Abstract
It has become commonplace to use complex computer models to predict outcomes in regions where data do not exist. Typically these models need to be calibrated and validated using some experimental data, which often consists of multiple correlated outcomes. In addition, some of the model parameters may be categorical in nature, such as a pointer variable to alternate models (or submodels) for some of the physics of the system. Here, we present a general approach for calibration in such situations where an emulator of the computationally demanding models and a discrepancy term from the model to reality are represented within a Bayesian smoothing spline (BSS) ANOVA framework. The BSS-ANOVA framework has several advantages over the traditional Gaussian process, including ease of handling categorical inputs and correlated outputs, and improved computational efficiency. Finally, this framework is then applied to the problem that motivated its design; a calibration of a computational fluid dynamics (CFD) model of a bubbling fluidized which is used as an absorber in a CO2 capture system. Supplementary materials for this article are available online.
Highlights
IntroductionThe analysis of many physical and engineering problems (e.g., climate change, nuclear reactor performance, fluid transport, and carbon capture systems) involves running complex computational models (i.e., simulators)
The analysis of many physical and engineering problems involves running complex computational models
The calibration approach is used on an example that mimics the bubbling bed analysis to see exactly how well it performs on a truth-known case
Summary
The analysis of many physical and engineering problems (e.g., climate change, nuclear reactor performance, fluid transport, and carbon capture systems) involves running complex computational models (i.e., simulators). Model calibration and model assessment (i.e., validation) are two important components of UQ that are addressed in this paper in the context of computational fluid dynamics (CFD) models as part of the Department of Energy’s (DOE’s) Carbon Capture Simulation Initiative (CCSI). Fluidized beds are widely used in chemical engineering systems and processes (e.g., combustion, mixing, polymerization, and carbon capture) (Asegehegn, Schreiber & Krautz 2011a). This particular problem is a “unit” problem within a larger set of problems being investigated through CCSI. The general UQ methodology will be used for further calibration on laboratory and pilot scale systems, and for uncertainty propagation to a full scale CO2 capture system
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