Search NASA⌕ Search

SEARCH · Search NASA

Results for “microstructure model”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,045 records · Page 58

A Novel Approach to Investigate Thermal Protection Systems Materials

The Koo Research Group (KRG) at The University of Texas at Austin (UT) and KAI has specialized in “Ablation Research” for more than fifteen years. Recently, the group has developed several incredibly unique capabilities that can advance “Thermal Protection Systems (TPS) Materials Research & Development” using an integrated experimental and numerical approach. The paper aims to introduce the methodology KRG has developed to solve this challenging problem. It will discuss how the KRG develops “Process-Properties-Performance” relationships of novel TPS materials in a systematical approach using (a) processing and fabrication, (b) thermal characterization of properties, (c) aerothermal testing, (d) microstructures characterization and analysis, and (e) numerical modeling. Progress and challenges of this research will also be discussed.

Engineering↗

Oxidation behavior of TD-NiCr in a dynamic high temperature environment

The oxidation behavior of TD-NiCr has been studied in static and high-speed flowing air environments at 1100 and 1200 C. It has been found that the stable oxide morphologies formed on the specimens exposed to the static and dynamic environments were markedly different. The faceted crystal morphology characteristic of static oxidation was found to be unstable under high-temperature, high-speed flow conditions and was quickly replaced by a porous NiO 'mushroom' type structure. Also, it was found that the rate of formation of CrO3 from Cr2O3 was greatly enhanced by high gas velocity conditions. The stability of Cr2-O3 was found to be greatly improved by the presence of an outer NiO layer, even though the NiO layer was very porous. An oxidation model is proposed to explain the observed microstructures and overall oxidation behavior of TD-NiCr alloys.

Tenney, D. R.↗

Correlations Between Metallurgical Characterization Studies, Exploratory Mechanical Tests, and Continuum Mechanics Approaches to Constitutive Equations

Austenitic stainless steels, such as types 316 and 304, are widely used as pressure vessel materials in the temperature range of 425 to 650 C. Stainless steel specimens were tested to rupture at two different stress levels sigma and sigma 2 sigma 1 sigma 2) to establish the normal stain-time behavior. A subsequent test was performed in which the specimen was crept at the higher stress (sigma 1) to the beginning of the secondary stage of creep, presumed to be the strain/time conditions at which a steady state microstructure is developed, and then the stress was reduced to the lower level (sigma 2). The associated microstructure, and significance of this microstructure on the creep strain-hardening model for variable uniaxial loads were assesed and found to be consistent with the use of creep-recovery models at high stresses and temperatures and strain-hardening models at low stresses and tempertures.

Moteff, J.↗

Estimation of high temperature low cycle fatigue on the basis of inelastic strain and strainrate

Fatigue life at elevated temperature can be predicted by introducing parametric values obtained from monotonic constitutive behavior into the Universal-Slopes Equation. For directionally solidified MAR-M200+HF at 975 C, these parameters are the maximum stress achievable under entirely plastic (time-independent) and purely creep (time-dependent) conditions and the corresponding inelastic strains, as well as the elastic modulus. For materials which exhibit plasticity/creep interaction, two more pairs of monotonic parameters must be evaluated for fatigue life prediction. This life-prediction method based on the Universal-Slopes Equation, resulted from a constitutive model characterizing monotonic and cyclic data as inelastic strainrate as a function of inelastic strain. Characterizing monotonic data is this way, permitted distinction between different material responses such as strain-hardening, strain-softening, and dynamic recovery effects. Understanding and defining the region of influence of each of these effects facilitated formulation of the constitutive model in relation to the mechanical and microstructural processes occurring in the material under cyclic loading.

Berkovits, A.↗

Microstructures in type III events in the solar wind

A model is presented for the generation and evolution of bump-in-tail driven Langmuir waves in the solar wind during type III emission which removes a number of apparent inconsistencies between theory and observations. Growth rates and energy densities of Langmuir waves are considerably enhanced, permitting growth to overcome linear scattering losses, and also allowing nonlinear decay into ion-acoustic waves, in line with observations. Estimates are made of the probability distribution p(E), of wave field strengths E, based on linear and nonlinear wave-packet evolution, yielding p(E) approximately equal to E exp -alpha, alpha greater than or equal to 3. This helps explain why very high values of E are rarely found in the measured spiky wave turbulence.

Melrose, D. B.↗

Noncontact temperature measurement: Requirements and applications for metals and alloys research

Temperature measurement is an essential capability for almost all areas of metals and alloys research. In the microgravity environment many of the science priorities that have been identified for metals and alloys also require noncontact temperature measurement capability. For example, in order to exploit the full potential of containerless processing, it is critical to have available a suitable noncontact temperature measurement system. This system is needed to track continuously the thermal history, including melt undercooling and rapid recalescence, of relatively small metal spheres during free-fall motion in drop tube systems. During containerless processing with levitation-based equipment, accurate noncontact temperature measurement is required to monitor one or more quasi-static samples with sufficient spatial and thermal resolution to follow the progress of solidification fronts originating in undercooled melts. In crystal growth, thermal migration, coarsening and other experiments high resolution thermal maps would be a valuable asset in the understanding and modeling of solidification processes, fluid flows and microstructure development. The science and applications requirements place several constraints on the spatial resolution, response time and accuracy of suitable instrumentation.

Perepezko, J. H.↗

Indirect measurement of the viscosity of the intergranular glass phase in yttria-sintered silicon nitride

Dense, sintered Si3N4 possesses a residual intergranular glass phase which softens at high temperatures, resulting in degradation of the ceramic's mechanical properties at high temperatures. An important parameter in the determination of the high temperature mechanical properties of sintered Si3N4 is the temperature-viscosity relationship of the intergranular glass. A method for indirectly measuring the intergranular glass viscosity at a given temperature using physical modelling of a two phase glass crystal microstructure and beam bending viscometry measurements of Si3N4 is described. Intergranular glass viscosities obtained by this method are presented for a yttria sintered Si3N4.

Dittmar, Mark B.↗

Anisotropic shock response of single-crystalline β-phase tin shock physics, molecular dynamics, plasticity

Mesoscale simulations of the dynamic response of polycrystalline metals to shockwave compression can provide unique insight in to the nature of the various physical mechanisms responsible for material failure. This approach requires a constitutive description for individual grains and boundaries, including defects such as dislocations, within an explicit representation of the microstructure geometry and evolving deformation fields. Computational models of the single-crystal constituents cannot be unambiguously constrained by traditional measurements of the shock or stress-strain response of polycrystalline metals. Instead, these models require comprehensive measurements of the anisotropic shock response of single crystals for their calibration and validation. We present a coordinated experimental and simulation campaign on the shock response of single-crystalline β-phase tin demonstrating a remarkable anisotropic elastic-plastic response of the metal. This anisotropy will be explained with the help of molecular dynamics simulations that show preferred twinning in one orientation.

36 MATERIALS SCIENCE↗

Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method

Abstract An interface-modified reproducing kernel particle method (IM-RKPM) is introduced in this work to allow for a direct model construction from image pixels of heterogeneous polycrystalline Li-ion battery microstructures. The interface-modified reproducing kernel (IM-RK) approximation is constructed through scaling of a kernel function by a regularized distance function in conjunction with strategic placement of interface node locations. This leads to RK shape functions with either weak or strong discontinuities across material interfaces, suitable for modeling various interface mechanics. With the placement of a triple junction node and distance-based scaling of kernel functions, the resulting IM-RK shape function also possesses proper discontinuities at the triple junctions. This IM-RK approximation effectively remedies the well-known Gibb’s oscillation in the smooth approximation of discontinuities. Different from the conventional meshfree approaches for interface discontinuities, this IM-RK approach is done without additional degrees of freedom associated with the enrichment functions, and it is formulated with the standard procedures in the RK shape function construction. This work focuses on identifying the accuracy and convergence properties of IM-RKPM for modeling the coupled electro-chemo-mechanical system. A linear patch test is formulated and numerically tested for the electro-chemo-mechanical coupled problem with a Butler–Volmer boundary condition representing the physical conditions in Li-ion battery microstructures. This is followed by verification of the optimal rates of convergence of IM-RKPM for solving the coupled problem with higher order solutions. The image-based modeling of Li-ion battery microstructures in the numerical examples demonstrates the applicability of the proposed method to realistic Li-ion battery materials modeling.

25 ENERGY STORAGE↗

Characterization of Flame Cut Heavy Steel: Modeling of Temperature History and Residual Stress Formation

Heavy steel plates are used in demanding applications that require both high strength and hardness. An important step in the production of such components is cutting the plates with a cost-effective thermal cutting method such as flame cutting. Flame cutting is performed with a controlled flame and oxygen jet, which burns the steel and forms a cutting edge. However, the thermal cutting of heavy steel plates causes several problems. A heat-affected zone (HAZ) is generated at the cut edge due to the steep temperature gradient. Consequently, volume changes, hardness variations, and microstructural changes occur in the HAZ. In addition, residual stresses are formed at the cut edge during the process. In the worst case, unsuitable flame cutting practices generate cracks at the cut edge. The flame cutting of thick steel plate was modeled using the commercial finite element software ABAQUS. The results of modeling were verified by X-ray diffraction-based residual stress measurements and microstructural analysis. The model provides several outcomes, such as obtaining more information related to the formation of residual stresses and the temperature history during the flame cutting process. In addition, an extensive series of flame cut samples was designed with the assistance of the model.

36 MATERIALS SCIENCE↗

Machine-learning-assisted deciphering of microstructural effects on ionic transport in composite materials: A case study of Li 7 La 3 Zr 2 O 12 -LiCoO 2

The effective diffusivity of ionic species in multiphase materials is critical for the design and function of composite materials for electrochemical energy storage. In practice, effective diffusivity depends sensitively not only on the intrinsic diffusivities of constituting materials but also on their topological arrangement; nevertheless, these coupled contributions are oversimplified in most analytical models. Here, we combine atomistically informed mesoscale modeling and machine learning (ML) analysis to unravel how such features affect effective diffusivity in two-phase composites. Using the Li 7 La 3 Zr 2 O 12 -LiCoO 2 composite solid-state battery cathode as a model system, we compute effective diffusivity for 600 distinct dense polycrystalline microstructures with different topological configurations of grains, grain boundaries, and heterointerfaces. We verify that in addition to atomic-scale variabilities, microstructural feature diversity can significantly impact effective transport properties. Across the ensemble of test microstructures, this often results in bimodal distributions of effective diffusivity that encompass two qualitatively distinct operating mechanisms, which we identify via flux analysis. An ML approach reveals that the most critical determining factors for effective diffusivity are the connectivity of bulk phases and their heterointerfaces. The role of ionic mobility at the heterointerfaces is also discussed. These insights highlight the combined importance of microstructure and interface engineering in tuning the transport properties of ionic species in composite materials. In conclusion, our framework can also be extended for understanding generic microstructure-property relationships in other complex multiphase materials.

25 ENERGY STORAGE↗

Utilizing machine learning to predict tensile ductility and yield strength of CoNiV-based multi-principal elements alloys

This study explores the use of machine learning (ML) as a computational tool to accelerate the design of multi-principal element alloys (MPEAs) with improved tensile elongation. An ML model was trained using available experimental data from the literature along with theoretically derived features to predict yield strength (YS) and ductility. A subset of ML-predicted compositions—CoNiVFe, CoNiVTi, CoNiVTiFe, and CoCrNiVTi—was synthesized and evaluated through tensile testing. The ML model underpredicted YS by approximately 20–30 % and overpredicted ductility by 60–70 % for Ti-containing alloys. Microstructural analysis revealed that Ti segregation at interdendritic regions contributed to early fracture, leading to discrepancies in ductility predictions. Ti segregation at these regions likely drives the increased YS due to segregation strengthening. In contrast, the CoNiVFe alloy showed good agreement with both experimental YS and elongation, with prediction errors of ∼10.2 % and ∼20.7 %, respectively. Microstructural characterization revealed minimal segregation in this alloy, suggesting that the ML model can reliably predict the properties of alloys with little to no segregation. These findings highlight the capability of ML in predicting YS with good accuracy but underscore its limitations in capturing defect-driven failure mechanisms such as segregation-induced embrittlement.

36 MATERIALS SCIENCE↗

Regional Subsidence Analysis Through a Multi-Scale Modeling Framework Based on Breakage Mechanics

Although poroelastic models are often used to explain the delay between subsurface fluid depletion and ground subsidence, inelastic compaction involving permanent changes of rock microstructure may exacerbate hydro-mechanical coupling, thus influencing the interpretation of measurements and long-term forecasts. Here, a multi-scale modeling approach is discussed, which accounts for the inherent connection between rock microstructure, hydraulic conductivity, and pore compaction. A constitutive model built within the framework of breakage mechanics is proposed to link the hydraulic conductivity of granular rocks with inelastic deformations and changes in grading caused by injection-depletion cycles at stress levels far from yielding. The proposed model has been incorporated into large-scale simulation frameworks, thus enabling the spatiotemporal mapping of regional subsidence through a hybrid, semi-analytical approach. Numerical results based on this strategy show that the model allows isolating near-field and far-field effects into the computation of land subsidence and can generate forecasts for different modeling scenarios (e.g., elastic and inelastic compaction, constant permeability, and concurrent change of compressibility and permeability). In particular, examples of simulations for the case of the Groningen gas field are discussed, showing the model capabilities to use both field measurements and laboratory tests for the generation of reasonable subsidence maps, without expensive computational costs. Results indicate that ignoring coupled inelastic effects has major consequences on the predicted timescale of subsidence. Specifically, while all the model scenarios produced similar long-term ground settlements, those ignoring breakage-dependent permeability changes result in a variation of the temporal window of residual subsidence of the order of several decades.

58 GEOSCIENCES↗

PyCMG-based Simulation of Volumetric Concrete Microstructure

Concrete is a complex, heterogeneous material with a microstructure composed of aggregates, cement paste, and pores spanning multiple length scales. Understanding this microstructure is critical for advancing the performance, durability, and modeling of concrete-based systems. While experimental imaging such as X-ray computed tomography (XCT) provides valuable insights, generating large datasets with detailed ground truth annotations is both costly and labor-intensive due to challenges in segmenting similar phases, such as aggregates and cement paste, that often share similar attenuation properties. To address this, we developed a pipeline to simulate realistic 3D concrete microstructures using the open-source Python package PyCMG. This simulation effort focuses on generating high-fidelity, annotated microstructures that can serve as training or benchmarking datasets for image analysis, segmentation algorithms, and machine learning models, particularly in scenarios where experimental data is scarce.

Ziabari, Amir [Oak Ridge National Laboratory; ORNL↗

Equilibria model for pH variations and ion adsorption in capacitive deionization electrodes

Ion adsorption and equilibrium between electrolyte and microstructure of porous electrodes are at the heart of capacitive deionization (CDI) research. Surface functional groups are among the factors which fundamentally affect adsorption characteristics of the material and hence CDI system performance in general. Current CDI-based models for surface charge are mainly based on a fixed (constant) charge density, and do not treat acid-base equilibria of electrode microstructure including so-called micropores. Here, we expand current models by coupling the modified Donnan (mD) model with weak electrolyte acid-base equilibria theory. In our model, surface charge density can vary based on equilibrium constants (pK's) of individual surface groups as well as micropore and electrolyte pH environments. In this initial paper, we consider this equilibrium in the absence of Faradaic reactions. The model shows the preferential adsorption of cations versus anions to surfaces with respectively acidic or basic surface functional groups. We introduce a new parameter we term “chemical charge efficiency” to quantify efficiency of salt removal due to surface functional groups. We validate our model using well controlled titration experiments for an activated carbon cloth (ACC), and quantify initial and final pH of solution after adding the ACC sample. We also leverage inductively coupled plasma mass spectrometry (ICP-MS) and ion chromatography (IC) to quantify the final background concentrations of individual ionic species. Our results show a very good agreement between experiments and model. The model is extendable to a wide variety of porous electrode systems and CDI systems with applied potential.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Creep Behavior and Phase Equilibria in Model Precipitate Strengthened Alumina-Forming Austenitic Alloys

Abstract Creep-rupture behavior and microstructural response in alumina-forming austenitic (AFA) alloys with two different precipitation strengthening mechanisms, “Laves-phase + M 23 C 6 carbide” and “coherent L1 2 γ′-Ni 3 (Al,Ti),” were explored as “model” cases of multi-phase, multi-scale heat-resistant AFA alloys for 650–750°C use. These alloys will be used to guide and verify computational alloy design and life-prediction modeling under an on-going eXtremeMAT project through the Office of Fossil Energy and Carbon Management, US Department of Energy. Computational thermodynamics were used to design and predict the amounts of strengthening and deteriorating secondary phases at 750°C. Creep-rupture lives of the alloys tested at 750°C and 100 MPa were in a range of 4000–9000 h, and the microstructure at the gage/grip after creep-rupture testing was compared with isothermally aged alloys for 1500 h, as well as the calculated phases. Detailed microstructure characterization includes phase identification, volume fraction measurement, and compositional analysis, which were correlated with the creep-rupture properties. High-temperature oxidation resistance was also screened and compared with commercial, chromia-forming heat-resistant steels. These model alloys also provide the basis for further design and optimization of next generation AFA alloys with improved creep resistance.

36 MATERIALS SCIENCE↗

Bridging Nano- and Microscale X-Ray Tomography for Battery Research by Leveraging Artificial Intelligence

X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials' absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for battery material imaging to voxel sizes of 50 nm, a limit previously achievable only with synchrotron facilities. Given the non-destructive nature of CT, in situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area and volume expansion, during battery operation or cycling. Combined with artificial intelligence and machine learning analysis techniques, nano-CT has enabled the development of predictive models to analyse the impact of the electrode microstructure on cell performances or the influence of material heterogeneities on electrochemical responses. In this Review, we discuss the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multiscale battery behavioural models.

diagnostics↗

Orthotropic Viscoelastic Modeling of Polymeric Battery Separator

The separator is one of the most important components for battery safety since its failure can cause short-circuit that may lead to thermal runaway. As such, understanding the mechanical behaviors of separators is critical to improve battery safety. Nevertheless, modeling the mechanical behaviors of separators is challenging since they usually depend on multiple factors such as orientation, temperature and strain rate due to the microstructure of separators. Here we develop an orthotropic linear viscoelastic model for separators, which takes both anisotropy and strain rate effects into account. This model is the first step of a future model that considers more factors such as temperature and large deformation. The model has been implemented as a user material model in a finite element package, where a discretization algorithm is developed to evaluate the stiffness-based hereditary integral with a kernel of Prony series. The user model has been applied to a polypropylene (PP) separator Celgard ® 2400. The simulation results agree well with experimental data, which include the rate dependent uniaxial tensile behavior of the separator in the two in-plane material directions and in shear, and the strain contour patterns of every strain component during biaxial tension.

25 ENERGY STORAGE↗