Engineering topics

Corbey, Jordan F.

Publications and source records attributed to Corbey, Jordan F..

3 records

DOE OSTI2024

Deep operator network surrogate for phase-field modeling of metal grain growth during solidification

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° C / min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Ligand–Metal Complementarity in Rare-Earth and Actinide Chemistry

In this article, the rare-earths are comprised of scandium, yttrium, and the lanthanides (atomic numbers 21, 39, and 57–71). (1) While these elements are essential to modern life, they were historically characterized as simple, perhaps dull, compared to the rich multielectron chemistry of d-block metals in catalysts and enzymes. These descriptions were quite fair and largely true at the time that these elements were being discovered and the first compounds containing them were being synthesized, although much progress has been made to overturn this view of the rare earths. Similarly, discussions about actinide chemistry are sometimes limited to research pertinent to the nuclear industry, which, while comprising technologies vital to modern society, does not celebrate the fascinating chemical space that much of the actinide series occupies. These historic characterizations of most of the rare-earth and actinide elements persist in some textbooks, and we acknowledge that it is a helpful place to start; however, altogether these series comprise 32 elements-27% of the 118 known by 2024-and we suggest it would be an unfortunate twist of physics if such a large proportion of the Periodic Table were genuinely dull. Indeed, as this Inorganic Chemistry Forum Issue “Ligand-Metal Complementarity in Rare-Earth and Actinide Chemistry” highlights, the chemistry-let alone the physics, that is, spectroscopy and magnetism-of these elements is rich and depends on ligands!

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Visualizing Uranium Crystallization from Melt: Experiment-Informed Phase Field Modeling and Machine Learning

The focus of this project was to observe and simulate the solidification of uranium metal at the crystallographic level from its molten state. Melting experiments were conducted at two different scales to observe microstructural evolution using either a laboratory-scale induction furnace (hundreds of grams of metal) or a microscope heating stage (hundreds of milligrams of metal), respectively. Experimental parameters and characterization data were then used to inform a phase field model of gamma-U crystal growth as dendrites with or without secondary phase impurities in the form of uranium carbide particles. Finally, training datasets were generated by the phase field model as inputs to a neural network, developed with the aim of providing a faster, cheaper surrogate model for microstructural simulations within a given parameter space. Progress is reported herein for each of these task areas. Ultimately, 1) an optical microscope heating stage capability has been stood-up for uranium metal solidification studies, 2) a phase field model was advanced to simulate multiple uranium grains growing in the presence of carbide impurity particles and 3) a neural network was constructed and optimized to predict the microstructure features of individually growing uranium crystals.

36 MATERIALS SCIENCE↗