Bayesian Inference of Atomic Diffusivity in a Binary Ni/Al System Based on Molecular Dynamics
This work focuses on characterizing the integral features of atomic diffusion in Ni/Al nanolaminates based on molecular dynamics (MD) computations. Attention is focused on the simplified problem of extracting the diffusivity, D, in an isothermal system at high temperature. To this end, a mixing m...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Society for Industrial and Applied Mathematics
2011
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| Online Access: | http://hdl.handle.net/1721.1/65861 https://orcid.org/0000-0001-8242-3290 |
| Summary: | This work focuses on characterizing the integral features of atomic diffusion in Ni/Al
nanolaminates based on molecular dynamics (MD) computations. Attention is focused on the simplified
problem of extracting the diffusivity, D, in an isothermal system at high temperature. To this
end, a mixing measure theory is developed that relies on analyzing the moments of the cumulative
distribution functions (CDFs) of the constituents. The mixing measures obtained from replica simulations
are exploited in a Bayesian inference framework, based on contrasting these measures with
corresponding moments of a dimensionless concentration evolving according to a Fickian process.
The noise inherent in the MD simulations is described as a Gaussian process, and this hypothesis is
verified both a priori and using a posterior predictive check. Computed values of D for an initially
unmixed system rapidly heated to 1500 K are found to be consistent with experimental correlation
for diffusion of Ni into molten Al. On the contrary, large discrepancies with experimental predictions
are observed when D is estimated based on large-time mean-square displacement (MSD) analysis,
and when it is evaluated using the Arrhenius correlation calibrated against experimental measurements
of self-propagating front velocities. Implications are finally drawn regarding extension of the
present work and potential refinement of continuum modeling approaches. |
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