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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. In conclusion, 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.

Authors:
ORCiD logo [1];  [2];  [3];  [4]
+ Show Author Affiliations
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. West Virginia Univ., Morgantown, WV (United States). Dept. of Mechanical and Aerospace Engineering
  3. National Energy Technology Lab., Morgantown, WV (United States)
  4. Mayo Clinic, Rochester, MN (United States)
Publication Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1571589
Report Number(s):
LA-UR-14-26061
Journal ID: ISSN 0162-1459
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Journal of the American Statistical Association
Additional Journal Information:
Journal Volume: 112; Journal Issue: 520; Journal ID: ISSN 0162-1459
Publisher:
Taylor & Francis
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 97 MATHEMATICS AND COMPUTING; Inorganic and Physical Chemistry; Mathematics; Bayesian hierarchical modeling; BSS-ANOVA; Computer model calibration; Extrapolation; Functional data; Propagation of uncertainty

Citation Formats

Bhat, K. Sham, Mebane, David S., Mahapatra, Priyadarshi, and Storlie, Curtis Byron. Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System. United States: N. p., 2018. Web. doi:10.1080/01621459.2017.1295863.
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Bhat, K. Sham, Mebane, David S., Mahapatra, Priyadarshi, & Storlie, Curtis Byron. Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System. United States. https://doi.org/10.1080/01621459.2017.1295863
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Bhat, K. Sham, Mebane, David S., Mahapatra, Priyadarshi, and Storlie, Curtis Byron. Fri . "Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System". United States. https://doi.org/10.1080/01621459.2017.1295863. https://www.osti.gov/servlets/purl/1571589.
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@article{osti_1571589,
title = {Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System},
author = {Bhat, K. Sham and Mebane, David S. and Mahapatra, Priyadarshi and Storlie, Curtis Byron},
abstractNote = {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. In conclusion, 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.},
doi = {10.1080/01621459.2017.1295863},
journal = {Journal of the American Statistical Association},
number = 520,
volume = 112,
place = {United States},
year = {Fri Jan 26 00:00:00 EST 2018},
month = {Fri Jan 26 00:00:00 EST 2018}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1080/01621459.2017.1295863
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Citation Metrics:
Cited by: 15 works
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Figures / Tables:

Table 1 Table 1: Summary of Inputs, Outputs, and small-scale sorbent parameters.
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    Works referencing / citing this record:

    Learning and Optimization with Bayesian Hybrid Models
    conference, July 2020

    On the Bayesian calibration of expensive computer models with input dependent parameters
    text, January 2017

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