Engineering topics

Yadav, Vishal

Publications and source records attributed to Yadav, Vishal.

3 records

DOE OSTI2024

Anisotropic physics-regularized interpretable machine learning of microstructure evolution

Anisotropic Physics-Regularized Interpretable Machine Learning Microstructure Evolution (APRIMME) is a general-purpose machine learning solution for grain growth simulations. In prior work, PRIMME employed a deep neural network to predict site-specific migration as a function of its neighboring sites to model normal, isotropic, grain growth behavior. This work aims to extend this method by incorporating grain boundary misorientation-based grain growth behavior. APRIMME is trained on anisotropic simulations created using the Monte Carlo-Potts (MCP) model. Furthermore, the results of this work are compared statistically using grain radius, number of sides per grain, mean neighborhood misorientations, and the standard deviation of triple junction dihedral angles, and are found to match in most cases. The exceptions are small and seem to be related to two causes: (1) the deterministic model of APRIMME is learning from the stochastic simulations of MCP, which seems to accentuate triple junction behaviors; and, (2) a bias against very small grains is made evident in a quicker decrease in grains than expected at the beginning of an APRIMME simulation. APRIMME is also evaluated for its general ability to capture anisotropic grain growth behavior by first investigating different test case initial conditions, including a circle grain, three grain, and hexagonal grain microstructures.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A triple junction energy study using an inclination-dependent anisotropic Monte Carlo Potts grain growth model

This work presents a Monte Carlo Potts grain growth model in which the grain boundary (GB) energies depend on the GB inclination. The inclination is calculated using a linear smoothing approach developed by the authors. In bicrystal simulations with a shrinking grain, the grain changes shape to prefer low energy GB inclinations. However, in polycrystal simulations the preferred inclinations depend on the approach used to assign the triple junction (TJ) energies. Approaches that produce unimodal TJ energy distributions result in the expected behavior of preferring low energy GB inclinations. However, approaches that produce bimodal TJ energy distributions result in medium energy or even high energy inclinations being preferred. Overall, this study underscores the importance of TJs in anisotropic grain growth.

Grain boundary inclination↗

DOE OSTI2023

A new efficient grain growth model using a random Gaussian-sampled mode filter

This paper presents the use of a Gaussian neighborhood mode filter for predicting grain growth in a manner similar to the solutions obtained by a Monte Carlo Potts model. This flexible grain growth model can quickly utilize modern, computationally optimized data science strategies on graphics processing units to simulate grain growth up to 100 times faster than the state-of-the-art, publicly available Monte Carlo Potts model. We show that, given the correct neighborhood, the mode filter can replicate normal grain growth in two or three dimensions. In addition, the paper briefly demonstrates the ability to model limited anisotropic in grain boundary energy and mobility. Anisotropic grain boundary energy is modeled by defining a weighted mode filter operation. Anisotropic grain boundary mobility is modeled by scaling and orienting the Gaussian neighborhood in a particular direction.

Anisotropy↗