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Title: Physics-Informed Machine Learning with Application to Solid Oxide Fuel Cell System Modeling and Optimization

Abstract

Traditional data-driven machine learning (ML) techniques were combined with the physics-based SOFC-MP model toward improving SOFC system-level performance prediction. Four different physics-informed ML methods were proposed to demonstrate better prediction accuracy compared with traditional ML methods. First, it was preferred that the existing Kriging-based surrogate model not be entirely abandoned, so errors between Kriging regression prediction and true solution are learned using ML. The goal was to find the pattern of the error distribution as a function of model input so discrepancies between the Kriging prediction and true solution could be reduced by adding the additional ML-trained error term to the existing prediction. Second, deep neural networks (DNN) coupled with the mass balance model (MBM) have been developed to significantly decrease the reduced order model (ROM) prediction error with fewer training data compared to the traditional Kriging-based ROM and traditional DNN regression approaches. Third, DNN regression coupled with a neural networks (NN) classifier and MBM was developed. It provides superior performance on classifying the physically operational conditions for natural gas fuel cell (NGFC) than traditional classification approaches. Finally, two kinds of DNN transferring were studied and tested. One was transferring DNN for low-fidelity model data to a high-fidelity model. Themore » second one was transferring DNN from state-of-the-art NGFC to advanced NGFC. These physic-informed ML frameworks can serve as a solid foundation for estimating U.S. Department of Energy-targeted fuel cell performance and exhibit great potential to be employed to other engineering applications.« less

Authors:
 [1];  [1]; ORCiD logo [1]; ORCiD logo [1]
  1. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1569289
Report Number(s):
PNNL-29124
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Bao, Jie, Wang, Chao, Xu, Zhijie, and Koeppel, Brian J. Physics-Informed Machine Learning with Application to Solid Oxide Fuel Cell System Modeling and Optimization. United States: N. p., 2019. Web. doi:10.2172/1569289.
Bao, Jie, Wang, Chao, Xu, Zhijie, & Koeppel, Brian J. Physics-Informed Machine Learning with Application to Solid Oxide Fuel Cell System Modeling and Optimization. United States. https://doi.org/10.2172/1569289
Bao, Jie, Wang, Chao, Xu, Zhijie, and Koeppel, Brian J. Wed . "Physics-Informed Machine Learning with Application to Solid Oxide Fuel Cell System Modeling and Optimization". United States. https://doi.org/10.2172/1569289. https://www.osti.gov/servlets/purl/1569289.
@article{osti_1569289,
title = {Physics-Informed Machine Learning with Application to Solid Oxide Fuel Cell System Modeling and Optimization},
author = {Bao, Jie and Wang, Chao and Xu, Zhijie and Koeppel, Brian J.},
abstractNote = {Traditional data-driven machine learning (ML) techniques were combined with the physics-based SOFC-MP model toward improving SOFC system-level performance prediction. Four different physics-informed ML methods were proposed to demonstrate better prediction accuracy compared with traditional ML methods. First, it was preferred that the existing Kriging-based surrogate model not be entirely abandoned, so errors between Kriging regression prediction and true solution are learned using ML. The goal was to find the pattern of the error distribution as a function of model input so discrepancies between the Kriging prediction and true solution could be reduced by adding the additional ML-trained error term to the existing prediction. Second, deep neural networks (DNN) coupled with the mass balance model (MBM) have been developed to significantly decrease the reduced order model (ROM) prediction error with fewer training data compared to the traditional Kriging-based ROM and traditional DNN regression approaches. Third, DNN regression coupled with a neural networks (NN) classifier and MBM was developed. It provides superior performance on classifying the physically operational conditions for natural gas fuel cell (NGFC) than traditional classification approaches. Finally, two kinds of DNN transferring were studied and tested. One was transferring DNN for low-fidelity model data to a high-fidelity model. The second one was transferring DNN from state-of-the-art NGFC to advanced NGFC. These physic-informed ML frameworks can serve as a solid foundation for estimating U.S. Department of Energy-targeted fuel cell performance and exhibit great potential to be employed to other engineering applications.},
doi = {10.2172/1569289},
url = {https://www.osti.gov/biblio/1569289}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}