Reduced-order model for microstructure evolution prediction in the electrodes of solid oxide fuel cell with dynamic discrepancy reduced modeling

Lei, Yinkai; Cheng, Tian-Le; Mebane, David S.; Wen, You-Hai

doi:10.1016/j.jpowsour.2019.01.046

Title: Reduced-order model for microstructure evolution prediction in the electrodes of solid oxide fuel cell with dynamic discrepancy reduced modeling

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Abstract

Microstructure evolution in the electrodes of solid oxide fuel cell is an important degradation mechanism which reduces active sites for redox reaction and the electric conductivity. Phase field models for microstructure evolution simulation are usually expensive for large scale simulations. In this paper, a reduced-order coarsening model is developed using dynamic discrepancy reduced modeling, which reduces the model order by inserting Gaussian process stochastic functions into the dynamic equations of Ostwald ripening. The reduced order model has been calibrated on a dataset generated by a phase field model that has been well validated to experiments. A validating dataset has also been generated with which the model prediction show good agreement. This model is further applied to predict long term microstructure evolution in different SOFC electrodes. Finally, this work is the first attempt of building a degradation model of SOFC using data science techniques.

Authors:

^[1]; Cheng, Tian-Le ^[2]; Mebane, David S. ^[3]; Wen, You-Hai ^[4]

National Energy Technology Lab. (NETL), Albany, OR (United States); Oak Ridge Institute for Science and Education, Oak Ridge, TN (United States)
National Energy Technology Lab. (NETL), Albany, OR (United States); AECOM, South Park, PA (United States)
West Virginia Univ., Morgantown, WV (United States)
National Energy Technology Lab. (NETL), Albany, OR (United States)

Publication Date:: Fri Feb 01 00:00:00 EST 2019

Research Org.:: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)

Sponsoring Org.:: USDOE Office of Fossil Energy (FE)

OSTI Identifier:: 1532662

Alternate Identifier(s):: OSTI ID: 1636570

Report Number(s):: NETL-PUB-22082
Journal ID: ISSN 0378-7753

Grant/Contract Number:: FE0004000

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Power Sources

Additional Journal Information:: Journal Volume: 416; Journal Issue: C; Journal ID: ISSN 0378-7753

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 36 MATERIALS SCIENCE; 42 ENGINEERING; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; SOFC degradation; Microstructure evolution; Reduced order model; Dynamic discrepancy reduced modeling; Bayesian calibration; Phase-field simulation

Citation Formats


                    Lei, Yinkai, Cheng, Tian-Le, Mebane, David S., and Wen, You-Hai. Reduced-order model for microstructure evolution prediction in the electrodes of solid oxide fuel cell with dynamic discrepancy reduced modeling.  United States: N. p., 2019. 
Web.  doi:10.1016/j.jpowsour.2019.01.046.

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                    Lei, Yinkai, Cheng, Tian-Le, Mebane, David S., & Wen, You-Hai. Reduced-order model for microstructure evolution prediction in the electrodes of solid oxide fuel cell with dynamic discrepancy reduced modeling.  United States.  https://doi.org/10.1016/j.jpowsour.2019.01.046

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                    Lei, Yinkai, Cheng, Tian-Le, Mebane, David S., and Wen, You-Hai. Fri .  
"Reduced-order model for microstructure evolution prediction in the electrodes of solid oxide fuel cell with dynamic discrepancy reduced modeling".  United States.  https://doi.org/10.1016/j.jpowsour.2019.01.046.  https://www.osti.gov/servlets/purl/1532662.

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@article{osti_1532662,

  title        = {Reduced-order model for microstructure evolution prediction in the electrodes of solid oxide fuel cell with dynamic discrepancy reduced modeling},

  author       = {Lei, Yinkai and Cheng, Tian-Le and Mebane, David S. and Wen, You-Hai},

  abstractNote = {Microstructure evolution in the electrodes of solid oxide fuel cell is an important degradation mechanism which reduces active sites for redox reaction and the electric conductivity. Phase field models for microstructure evolution simulation are usually expensive for large scale simulations. In this paper, a reduced-order coarsening model is developed using dynamic discrepancy reduced modeling, which reduces the model order by inserting Gaussian process stochastic functions into the dynamic equations of Ostwald ripening. The reduced order model has been calibrated on a dataset generated by a phase field model that has been well validated to experiments. A validating dataset has also been generated with which the model prediction show good agreement. This model is further applied to predict long term microstructure evolution in different SOFC electrodes. Finally, this work is the first attempt of building a degradation model of SOFC using data science techniques.},

  doi          = {10.1016/j.jpowsour.2019.01.046},

  journal      = {Journal of Power Sources},

  number       = C,

  volume       = 416,

  place        = {United States},

  year         = {Fri Feb 01 00:00:00 EST 2019},

  month        = {Fri Feb 01 00:00:00 EST 2019}

}

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