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Title: Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System

Journal Article · · Journal of the American Statistical Association
ORCiD logo [1];  [2];  [3];  [4]
  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)
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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.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE Office of Fossil Energy (FE)
Grant/Contract Number:
89233218CNA000001
OSTI ID:
1571589
Report Number(s):
LA-UR-14-26061
Journal Information:
Journal of the American Statistical Association, Vol. 112, Issue 520; ISSN 0162-1459
Publisher:
Taylor & FrancisCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 15 works
Citation information provided by
Web of Science

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conference June 2011
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Cited By (2)

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

Linked Research (2)

Figures / Tables (16)

Table 1(p. 36)table Table 1
Table 2(p. 37)table Table 2
Figure 1(p. 38)figure Figure 1
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Figure 14(p. 51)figure Figure 14

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
97 MATHEMATICS AND COMPUTING
Inorganic and Physical Chemistry
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Functional data
Propagation of uncertainty