Upscaling Uncertainty with Dynamic Discrepancy for a Multi-scale Carbon Capture System

TL;DR

This paper presents a Bayesian multiscale modeling framework that incorporates dynamic discrepancy representation to accurately propagate uncertainties from small-scale models to large-scale systems, improving reliability in predictions.

Contribution

It introduces a novel Bayesian approach with dynamic discrepancy modeling using BSS-ANOVA within an intrusive UQ framework for multiscale systems.

Findings

Effective uncertainty propagation in a carbon capture system model

Improved prediction accuracy through dynamic discrepancy integration

Framework applicable to various multiscale modeling contexts

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 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 propagating the joint model parameter and discrepancy posterior into the larger-scale system of rate expressions. The broad UQ framework presented here may have far-reaching impact into virtually all areas of science where multiscale modeling is used.