], figure_outdir, key, extra_title=f' ({mape:.2f}% MAPE)')
3D microstructure↗
Microstructure-based modeling of inner oxygen pressure in solid oxide electrolysis cells: Analysis of electrode delamination and mitigation
One major degradation mechanism during long-term operation of solid oxide electrolysis cells (SOECs) is delamination of oxygen electrodes (OEs). The driving force for the electrode delamination could be the generated high inner oxygen pressure near the electrode-electrolyte interface during operation. However, the effects of transport properties and electrode thickness on the inner oxygen partial pressure are not well understood. Here a microstructure-based electrochemical model, which includes the conduction of electrons and oxygen ions coupled with Butler-Volmer-type chemical reactions at triple-phase-boundaries (TPBs), is employed to investigate the oxygen pressure in lanthanum strontium manganate (LSM)-based SOECs. The model is applied to both two-dimensional (2D) prototype microstructures and three-dimensional (3D) realistic microstructures, and the oxygen pressure is analyzed as a function of transport properties and electrode thickness under both potentiostatic and galvanostatic operations. The simulation results suggest strategies to suppress electrode delamination. The simulation results are compared to an analytical solution, and the discrepancies are attributed to the Butler-Volmer-type kinetics included in the microstructure-based model.
25 ENERGY STORAGE↗
Characterization of the as Manufactured Variability in a CVI SiC/SiC Woven Composite
The microstructure of a 2D woven ceramic matrix composite displays significant variability and irregularity. For example, a chemical vapor infiltrated (CVI) SiC/SiC composite exhibits significant amount of porosity arranged in irregular patterns. Furthermore, the fiber tows within a ply frequently have irregular shape and spacing, and the stacked plies are often misaligned and irregularly nested within each other. The goal of an ongoing project at NASA Glenn is to investigate the effects of the complex microstructure and its variability on the properties and the durability of the material. One key requirement for this effort is the development of methods to characterize the distribution in as-fabricated ceramic matrix composite (CMC) microstructures with the objective of correlating microstructural distribution parameters with mechanical performance. An initial task in this effort was to perform quantitative image analysis of polished cross sections of CVI SiC/SiC composite specimens. This analysis provided sample distributions of various microstructural composite features, including: inter-tow pore sizes and shapes, transverse sectioned tow sizes and shapes, and within ply tow spacing. This information can then be used to quantify the effect of extreme values of these features on the local stress state with the goal of determining the likelihood of matrix cracking at a given external load.
Bonacuse, Peter J.↗
Higher-Order Theory for Functionally Graded Materials
This paper presents the full generalization of the Cartesian coordinate-based higher-order theory for functionally graded materials developed by the authors during the past several years. This theory circumvents the problematic use of the standard micromechanical approach, based on the concept of a representative volume element, commonly employed in the analysis of functionally graded composites by explicitly coupling the local (microstructural) and global (macrostructural) responses. The theoretical framework is based on volumetric averaging of the various field quantities, together with imposition of boundary and interfacial conditions in an average sense between the subvolumes used to characterize the composite's functionally graded microstructure. The generalization outlined herein involves extension of the theoretical framework to enable the analysis of materials characterized by spatially variable microstructures in three directions. Specialization of the generalized theoretical framework to previously published versions of the higher-order theory for materials functionally graded in one and two directions is demonstrated. In the applications part of the paper we summarize the major findings obtained with the one-directional and two-directional versions of the higher-order theory. The results illustrate both the fundamental issues related to the influence of microstructure on microscopic and macroscopic quantities governing the response of composites and the technologically important applications. A major issue addressed herein is the applicability of the classical homogenization schemes in the analysis of functionally graded materials. The technologically important applications illustrate the utility of functionally graded microstructures in tailoring the response of structural components in a variety of applications involving uniform and gradient thermomechanical loading.
Aboudi, Jacob↗
Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material
Here, we have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.
36 MATERIALS SCIENCE↗
Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel
Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.
36 - MATERIALS SCIENCE↗
Analysis of gamma prime shape changes in a single crystal Ni-base superalloy
The microstructural evolution of a commercial single crystal superalloy, NASAIR 100, is analyzed using the existing high-temperature lattice mismatch data and high-temperature moduli obtained from tests on single crystals of gamma and gamma prime. A multiparticle analysis of the microstructural evolution is performed using a novel microstructural lattice simulation technique, MCFET. Under a uniaxial stress, a regular array of gamma prime particles in the simulated microstructure is predicted to coalesce and form a plate morphology, with the broad faces of the plates and stress axis perpendicular in tension but parallel in compression. These results are consistent with changes in gamma prime shape observed in NASAIR 100 following creep testing at 1000 C.
Gayda, J.↗
Tracking Dendritic Growth in Hydrogen-Based Hematite Reduction via Computer Vision
The reduction of hematite to metallic iron using hydrogen (H2) as a reducing agent presents a promising pathway for decarbonizing steel production. In this study, we employ a combination of in situ confocal scanning laser microscopy (CSLM) and advanced computer vision techniques to quantitatively analyze dendritic growth of ferrite during H2-based reduction of iron oxide at high temperatures. A workflow integrating Watershed Image Segmentation (WIS) and Lucas-Kanade Optical Flow (LKOF) is developed to extract both global and local kinetic information from time-resolved micrograph sequences. H2 reduction experiments conducted at 1400 degrees C and 1500 degrees C demonstrate a clear correlation between temperature and reduction rate, as evidenced by accuracy of fitted Johnson-Mehl-Avrami-Kolmogorov (JMAK) parameters. Optical flow analysis further elucidates the anisotropic and branched nature of dendritic growth, providing spatially resolved velocity fields that correlate well with global transformation kinetics. The proposed methodology demonstrates strong agreement with experimental measurements and literature values, offering a robust framework for automated image-based analysis to study kinetics through microstructural evolution in the reduction of iron ore, and likely other reaction-diffusion phenomena.
08 HYDROGEN↗
Examination of the influence of coatings on thin superalloy sections. Volume 1: Description and analysis
The effects of an aluminide coating and of section thickness were investigated on two cast nickel-base superalloys. Cast section thickness ranged from 0.038 cm to 0.15 cm. Simulated engine exposures in a jet fuel burner rig with cyclic air cooling were studied. The effects of surface machining before coating and re-machining and re-coating after exposures were examined. The properties evaluated included tensile strength, stress rupture, high-cycle mechanical fatigue, and thermal fatigue. A metallurgical analysis was made of the microstructures of the coated and uncoated alloy.
Kaufman, M.↗
Experimental laboratory measurement of thermophysical properties of selected coal types
A number of bituminous coals of moderate to high plasticity were examined, along with portions of their extrudates from the JPL 1.5-inch 850 F screw extruder. Portions of the condensed pyrolysis liquids released during extrusion, and of the gaseous products formed during extrusion were also analyzed. In addition to the traditional determinations, the coals and extrudates were examined in terms of microstructure (especially extractable fractions), thermal analysis (especially that associated with the plastic state), and reactivity towards thermal and catalyzed hydroliquefaction. The process of extrusion increases the fixed carbon content of coals by about 5% and tends to increase the surface area. Coals contaning 25% or more DMF-extractable material show an increase in extractables as a result of extrusion; those initially containing less than 20% extractables show a decrease as a result of extrusion. Both the raw and extruded samples of Kentucky #9 coal are highly reactive towards hydroliquefaction, undergoing conversions of 75 to 80% in 15 min and 85-94% in 60 min in a stirred clave.
Lloyd, W. G.↗
Carbides in iron-rich Fe-Mn-Cr-Mo-Al-Si-C systems
The optimization of high carbon iron-base superalloy properties with duplex microstructure gamma + M7C3 carbide requires analysis in the context of a seven-component system. Data are first provided here for the Fe-Mn-Cr-Mo-C quinary system, at 30 at. pct carbon. A characterization of competing carbides, according to a pseudoternary phase diagram at 35 wt pct iron, is made from isothermal sections. It is noted that while M7C3 and M3C carbides' occurrences are respectively favored at the Cr and Mn corners, the M2C carbide and molybdenum cementite are predominant with increasing amounts of Mo. Lattice parameters are reported for the various carbides.
Lemkey, F. D.↗
Snow property measurements correlative to microwave emission at 35 GHz
Snow microstructure, measured by plane section analysis, and snow wetness, measured by the dilution method, are used to calculate input parameters for a microwave emission model that uses the radiative transfer method. The scattering and absorbing properties are calculated by Mie theory. The effects of different equivalent sphere conversions, adjustments for near-field interference, and different snow wetness characterizations are compared for various snow conditions. The concentric shell geometry of liquid water in snow yields higher emissivities and better model results than the separate-sphere configuration for liquid water contents greater than 0.05, while at lower liquid water contents the separate-sphere treatment gives better results.
Davis, R. E.↗
Snow property measurements correlative to microwave emission at 35 GHz
Snow microstructure, measured by plane section analysis, and snow wetness, measured by the dilution method, are used to calculate input parameters for a microwave emission model that uses the radiative transfer method. The scattering and absorbing properties are calculated by Mie theory. The effects of different equivalent sphere conversions, adjustments for near-field interference, and different snow wetness characterizations are compared for various snow conditions. The concentric shell geometry of liquid water in snow yields higher emissivities and better model results than the separate-sphere configuration for liquid water contents greater than 0.05, while at lower liquid water contents the separate-sphere treatment gives better results.
Davis, Robert E.↗
Supercooling effects in faceted eutectic Nb-Si alloys
The effect of melt supercooling on the microstructure of an Nb-58 at. pct Si alloy is investigated experimentally using an electromagnetic levitation apparatus. It is found that, starting with an alloy nominally of eutectic composition, nucleation of Nb5Si3 occurs in the supercooled liquid first. Upon further cooling, the remaining liquid continues to supercool until the second phase, NbSi2 is nucleated, which is commonly accompanied by rapid recalescence. The primary phase exibits a eutectoid-type decomposition. The observations are discussed with reference to the results of quantitative microstructural measurements, compositional and thermal analysis, and preliminary thermodynamic modeling of the phase diagram.
Gokhale, A. B.↗
Heat pipe fatigue test specimen: Metallurgical evaluation
An innovative creep/fatigue test was run to simulate the temperature, mechanical load, and sodium corrosion conditions expected in a heat pipe designed to supply thermal energy to a Stirling cycle power converter. A sodium-charged Inconel 718 heat pipe with a Nickel 200 screen wick was operated for 1090 hr at temperatures between 950 K (1250 F) and 1050 K (1430 F) while being subjected to creep and fatigue loads in a servo-hydraulic testing machine. After testing, the heat pipe was sectioned and examined using optical microscopy, scanning electron microscopy, and electron microprobe analysis with wavelength dispersive x-ray spectroscopy. The analysis concentrated on evaluating topographic, microstructural, and chemical changes in the sodium exposed surfaces of the heat pipe wall and wick. Surface changes in the evaporator, condenser, and adiabatic sections of the heat pipe were examined in an effort to correlate the changes with the expected sodium environment in the heat pipe. This report describes the setup, operating conditions, and analytical results of the sodium heat pipe fatigue test.
Walak, Steven E.↗
Higher-Order Theory for Functionally Graded Materials
Functionally graded materials (FGM's) are a new generation of engineered materials wherein the microstructural details are spatially varied through nonuniform distribution of the reinforcement phase(s). Engineers accomplish this by using reinforcements with different properties, sizes, and shapes, as well as by interchanging the roles of the reinforcement and matrix phases in a continuous manner (ref. 1). The result is a microstructure that produces continuously or discretely changing thermal and mechanical properties at the macroscopic or continuum scale. This new concept of engineering the material's microstructure marks the beginning of a revolution both in the materials science and mechanics of materials areas since it allows one, for the first time, to fully integrate the material and structural considerations into the final design of structural components. Functionally graded materials are ideal candidates for applications involving severe thermal gradients, ranging from thermal structures in advanced aircraft and aerospace engines to computer circuit boards. Owing to the many variables that control the design of functionally graded microstructures, full exploitation of the FGM's potential requires the development of appropriate modeling strategies for their response to combined thermomechanical loads. Previously, most computational strategies for the response of FGM's did not explicitly couple the material's heterogeneous microstructure with the structural global analysis. Rather, local effective or macroscopic properties at a given point within the FGM were first obtained through homogenization based on a chosen micromechanics scheme and then subsequently used in a global thermomechanical analysis.
Aboudi, J.↗
Helium Release During Fracture and Granular Fragmentation of Rocks
Geogenic Helium-4 ( 4 He) in-situ increases locally in regions of large deformation generated naturally or anthropogenically. This gas release by deformation is a potential geochemical precursor signal for subsurface deformation. To evaluate the applicability of 4 He degassing for correlating deformation in different lithologies, we conducted high force crush tests, up to 97,800 N axial load, to assess the total 4 He released during fragmentation of the rocks. We observed that the highest 4 He released occurred in the sedimentary rocks and that release correlated strongly with lithologic age and U/Th content. Microstructural changes of the pre- and post-test rocks indicate that the degree of grain size reduction relates directly to the total 4 He released during crushing. The range of in-place 4 He was calculated based XRF measurements of uranium and thorium in each lithology, with the results indicating that the majority of the trapped 4 He was not released. However, the 4 He released by deformation depended upon how the each rock deformed during deformation and the degree of grain size reduction. We postulate that 4 He precursor signals can be used to understand subsurface deformation only if geomechanical and geochemical conditions for 4 He enrichment in a lithology are met.
Deformation signals↗