Bayesian strategies for uncertainty quantification of the thermodynamic properties of materials

doi:10.1016/j.ijengsci.2019.05.011

This content will become publicly available on June 13, 2020

Title: Bayesian strategies for uncertainty quantification of the thermodynamic properties of materials

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Abstract

Reliable models of the thermodynamic properties of materials are critical for industrially relevant applications that require a good understanding of equilibrium phase diagrams, thermal and chemical transport, and microstructure evolution. The goal of thermodynamic models is to capture data from both experimental and computational studies and then make reliable predictions when extrapolating to new regions of parameter space. These predictions will be impacted by artifacts present in real data sets such as outliers, systematic errors, and unreliable or missing uncertainty bounds. Such issues increase the probability of the thermodynamic model producing erroneous predictions. We present a Bayesian framework for the selection, calibration, and quantification of uncertainty of thermodynamic property models. The modular framework addresses numerous concerns regarding thermodynamic models including thermodynamic consistency, robustness to outliers, and systematic errors by the use of hyperparameter weightings and robust Likelihood and Prior distribution choices. Furthermore, the framework’s inherent transparency (e.g. our choice of probability functions and associated parameters) enables insights into the complex process of thermodynamic assessment. We introduce these concepts through examples where the true property model is known. Additionally, we demonstrate the utility of the framework through the creation of a property model from a large set of experimental specific heatmore »« less

Authors:

^[1]; Jennings, Elise ^[1]; Stan, Marius ^[1]

Argonne National Lab. (ANL), Lemont, IL (United States)

Publication Date:: 2019-06-13

Research Org.:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Org.:: National Institute of Standards and Technology (NIST), Center for Hierarchical Materials Design (CHiMaD); USDOE

OSTI Identifier:: 1526026

Grant/Contract Number:: AC02-06CH11357

Resource Type:: Accepted Manuscript

Journal Name:: International Journal of Engineering Science

Additional Journal Information:: Journal Volume: 142; Journal Issue: C; Journal ID: ISSN 0020-7225

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: Bayesian statistics; CALPHAD; Hafnium; thermodynamic property models; uncertainty quantification

Citation Formats


                    Paulson, Noah H., Jennings, Elise, and Stan, Marius. Bayesian strategies for uncertainty quantification of the thermodynamic properties of materials.  United States: N. p., 2019. 
        Web.  doi:10.1016/j.ijengsci.2019.05.011.

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                    Paulson, Noah H., Jennings, Elise, & Stan, Marius. Bayesian strategies for uncertainty quantification of the thermodynamic properties of materials.  United States.  doi:10.1016/j.ijengsci.2019.05.011.

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                    Paulson, Noah H., Jennings, Elise, and Stan, Marius. Thu .  
        "Bayesian strategies for uncertainty quantification of the thermodynamic properties of materials".  United States.  doi:10.1016/j.ijengsci.2019.05.011.

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@article{osti_1526026,

  title        = {Bayesian strategies for uncertainty quantification of the thermodynamic properties of materials},

  author       = {Paulson, Noah H. and Jennings, Elise and Stan, Marius},

  abstractNote = {Reliable models of the thermodynamic properties of materials are critical for industrially relevant applications that require a good understanding of equilibrium phase diagrams, thermal and chemical transport, and microstructure evolution. The goal of thermodynamic models is to capture data from both experimental and computational studies and then make reliable predictions when extrapolating to new regions of parameter space. These predictions will be impacted by artifacts present in real data sets such as outliers, systematic errors, and unreliable or missing uncertainty bounds. Such issues increase the probability of the thermodynamic model producing erroneous predictions. We present a Bayesian framework for the selection, calibration, and quantification of uncertainty of thermodynamic property models. The modular framework addresses numerous concerns regarding thermodynamic models including thermodynamic consistency, robustness to outliers, and systematic errors by the use of hyperparameter weightings and robust Likelihood and Prior distribution choices. Furthermore, the framework’s inherent transparency (e.g. our choice of probability functions and associated parameters) enables insights into the complex process of thermodynamic assessment. We introduce these concepts through examples where the true property model is known. Additionally, we demonstrate the utility of the framework through the creation of a property model from a large set of experimental specific heat and enthalpy measurements of Hafnium metal from 0 to 4900K.},

  doi          = {10.1016/j.ijengsci.2019.05.011},

  journal      = {International Journal of Engineering Science},

  number       = C,

  volume       = 142,

  place        = {United States},

  year         = {2019},

  month        = {6}

}

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