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Uncertainty Propagation in a Multiscale CALPHAD-Reinforced Elastochemical Phase-Field Model

22 Pages Posted: 31 Jul 2019 Publication Status: Accepted

See all articles by Vahid Attari

Vahid Attari

Texas A&M University - Department of Materials Science and Engineering

Pejman Honarmandi

Texas A&M University - Department of Materials Science and Engineering

Thien Duong

Texas A&M University - Department of Mechanical Engineering

Daniel J. Sauceda

Texas A&M University - Department of Materials Science and Engineering

Douglas Allaire

Texas A&M University - Department of Mechanical Engineering

Raymundo Arroyave

Texas A&M University - Department of Materials Science and Engineering; Texas A&M University - Center for intelligent Multifunctional Materials and Structures; Texas A&M University - Department of Mechanical Engineering

Abstract

ICME approaches provide decision support for materials design by establishing quantitative process-structure-property relations. Confidence in the decision support, however, must be achieved by establishing uncertainty bounds in ICME model chains. The quantification and propagation of uncertainty in computational materials science, however, remains a rather unexplored aspect of computational materials science approaches. Moreover, traditional uncertainty propagation frameworks tend to be limited in cases with computationally expensive simulations. A rather common and important model chain is that of CALPHAD-based thermodynamic models of phase stability coupled to phase field models for microstructure evolution. Propagation of uncertainty in these cases is challenging not only due to the sheer computational cost of the simulations but also because of the high dimensionality of the input space. In this work, we present a framework for the quantification and propagation of uncertainty in a CALPHAD-based elasto-chemical phase field model. We motivate our work by investigating the microstructure evolution in Mg2(SixSn1−x) thermoelectric materials. We first carry out a Markov Chain Monte Carlo-based inference of the CALPHAD model parameters for this pseudobinary system and then use advanced sampling schemes to propagate uncertainties across a high-dimensional simulation input space. Through high-throughput phase field simulations we generate 200,000 time series of synthetic microstructures and use machine learning approaches to understand the effects of propagated uncertainties on the microstructure landscape of the system under study. The microstructure dataset has been curated in the Open Phase-field Microstructure Database (OPMD), available at http://microstructures.net.

Keywords: Phase-field modeling, Uncertainty propagation, Uncertainty quantification, Thermoelectrics, Microstructure, Mass scattering, Phonon scattering

Suggested Citation

Attari, Vahid and Honarmandi, Pejman and Duong, Thien and Sauceda, Daniel J. and Allaire, Douglas and Arroyave, Raymundo, Uncertainty Propagation in a Multiscale CALPHAD-Reinforced Elastochemical Phase-Field Model (2019). Available at SSRN: https://ssrn.com/abstract=3427526 or http://dx.doi.org/10.2139/ssrn.3427526

Vahid Attari (Contact Author)

Texas A&M University - Department of Materials Science and Engineering ( email )

Langford Building A
798 Ross St.
College Station, TX 77843-3137
United States

Pejman Honarmandi

Texas A&M University - Department of Materials Science and Engineering

Langford Building A
798 Ross St.
College Station, TX 77843-3137
United States

Thien Duong

Texas A&M University - Department of Mechanical Engineering

Langford Building A
798 Ross St.
College Station, TX 77843-3137
United States

Daniel J. Sauceda

Texas A&M University - Department of Materials Science and Engineering

Langford Building A
798 Ross St.
College Station, TX 77843-3137
United States

Douglas Allaire

Texas A&M University - Department of Mechanical Engineering

Langford Building A
798 Ross St.
College Station, TX 77843-3137
United States

Raymundo Arroyave

Texas A&M University - Department of Materials Science and Engineering

Langford Building A
798 Ross St.
College Station, TX 77843-3137
United States

Texas A&M University - Center for intelligent Multifunctional Materials and Structures

7607 Eastmark Dr
College Station, TX 77840
United States

Texas A&M University - Department of Mechanical Engineering

Langford Building A
798 Ross St.
College Station, TX 77843-3137
United States

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