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Title: Hierarchical calibration and validation of computational fluid dynamics models for solid sorbent-based carbon capture

To quantify the predictive confidence of a solid sorbent-based carbon capture design, a hierarchical validation methodology—consisting of basic unit problems with increasing physical complexity coupled with filtered model-based geometric upscaling has been developed and implemented. This paper describes the computational fluid dynamics (CFD) multi-phase reactive flow simulations and the associated data flows among different unit problems performed within the said hierarchical validation approach. The bench-top experiments used in this calibration and validation effort were carefully designed to follow the desired simple-to-complex unit problem hierarchy, with corresponding data acquisition to support model parameters calibrations at each unit problem level. A Bayesian calibration procedure is employed and the posterior model parameter distributions obtained at one unit-problem level are used as prior distributions for the same parameters in the next-tier simulations. Overall, the results have demonstrated that the multiphase reactive flow models within MFIX can be used to capture the bed pressure, temperature, CO2 capture capacity, and kinetics with quantitative accuracy. The CFD modeling methodology and associated uncertainty quantification techniques presented herein offer a solid framework for estimating the predictive confidence in the virtual scale up of a larger carbon capture device.
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Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 0032-5910; AA9010100
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Powder Technology; Journal Volume: 288
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
Country of Publication:
United States
CCSI; carbon capture; sorbent; CFD; MFIX; bubbling bed; hierarchical model validation methodology; multiphase reactive flow models; uncertainty quantification; Bayesian calibration