Atomic Scale Modeling of Microstructural Features and Defects in Shape Memory Alloys
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Gabriel Plummer↗
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Multiscale Modeling for the Analysis for Grain-Scale Fracture Within Aluminum Microstructures
Multiscale modeling methods for the analysis of metallic microstructures are discussed. Both molecular dynamics and the finite element method are used to analyze crack propagation and stress distribution in a nanoscale aluminum bicrystal model subjected to hydrostatic loading. Quantitative similarity is observed between the results from the two very different analysis methods. A bilinear traction-displacement relationship that may be embedded into cohesive zone finite elements is extracted from the nanoscale molecular dynamics results.
Glaessgen, Edward H.↗
Numerical simulation of low Prandtl number turbulent mixing
Numerical simulations of turbulent mixing of strongly diffusive scalar fields were carried out with and without subgrid-scale modeling of the small-scale strain field. For low Reynolds number flows, when the rate of strain field (determined primarily by the small scales) is fully resolved, the scalar microstructure was found to collapse under Batchelor rate-of-strain scaling even for small Prandtl numbers, in agreement with Kerr. For high Reynolds number flows, when small-scale straining is modeled with a subgrid-scale model, the scalar microstructure follows the Batchelor, Howells, and Townsend predictions that the small-scale rate-of-strain is irrelevant.
Gibson, C.↗
Multi-Scale Computational Modeling of Two-Phased Metal Using GMC Method
A multi-scale computational model for determining plastic behavior in two-phased CMSX-4 Ni-based superalloys is developed on a finite element analysis (FEA) framework employing crystal plasticity constitutive model that can capture the microstructural scale stress field. The generalized method of cells (GMC) micromechanics model is used for homogenizing the local field quantities. At first, GMC as stand-alone is validated by analyzing a repeating unit cell (RUC) as a two-phased sample with 72.9% volume fraction of gamma'-precipitate in the gamma-matrix phase and comparing the results with those predicted by finite element analysis (FEA) models incorporating the same crystal plasticity constitutive model. The global stress-strain behavior and the local field quantity distributions predicted by GMC demonstrated good agreement with FEA. High computational saving, at the expense of some accuracy in the components of local tensor field quantities, was obtained with GMC. Finally, the capability of the developed multi-scale model linking FEA and GMC to solve real life sized structures is demonstrated by analyzing an engine disc component and determining the microstructural scale details of the field quantities.
nickel alloys↗
Comparison of Commonly-Used Microwave Radiative Transfer Models for Snow Remote Sensing
This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (T(sub B)): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snowpack properties, we compared the simulated T(sub B) at 11, 19 and 37 GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (D(sub max)) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (D(sub max)), assuming non-sticky spheres for the DMRT models. Simulated T(sub B) sensitivity analysis using the same inputs shows relatively consistent T(sub B) behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18 K and 27 K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated T(sub B). Results using in-situ grain size measurements (SSA or D(sub max), depending on the model) give a mean T(sub B) RMSE (19 and 37 GHz) of the order of 16-26 K, which is similar for all models when the snow microstructure metrics are scaled. However, the MEMLS model converges to better results when driven by the correlation length estimated from in-situ SSA measurements rather than D(sub max) measurements. On a practical level, this paper shows that the SSA parameter, a snow property that is easy to retrieve in-situ, appears to be the most relevant parameter for characterizing snow microstructure, despite the need for a scaling factor.
Snow↗
Macro-scale Testing and Micromechanics Modeling of Fracture Behaviors for Boron Carbide Composites with Hierarchical Microstructures
With a unique combination of properties including high hardness, low density, wear and corrosion resistance, thermal stability, high neutron absorption, and semi-conductivity, boron carbide (B4C) is a candidate material for various engineering applications that involve extreme environment. The current applications of boron carbide, however, are limited by its intrinsic brittleness due to its strong covalent bonding. To toughen boron carbide, in this work hierarchical microstructure designs was used to provide multiple toughening mechanisms including crack deflection/bridging, micro-crack toughening, etc. Using field assisted sintering, B4C composites with hierarchical microstructure features including graphite platelets, micron and sub-micron sized TiB2 reinforcements were fabricated. The fracture toughness of fabricated B4C composites were previously measured at micro-scale using micro-indentation followed by post-testing microstructure inspection. However, questions including whether the fracture toughness enhancement measured at micro-scale can translate to macro-scale mechanical properties, and what are the fundamental mechanisms behind observed fracture toughness enhancement, remain to be answered. In this study, the fabricated B4C composites were tested using standardized four-point bending method to obtain fracture toughness at macro-scale. In addition, micromechanics modeling was conducted using MAC/GMC code and crack-band model to study the effect of residual stress and weak interphases on fracture behaviors of B4C composites reinforced with TiB2 particles. Through standardized four-point bending tests, fracture toughness enhancements up to 2.85, 3.32, and 3.65 MPa∙m(exp 1/2) (from 2.38 MPa∙m(exp 1/2)) were achieved for B4C composites with graphite platelets addition (micro/nano B4C), with TiB2 formation (micro B4C-TiB2), and with both graphite and TiB2 addition (micro/nano B4C-TiB2) respectively. Micromechanics modeling indicated that introduction of thermal residual stress and weak interphases caused enhanced micro-cracking behavior and resulted in the observed fracture toughness enhancement. These results furthered understanding about the mechanical behaviors at macro-scale and the mechanisms behind observed fracture toughness enhancement for B4C composites with hierarchical microstructures and can provide reference data for the future design of B4C composites with optimized microstructures for further fracture toughness enhancement.
Boron Carbide↗
Achieving High Efficiency in Reduced Order Modeling for Large Scale Polycrystal Plasticity Simulations
Reduced order models for the nonlinear response of heterogeneous microstructures typically require a construction (or training) stage to build the reduced order basis. In this manuscript, an efficient model construction strategy for the eigenstrain homogenization method (EHM) is presented. The proposed strategy relies on a parallel, element-by-element, conjugate gradient solver. Near linear scaling has been achieved with respect to the number of degrees of freedom used to resolve the microstructure. Linear scaling with respect to the number of pre-analyses required to construct the reduced order model (ROM) follows from the EHM formulation. Furthermore, a parallel implementation for fast evaluation of the constructed ROM has been developed using shared memory parallelization. It has been shown that for large microstructures with ≈ 10,000 grains, the total computational cost of evaluating the nonlinear response of a polycrystal could be reduced by approximately an order of magnitude using 32 cores with respect to serial ROM simulation. The present methodology has been verified using an additively manufactured polycrystalline microstructure of a nickel-based superalloy, Inconel 625. The capability of the developed framework to construct a ROM for such large microstructures, as well as the ability of the ROM to predict average and local quantities of interest has been demonstrated.
microscale↗
Microstructure Quantification and Random Forest Regression Models for Li4Ti5O12–Ni Property Prediction
All-solid-state structural lithium-ion batteries are sought to enable all-electric propulsion in next generation aerospace concepts through improved safety and systems level weight savings. In this work, the influence of processing conditions on microstructural evolution was evaluated for anode composites of strain-free Li4Ti5O12 and metallic nickel current collector. Beyond size distributions, this study explored methods of quantifying microstructural features that describe changes in the spatial distribution and coalescence of nickel particles as a function of sample composition and sintering conditions. Processing-microstructure-property relationships were described by microstructure quantifiers including nickel particle count per area, nearest neighbor distance distribution, and edge-to-edge distance distribution. Machine learning methods were applied to compare the relative influence of processing conditions and microstructural features on electrical conductivity and mechanical strength to optimize for simultaneous energy storage and load bearing performance. Insights gained from this work inform future evaluation of alternative energy storage materials and microstructures for multifunctional performance, and generation of microstructural descriptors strengthens modeling across length scales.
anode↗
Plasticity and Damage Modeling of Stress Asymmetry and Dynamic Behavior of AFS Additive Manufactured Aluminum Alloy 2219
The Solid State Additive Manufacturing (AM) process referred as MELD that fabricated the samples in this study, provides a new path for repairing, coating, joining and additive manufacturing metals and metal matrix composites. This research will be the first application of a physics-based microstructure dependent internal state variable (ISV) plasticity and damage material model to capture the mechanical response of an AM Aluminum Alloy (AA) 2219 via the MELD process. In this research, a microstructure-based internal state variable (ISV) plasticity-damage model was used to capture the mechanical behavior of AFS 2219 aluminum alloy. Aeroprobe Corporation, creator and patent holder for the MELD process, fabricated the material by pushing a solid filler rod of AA2219-T861 material through a hollow rotating tool onto an AA2219 T851 plate substrate. As feedstock, solid or powder precursor metals are pushed through a nonconsumable rotating cylindrical tool. Herein, added layers are deposited and metallurgically bonded to substrate material or previously deposited layers by the heat generated from the rotating tool through plastic deformation of the filler material. Once a layer has been added, the tool height increases, and starts the deposition of the next layer. This process results in beneficial properties such as grain refinement, homogenization and reduced porosity (fully dense). This process will experience temperatures similar to those in the weld nugget zone (WNZ) in friction stir welding (FSW), ranging from 0.6-0.9 Tm, with Tm being the melting point of the material. MELD is highly scalable with AA deposition rates reaching over 1000 cm3/hr, which allows for MELD being used for repairs, coatings, and building components. A motivating factor driving the research for physics-based history dependent material modeling of MELD components is the ability to accurately capture the stress-state and strain rate dependence in the material caused by variations in material microstructure from the MELD processing of new or repaired components. The ISV model incorporates microstructural content and is consistent with continuum level kinematics, kinetics, and thermodynamics. These features allow the ISV model to capture large deformations at the structural scale using the kinematic and isotropic hardening, while microscale damage is obtained from the microstructural features. The benefits of the ISV model arise from the inclusion of structure-property relationships identified from microstructural characterization and experimentation. The Bauschinger effect (BE) is an important concept, vital in the accurate prediction of cyclic stress-strain response of ductile materials such as metals. The ISV model has been successfully used to capture the behavior and damage, and the BE of different aluminum alloys and steels. The ISV model uses kinematic and isotropic hardening to help capture deformations of the material at the macro scale. To understand this hardening relationship, calculating the kinematic and isotropic hardening relationship in the material is warranted for a high-fidelity model. Electron Backscattered Diffraction (EBSD) was used to characterize the as-fabricated microstructure, where a fully-dense equiaxed grain morphology with average grain size of 2.5 μm was observed. Microhardness mapping of the as-built structures, monotonic tension and compression experiments at both quasi-static (0.001/s) strain rates, tension-followed-by-compression and compression-followed-by-tension experiments were performed to obtain the set of plasticity and damage constants necessary to capture strain rate and stress state behavior of this additive material. To calibrate the plasticity-damage model, a single set of constants were determined to capture the different stress states the MELD AA2219. One set of the constants was determined from experimental true stress-strain curves for the tension and compression data. Additionally, microstructural information and data from the open literature were used as the other model constants. This research is a first of its kind for AFS AA2219, includes correlating the ISV model to the monotonic experimental results that capture the isotropic and kinematic plasticity mechanical response.
Rivera, O. G.↗
A Method for Combining Experimentation and Molecular Dynamics Simulation to Improve Cohesive Zone Models for Metallic Microstructures
Fracture processes within a material begin at the nanometer length scale at which the formation, propagation, and interaction of fundamental damage mechanisms occur. Physics-based modeling of these atomic processes quickly becomes computationally intractable as the system size increases. Thus, a multiscale modeling method, based on the aggregation of fundamental damage processes occurring at the nanoscale within a cohesive zone model, is under development and will enable computationally feasible and physically meaningful microscale fracture simulation in polycrystalline metals. This method employs atomistic simulation to provide an optimization loop with an initial prediction of a cohesive zone model (CZM). This initial CZM is then applied at the crack front region within a finite element model. The optimization procedure iterates upon the CZM until the finite element model acceptably reproduces the near-crack-front displacement fields obtained from experimental observation. With this approach, a comparison can be made between the original CZM predicted by atomistic simulation and the converged CZM that is based on experimental observation. Comparison of the two CZMs gives insight into how atomistic simulation scales.
Hochhalter, J. D.↗
Thermomechanical Property Prediction of Amorphous and Crystal PEKK via Molecular Dynamics
Traditionally, advanced aerospace composites have been manufactured using thermoset resins. However, recently, thermoplastics have been investigated for use in the manufacturing of composite materials due to their unique manufacturing characteristics. Thermoplastic resins can be reshaped and formed, along with the added benefit of being recyclable, which thermoset resin cannot. Thermoplastic materials undergo a crystallization process during manufacturing which affects the percent crystallinity of the material. The crystallization needs to be understood better to maximize the potential of thermoplastic resins. PEKK is a thermoplastic material with good chemical, thermal, and mechanical loading resistance. PEKK is also a material NASA is interested in for developing new bonded joint technology. The crystalline microstructure of PEKK is at the micrometer length scale, and it is of interest to model the effects of the crystallinity structure on PEKK’s bulk properties. Molecular dynamics (MD) is a simulation tool that allows for property-structure relationships between atomistic structure and nanometer-length portions of a material. This makes MD a useful tool for developing the structure-property relationship of PEKK. However, the micrometer length scale of PEKK’s crystal structure is too large for MD. Thus, a hybrid approach to modeling PEKK’s microstructure is proposed in this work where MD models are built of both the amorphous and crystalline phases of PEKK. The engineering material properties can be obtained using MD at the nanometer length scale. A micromechanics approach can then generate the micrometer length scale of the crystallinity and the effective properties can be homogenized. The objective of this paper is to show the MD model workflow and the MD-predicted properties of PEKK. The properties can then be homogenized with different crystalline percentages to build design graphs that can be used to tailor PEKK for specific composite applications.
poly ether ketone ketone↗
The Galactic distribution of free electrons
Various radioastronomical measurements are synthesized here to derive a large-scale Galactic model for the distribution of free electrons and for microstructure in the distribution that seems to be caused by turbulence in the ISM. A two-component axisymmetric model fitted on all scales from 100 km to a few pc, accounts for most of the data. A population of dense, discrete clouds is also needed, and there is some evidence for spiral structure. The model allows better distance estimates for pulsars to be made. The implications of the model for the structure and ionization of the ISM and for the distribution of interstellar turbulence and the diffusion of cosmic rays are discussed.
Cordes, J. M.↗
Multiscale Thermal Conductivity Modeling of 3D Woven Composite Thermal Protection System Materials
Novel thermal protection systems (TPS) for re-entry and hypersonic vehicles utilize 3D woven composite materials with blended carbon filament tows (yarns) and a phenolic resin matrix. The presence of continuous carbon fibers provides superior stiffness and strength compared to legacy TPS, while the 3D weave pattern provides a great deal of design flexibility for both in-plane and through thickness thermomechanical behavior. However, modeling of 3D woven composites is notoriously complex and numerically intensive. The present investigation utilizes a unique ultra-efficient approach, known as Multiscale Recursive Micromechanics (MsRM), wherein recursive semi-analytical micromechanics methods are employed at various length scales within the composite. The MsRM approach has recently been extended to solve for the effective thermal conductivity as well as the local temperature and thermal flux fields throughout the composite. Presented model results focus on the impact of the microstructural geometry representation at each length scale on the material’s effective thermal conductivity, along with the local thermal flux and temperature fields induced in the microstructures, for a novel 3D woven TPS.
Composite↗
Stereological characterization of dry alpine snow for microwave remote sensing
A persistent problem in investigations of electromagnetic properties of snow, from reflectance at visible wavelengths to emission and backscattering in the microwave, has been the proper characterization of the snow's physical properties. It is suggested that the granular and laminar structure of snow can be measured in its aggregated state by stereology performed on sections prepared from snow specimens, and that these kinds of measurements can be incorporated into models of the electromagnetic properties. With careful sampling, anisotropy in the snow microstructure at various scales can be quantified. It is shown how stereological parameters can be averaged over orientation and optical depth for radiative transfer modeling.
Davis, Robert E.↗
ISS-Experiments of Columnar-to-Equiaxed Transition in Solidification Processing
The main topic of the research project CETSOL in the framework of the Microgravity Application Promotion (MAP) programme of the European Space Agency (ESA) is the investigation of the transition from columnar to equiaxed grain growth during solidification. Microgravity environment allows for suppression of buoyancy-driven melt flow and for growth of equiaxed grains free of sedimentation and buoyancy effects. This contribution will present first experimental results obtained in microgravity using hypo-eutectic AlSi alloys in the Materials Science Laboratory (MSL) on-board the International Space Station (ISS). The analysis of the experiments confirms the existence of a columnar to equiaxed transition, especially in the refined alloy. Temperature evolution and grain structure analysis provide critical values for the position, the temperature gradient and the solidification velocity at the columnar to equiaxed transition. These data will be used to improve modeling of solidification microstructures and grain structure on different lengths scales.
Sturz, Laszlo↗
Investigation of Micro-Scale Architectural Effects on Damage of Composites
This paper presents a three-dimensional, energy based, anisotropic, stiffness reduction, progressive damage model for composite materials and composite material constituents. The model has been implemented as a user-defined constitutive model within the Abaqus finite element software package and applied to simulate the nonlinear behavior of a damaging epoxy matrix within a unidirectional composite material. Three different composite microstructures were considered as finite element repeating unit cells, with appropriate periodicity conditions applied at the boundaries. Results representing predicted transverse tensile, longitudinal shear, and transverse shear stress-strain curves are presented, along with plots of the local fields indicating the damage progression within the microstructure. It is demonstrated that the damage model functions appropriately at the matrix scale, enabling localization of the damage to simulate failure of the composite material. The influence of the repeating unit cell geometry and the effect of the directionality of the applied loading are investigated and discussed.
damage↗
AI-Enhanced Computational Tools for Entry Systems Modeling
To advance the understanding of complex atmospheric entry phenomena, NASA’s Entry Systems Modeling (ESM) team [1] has developed high-fidelity computational tools addressing multiscale challenges, from material microstructures to full-scale heatshield response. This abstract highlights a subset of ESM tools, focusing on AI integration to enhance workflows and predictive modeling. - PuMA [2] computes effective material properties from high-resolution micro-CT scans, supporting TPS analysis for NASA missions. - TomoSAM [3] automates 3D tomography dataset segmentation for PuMA using the Segment Anything Model, reducing manual effort and improving accuracy. - PATO [4] models porous reactive materials under extreme conditions, with advancements such as unified solvers, mechanical erosion, and TPS coatings for NASA missions. - arcjetCV [5] employs deep learning to analyze arc jet test footage, measuring recession rates, shape changes, and shock standoff distances, bridging simulations, and experiments to reveal TPS ablation behavior. - ARCHeS [6] simulates arc heater plasma flows, modeling turbulence, radiation, and electromagnetic interactions to optimize arc heater performance, validate TPS under extreme conditions, and serve as a foundation for developing digital twins of arc heater facilities. - SPARTA [7] simulates rarefied hypersonic flows and gas-surface interactions for planetary entry missions, leveraging GPU architectures for scalable and efficient aerothermal and ablation analyses. AI-driven solutions, such as deep learning segmentation, have streamlined workflows in ESM tools and still hold significant potential to further accelerate processes and enhance automation in entry systems modeling. [1] Haskins, J.B. (2023), [2] Ferguson, J.C. (2018), [3] Meurisse, J.B.E. (2018), [4] Semeraro, F. (2023), [5] Quintart, A. (2024) [6] Meurisse, J.B.E. (2022), [7] Plimpton, S.J. (2019)
Predictive Modeling↗
Modeling and Characterization of Damage Processes in Metallic Materials
This paper describes a broad effort that is aimed at understanding the fundamental mechanisms of crack growth and using that understanding as a basis for designing materials and enabling predictions of fracture in materials and structures that have small characteristic dimensions. This area of research, herein referred to as Damage Science, emphasizes the length scale regimes of the nanoscale and the microscale for which analysis and characterization tools are being developed to predict the formation, propagation, and interaction of fundamental damage mechanisms. Examination of nanoscale processes requires atomistic and discrete dislocation plasticity simulations, while microscale processes can be examined using strain gradient plasticity, crystal plasticity and microstructure modeling methods. Concurrent and sequential multiscale modeling methods are being developed to analytically bridge between these length scales. Experimental methods for characterization and quantification of near-crack tip damage are also being developed. This paper focuses on several new methodologies in these areas and their application to understanding damage processes in polycrystalline metals. On-going and potential applications are also discussed.
Glaessgen, E. H.↗