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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Toucan: A performance portable, scalable implementation of the DECA algorithm

In the field of additive manufacturing (AM), cellular automata (CA) is extensively used to simulate microstructural evolution during solidification. However, while traditional CA approaches are relatively fast, they still require a substantial number of time steps, are limited to moderate volumes, and are relatively difficult to improve through parallelism due to the highly localized nature of the solidification front. Here, to address these issues of time to solution and load balancing, we introduce Toucan, a parallel, performance-portable, and scalable code written in C++ with the Kokkos library that leverages the discrete event inspired cellular automata (DECA) algorithm to perform parallel-in-time (PinT) grain growth simulations. Toucan effectively mitigates load balancing issues by distributing the computational workload more evenly across processors, enhancing scalability and efficiency. We conduct both strong and weak scaling studies on up to 64 GPUs on the Frontier supercomputer, demonstrating that Toucan significantly outperforms the current state-of-the-art, time-stepped CA code, ExaCA, on both single and multi-GPU simulations. Even in AM-specific weak scaling scenarios, Toucan maintains near-ideal scaling, in contrast to the linear increase observed with ExaCA due to the moving laser raster pattern. This study highlights Toucan’s potential to transform microstructural simulations in AM by radically improving both efficiency and scalability over existing methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Genetic programming for interpretable, data-driven continuum damage models.

The damage mechanisms that lead to failure in engineering alloys have been studied extensively, but converting this knowledge into constitutive models that are suitable for engineering-scale analysis remains a challenge. Evolution laws for continuum damage have been developed in the past and have proven effective but suffer from many non-physical assumptions that inhibit the overall accuracy of the model. Further, the assumptions inherent in these existing models prevent them from being applicable to a broad class of materials. At the same time, computational models of fine-scale damage mechanisms continue to advance making it tractable to generate large training data sets through computer simulation. Data-driven machine learning approaches can leverage these data sets to avoid making limiting assumptions, and instead produce models directly from the results of microstructural simulations and/or experiments. Many of these machine learning approaches are rapid and accurate, but they offer little to no insight into the underlying relationships among state variables being discovered. Conversely, genetic programming symbolic regression (GPSR) is a machine learning method that produces analytic expressions relating the state variables, allowing maximal insight and interpretability. To that end, we propose using GPSR as a data-driven method of obtaining microstructurally informed continuum damage models. Data is generated using microstructural simulations of damage evolution, parameterized over microstructural statistics (i.e., pore shape) and nominally applied deformations. Analytic expressions for damage evolution are obtained from the data using GPSR, and these expressions are then utilized within a continuum constitutive model. Overall, this approach is a promising method of automatically obtaining analytic relations describing constitutive phenomena in a material.

Buche, Michael Robert↗
NASA NTRS2024

Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi

Formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.

Gabriel Plummer↗
NASA NTRSDate not supplied

Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi

The formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.

Gabriel Plummer↗
NASA NTRSDate not supplied

Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi

Formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.

Gabriel Plummer↗
DOE OSTI2024

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↗
DOE OSTI2022

Dynamic Data-Driven Multiscale Modeling for Predicting Microstructure and Mechanical Properties of 316L Stainless Steel

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↗
NASA NTRS2010

Microstructure Modeling of Third Generation Disk Alloys

The objective of this program was to model, validate, and predict the precipitation microstructure evolution, using PrecipiCalc (QuesTek Innovations LLC) software, for 3rd generation Ni-based gas turbine disc superalloys during processing and service, with a set of logical and consistent experiments and characterizations. Furthermore, within this program, the originally research-oriented microstructure simulation tool was to be further improved and implemented to be a useful and user-friendly engineering tool. In this report, the key accomplishments achieved during the third year (2009) of the program are summarized. The activities of this year included: Further development of multistep precipitation simulation framework for gamma prime microstructure evolution during heat treatment; Calibration and validation of gamma prime microstructure modeling with supersolvus heat treated LSHR; Modeling of the microstructure evolution of the minor phases, particularly carbides, during isothermal aging, representing the long term microstructure stability during thermal exposure; and the implementation of software tools. During the research and development efforts to extend the precipitation microstructure modeling and prediction capability in this 3-year program, we identified a hurdle, related to slow gamma prime coarsening rate, with no satisfactory scientific explanation currently available. It is desirable to raise this issue to the Ni-based superalloys research community, with hope that in future there will be a mechanistic understanding and physics-based treatment to overcome the hurdle. In the mean time, an empirical correction factor was developed in this modeling effort to capture the experimental observations.

Jou, Herng-Jeng↗
DOE OSTI2026

Scaling kinetic Monte-Carlo simulations of grain growth with combined convolutional and graph neural networks

Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we suggest a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160 3 ), our method reduces memory usage and runtime in inference by 117× and 115×, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder’s ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. Training data are generated from stochastic grain growth simulations, providing realistic variability for learning robust microstructure evolution. Comprehensive system validation confirms that the model is accurate, robust, and scalable.

36 MATERIALS SCIENCE↗
NASA NTRS1989

A Monte Carlo-finite element model for strain energy controlled microstructural evolution - 'Rafting' in superalloys

This paper presents a specialized microstructural lattice model, MCFET (Monte Carlo finite element technique), which simulates microstructural evolution in materials in which strain energy has an important role in determining morphology. The model is capable of accounting for externally applied stress, surface tension, misfit, elastic inhomogeneity, elastic anisotropy, and arbitrary temperatures. The MCFET analysis was found to compare well with the results of analytical calculations of the equilibrium morphologies of isolated particles in an infinite matrix.

Gayda, J.↗
NASA NTRS1987

A microstructural lattice model for strain oriented problems: A combined Monte Carlo finite element technique

A specialized, microstructural lattice model, termed MCFET for combined Monte Carlo Finite Element Technique, was developed which simulates microstructural evolution in material systems where modulated phases occur and the directionality of the modulation is influenced by internal and external stresses. In this approach, the microstructure is discretized onto a fine lattice. Each element in the lattice is labelled in accordance with its microstructural identity. Diffusion of material at elevated temperatures is simulated by allowing exchanges of neighboring elements if the exchange lowers the total energy of the system. A Monte Carlo approach is used to select the exchange site while the change in energy associated with stress fields is computed using a finite element technique. The MCFET analysis was validated by comparing this approach with a closed form, analytical method for stress assisted, shape changes of a single particle in an infinite matrix. Sample MCFET analytical for multiparticle problems were also run and in general the resulting microstructural changes associated with the application of an external stress are similar to that observed in Ni-Al-Cr alloys at elevated temperature.

Gayda, J.↗
DOE OSTI2025

Implementation of disruptive designs for gas turbine components using direct energy deposition additive manufacturing

This research aims to develop a framework for establishing the correlation between in-situ monitoring data, process parameters, and microstructure evolution in blown-powder laser-directed energy deposition (DED) additive manufacturing (AM). To achieve this, a comprehensive manufacturing framework has been developed, spanning from in-situ data acquisition, melt-pool simulation, microstructure modeling, and statistical microstructure quantification. A machine learning-based surrogate model is constructed to predict melt pool geometry directly from in-situ coaxial camera data. The surrogate model is trained using outputs from a high-fidelity melt pool simulation, which provides accurate melt pool dimension data under varying process conditions. The predicted melt pool geometry is then used as input to a microstructure model to predict microstructural features. To rigorously compare and analyze microstructures, the project introduces statistical metrics that quantify differences based on key features such as morphology and texture. Microstructures are represented using advanced statistical descriptors including angular chord length distribution, two-point spatial statistics, orientation distribution function, and global spherical harmonic. These representations are used to compute four distinct “dissimilarity scores” that quantitatively capture differences in texture and morphology. This framework is demonstrated to enable automated calibration of simulation parameters by minimizing discrepancies between simulated and target microstructures. The technology developed in this project enables direct correlation between in-situ monitoring data and resulting microstructure, paving the way for adaptive microstructure control in metal AM. This capability strengthens the connection between process parameters and final material properties, facilitating more precise and reliable material design.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Simultaneous Development and Robust Optimization of a Microstructure Dependent Material

Recent microstructure characterization techniques combined with Symbolic Regression(SR)analysis has been proven to generate white box plasticity models well suited for incorporation into FEA software.The current work builds upon those efforts and demonstrates the applicability of Sequential Monte-Carlo (SMC) methods within SR analysis to condense model development and robust optimization into a single, co-dependent process. In this project, SMC methods provide a mechanism through which the observed microstructure features and associated variability can be incorporated into the discovery phase of model development and simultaneously recover approximate parameter distributions through SR analysis. The demonstration utilized a data set consisting of tensile test results from a limited number of sample specimens with corresponding EBSD data from which microstructure features were characterized.The maximum threshold stress model in the Visco-Plastic Self-Consistent (VPSC) code developed by Los Alamos National Laboratories was calibrated using mechanical test data.Synthetic volume elements with statistically equivalent microstructure were generated with DREAM3Dbased on the observed EBSD data. VPSC was used to simulate the corresponding tensile test response for each of the synthetic volume elements. The simulated microstructure and tensile test data was used astraining datafor SMC-SR algorithm and the resulting model was validated with data from the original empirical data set.

Karl Garbrecht↗
DOE OSTI2022

Understanding Phase and Interfacial Effects of Spall Fracture in Additively Manufactured Ti-5Al-5V-5Mo-3Cr

Additive manufactured Ti-5Al-5V-5Mo-3Cr (Ti-5553) is being considered as an AM repair material for engineering applications because of its superior strength properties compared to other titanium alloys. Here, we describe the failure mechanisms observed through computed tomography, electron backscatter diffraction (EBSD), and scanning electron microscopy (SEM) of spall damage as a result of tensile failure in as-built and annealed Ti-5553. We also investigate the phase stability in native powder, as-built and annealed Ti-5553 through diamond anvil cell (DAC) and ramp compression experiments. We then explore the effect of tensile loading on a sample containing an interface between a Ti-6Al-V4 (Ti-64) baseplate and additively manufactured Ti-5553 layer. Post-mortem materials characterization showed spallation occurred in regions of initial porosity and the interface provides a nucleation site for spall damage below the spall strength of Ti-5553. Preliminary peridynamics modeling of the dynamic experiments is described. Finally, we discuss further development of Stochastic Parallel PARticle Kinteic Simulator (SPPARKS) Monte Carlo (MC) capabilities to include the integration of alpha (α)-phase and microstructural simulations for this multiphase titanium alloy.

36 MATERIALS SCIENCE↗