Upscaling Uncertainty in a Multi-Scale System

• Activity Number: 533
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 AM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): K. Sham Bhat*+ and Curtis Storlie and David Mebane and Joanne Wendelberger
• Companies: Statistical Sciences Group, Los Alamos National Lab and Los Alamos National Laboratory and Department of Mechanical and Aerospace Engineering,West Virginia University and Los Alamos National Laboratory
• Keywords: Computer models ; multiscale models ; uncertainty quantification ; Bayesian modeling ; uncertainty propagation

Abstract

Uncertainties from model parameters and model discrepancy from small-scale models impact the accuracy and reliability of large-scale systems. Inadequate representation of these uncertainties may result in inaccurate and overconfident predictions during scale-up to larger models. Hence multiscale modeling efforts must 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. We use an intrusive uncertainty quantification (UQ) approach by including 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 passing on the joint model-discrepancy posterior into the larger-scale system of rate expressions (to be solved). The broad UQ framework presented here may have far-reaching impact into virtually all areas of science where multiscale modeling is used.