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To quantify the predictive confidence of a device scale solid sorbent-based carbon capture design where there is no direct experimental data available, a hierarchical validation methodology is first proposed. In this hierarchy, a sequence of increasingly complex "unit problems" are validated using a statistical calibration framework. This paper describes the computational fluid dynamics (CFD) multi-phase reactive flow simulations and the associated data flows within each unit problem. Each validation requires both simulated and physical data, so the bench-top experiments used in each increasingly complex stage were carefully designed to follow the same operating conditions as the simulation scenarios. A Bayesian calibration procedure is employed and the posterior model parameter distributions obtained at one unitproblem level are used as prior distributions for the same parameters in the next-tier simulations.Overall, the results have demonstrated that the calibrated multiphase reactive flow models within MFIX can be used to capture the bed pressure, temperature, CO2 capture capacity, and kinetics © 2015. This manuscript version is made available under the Elsevier user license http://www.elsevier.com/open-access/userlicense/1.0/ 2 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. KeywordsComputational fluid dynamics, carbon capture, hierarchical model validation methodology, multiphase reactive flow models, Bayesian calibration, MFIX.