Verification, validation, and uncertainty quantification of a sub-grid model for heat transfer in gas-particle flows with immersed horizontal cylinders. (2nd February 2018)

• Record Type: Journal Article
• Authors: Lane, W.A.; Ryan, E.M.
• Is Part Of: Chemical engineering science. Volume 176(2018)
• Page Start: 409
• Page End: 420
• Publication Date: 2018-02-02
• Subjects: Sub-grid -- Multiphase -- Heat transfer -- Verification -- Validation -- Uncertainty quantification; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2017.11.018
• Languages: English
• ISSNs: 0009-2509

Abstract

Highlights: Model showed excellent transient and steady state behavior with verification cases. Large-scale validation agreed well with test cases, observing 100, 000+ times speedup. Prediction intervals from bootstrapping and MATLAB'snlpredi were within ±20%. Simulated a fully-reacting 1 MW CO2 adsorber, outperforming ad-hoc approaches. Abstract: In previous work we developed and implemented a sub-grid model for the efficient simulation of heat transfer in gas-particle flows around immersed horizontal cylinders. In this study we apply verification, validation, and uncertainty quantification methods to the developed model to rigorously examine its capabilities and limitations. Numerical verification with small, unit-cell problems shows excellent transient and steady-state behavior. Validation of a bubbling bed and a turbulent bed showed good agreement with high-resolution simulations. To quantify the error of the constitutive model predictions two methods were used to calculate confidence intervals, showing an error of approximately ± 20%, well within the range of typical Nusselt number approximations. The sub-grid model was applied to a conceptual pilot-scale 1 MWe carbon capture reactor to compare with alternative modeling methods. Results show fair predictions of hydrodynamics, heat transfer, and carbon capture rates with significant savings in computational runtimes.