Fast three-dimensional rules-based simulation of thermal-sprayed microstructures

Rodgers, Theron M.; Mitchell, John A.; Olson, Aaron; Bolintineanu, Dan S.; Vackel, Andrew; Moore, Nathan W.

doi:10.1016/j.commatsci.2021.110437

Fast three-dimensional rules-based simulation of thermal-sprayed microstructures

Journal Article · 15 June 2021 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2021.110437· OSTI ID:1775189

Rodgers, Theron M. ^[1]; Mitchell, John A. ^[1]; Olson, Aaron ^[1]; Bolintineanu, Dan S. ^[1]; Vackel, Andrew ^[1]; Moore, Nathan W. ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Thermal spray processes involve the repeated impact of millions of discrete particles, whose melting, deformation, and coating-formation dynamics occur at microsecond timescales. The accumulated coating that evolves over minutes is comprised of complex, multiphase microstructures, and the timescale difference between the individual particle solidification and the overall coating formation represents a significant challenge for analysts attempting to simulate microstructure evolution. In order to overcome the computational burden, researchers have created rule-based models (similar to cellular automata methods) that do not directly simulate the physics of the process. Instead, the simulation is governed by a set of predefined rules, which do not capture the fine-details of the evolution, but do provide a useful approximation for the simulation of coating microstructures. Here, we introduce a new rules-based process model for microstructure formation during thermal spray processes. The model is 3D, allows for an arbitrary number of material types, and includes multiple porosity-generation mechanisms. Example results of the model for tantalum coatings are presented along with sensitivity analyses of model parameters and validation against 3D experimental data. The model’s computational efficiency allows for investigations into the stochastic variation of coating microstructures, in addition to the typical process-to-structure relationships.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC04-94AL85000; NA0003525

OSTI ID:: 1775189

Alternate ID(s):: OSTI ID: 1781556

Report Number(s):: SAND-2021-3494J; 695012

Journal Information:: Computational Materials Science, Vol. 194; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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