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Title: Quantifying uncertainty in the process-structure relationship for Al–Cu solidification

Abstract

Phase field method (PFM) is a simulation tool to predict microstructural evolution during solidification and is helpful to establish the process-structure relationship for alloys. The robustness of the relationship however is affected by model-form and parameter uncertainties in PFM. In this paper, the uncertainty associated with the thermodynamic and process parameters of PFM is studied and quantified. Surrogate modeling is used to interpolate four quantities of interests (QoIs), including dendritic perimeter, area, primary arm length, and solute segregation, as functions of thermodynamic and process parameters. A sparse grid approach is applied to mitigate the curse-of-dimensionality computational burden in uncertainty quantification. Polynomial chaos expansion is employed to obtain the probability density functions of the QoIs. The effect of parameter uncertainty on the Al–Cu dendritic growth during solidification simulation are investigated. Here, the results show that the dendritic morphology varies significantly with respect to the interface mobility and the initial temperature.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]
  1. Georgia Inst. of Technology, Atlanta, GA (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1559610
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Modelling and Simulation in Materials Science and Engineering
Additional Journal Information:
Journal Volume: 27; Journal Issue: 6; Journal ID: ISSN 0965-0393
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; uncertainty quantification; phasefield method; solidification; sparse grid; polynomial chaos expansion

Citation Formats

Tran, Anh, Liu, Dehao, Tran, Hoang A., and Wang, Yan. Quantifying uncertainty in the process-structure relationship for Al–Cu solidification. United States: N. p., 2019. Web. doi:10.1088/1361-651X/ab2690.
Tran, Anh, Liu, Dehao, Tran, Hoang A., & Wang, Yan. Quantifying uncertainty in the process-structure relationship for Al–Cu solidification. United States. doi:10.1088/1361-651X/ab2690.
Tran, Anh, Liu, Dehao, Tran, Hoang A., and Wang, Yan. Wed . "Quantifying uncertainty in the process-structure relationship for Al–Cu solidification". United States. doi:10.1088/1361-651X/ab2690.
@article{osti_1559610,
title = {Quantifying uncertainty in the process-structure relationship for Al–Cu solidification},
author = {Tran, Anh and Liu, Dehao and Tran, Hoang A. and Wang, Yan},
abstractNote = {Phase field method (PFM) is a simulation tool to predict microstructural evolution during solidification and is helpful to establish the process-structure relationship for alloys. The robustness of the relationship however is affected by model-form and parameter uncertainties in PFM. In this paper, the uncertainty associated with the thermodynamic and process parameters of PFM is studied and quantified. Surrogate modeling is used to interpolate four quantities of interests (QoIs), including dendritic perimeter, area, primary arm length, and solute segregation, as functions of thermodynamic and process parameters. A sparse grid approach is applied to mitigate the curse-of-dimensionality computational burden in uncertainty quantification. Polynomial chaos expansion is employed to obtain the probability density functions of the QoIs. The effect of parameter uncertainty on the Al–Cu dendritic growth during solidification simulation are investigated. Here, the results show that the dendritic morphology varies significantly with respect to the interface mobility and the initial temperature.},
doi = {10.1088/1361-651X/ab2690},
journal = {Modelling and Simulation in Materials Science and Engineering},
number = 6,
volume = 27,
place = {United States},
year = {2019},
month = {6}
}

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