@article{2c6605dcc02a4bd48d67140254a25623,
title = "Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System",
abstract = "Uncertainties from model parameters and model discrepancy from small-scale models impact the accuracy and reliability of predictions of large-scale systems. Inadequate representation of these uncertainties may result in inaccurate and overconfident predictions during scale-up to larger systems. Hence, multiscale modeling efforts must accurately quantify the effect of the propagation of uncertainties during upscaling. Using a Bayesian approach, we calibrate a small-scale solid sorbent model to thermogravimetric (TGA) data on a functional profile using chemistry-based priors. Crucial to this effort is the representation of model discrepancy, which uses a Bayesian smoothing splines (BSS-ANOVA) framework. Our uncertainty quantification (UQ) approach could be considered intrusive as it includes the discrepancy function within the chemical rate expressions; resulting in a set of stochastic differential equations. Such an approach allows for easily propagating uncertainty by propagating the joint model parameter and discrepancy posterior into the larger-scale system of rate expressions. The broad UQ framework presented here could be applicable to virtually all areas of science where multiscale modeling is used. Supplementary materials for this article are available online.",
keywords = "BSS-ANOVA, Bayesian hierarchical modeling, Computer model calibration, Extrapolation, Functional data, Propagation of uncertainty",
author = "Bhat, {K. Sham} and Mebane, {David S.} and Priyadarshi Mahapatra and Storlie, {Curtis B.}",
note = "Funding Information: This work was partially supported by the Carbon Capture Simulation Initiative. The authors also thank Andrew Lee and Dan Fauth for helping to develop the sorbent model and providing us the TGA data, Brian Logdson for the extracting the snippets used in this article from the original TGA data, Joel Kress for ab initio calculations of quantum chemistry data used for priors of model parameters, Brenda Ng and others at LLNL and LBNL for helping us operate the turbine gateway to set up and run the process model, and Dan Fauth and Mac Gray for relevant figures. Funding for this work was provided by the Office of Fossil Energy, US Department of Energy through the Carbon Capture Simulation Initiative. This article was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Publisher Copyright: {\textcopyright} 2017 American Statistical Association.",
year = "2017",
month = oct,
day = "2",
doi = "10.1080/01621459.2017.1295863",
language = "English (US)",
volume = "112",
pages = "1453--1467",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor and Francis Ltd.",
number = "520",
}