Numerical modeling and uncertainty quantification of a bubbling fluidized bed with immersed horizontal tubes

Lane, W.A.; Storlie, Curtis B.; Montgomery, Christopher J.; Ryan, Emily M.

doi:10.1016/j.powtec.2013.11.037

Cited by 27 publications

(24 citation statements)

References 36 publications

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“…The relevant parameters identified in each C2U unit problem are described in their respective sections. In each unit problem, the MFIX model is analyzed using the same statistical UQ techniques as those used in the bubbling bed unit problem [34], including sensitivity analysis [35,36], Bayesian calibration, and model assessment [31,32,37]. The Bayesian model calibration of unit problem 1: 32D1 Cold Flow results in a posterior distribution to describe the remaining uncertainty in the model parameters .…”

Section: Hierarchical Calibration and Validation Proceduresmentioning

confidence: 99%

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

Lai

Pan

et al. 2016

Powder Technology

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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.

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Section: Hierarchical Calibration and Validation Proceduresmentioning

confidence: 99%

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

Lai

Pan

et al. 2016

Powder Technology

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“…A volume of fluid (VOF) method, an interface-tracking technique used for locating gas-liquid interface, together with a continuum surface force (CSF) model, 18,19 has been a common approach for exploring the hydrodynamics of gas-liquid interaction, including locating the gas-liquid interface and tracking the motion of gas and liquid flow. Meanwhile, Lane et al 28 and Storlie et al 29 have designed a general calibration framework to calibrate the CFD model of a bubbling fluidized bed, which is used as an absorber in the CO 2 capture system. To quantify the predictive confidence for the N 2 O/MEA system, the established model framework must be validated with controlled experimental results with a statistical calibration framework.…”

Section: Introductionmentioning

confidence: 99%

“…[23][24][25][26] Lai et al 27 have implemented a Bayesian calibration procedure to estimate the predictive confidence of the CFD model for solid sorbent-based carbon capture. Meanwhile, Lane et al 28 and Storlie et al 29 have designed a general calibration framework to calibrate the CFD model of a bubbling fluidized bed, which is used as an absorber in the CO 2 capture system.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical calibration and validation for modeling bench‐scale solvent‐based carbon capture. Part 1: Non‐reactive physical mass transfer across the wetted wall column

Wang

Lai

et al. 2017

Greenhouse Gases

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A hierarchical model calibration and validation are proposed for quantifying the confidence level of mass transfer prediction using a computational fluid dynamics (CFD) model, where the solvent‐based carbon dioxide (CO2) capture is simulated and the predicted results are compared to the corresponding bench‐scale experimental data. Two unit problems with an increasing level of complexity are proposed to break down the complex physical/chemical processes of solvent‐based CO2 capture into relatively simpler problems to separate the effects of physical transport and chemical reaction. This paper focuses on the calibration and validation of the first unit problem, i.e., the CO2 mass transfer across a falling ethanolamine (MEA) film in the absence of chemical reaction. This problem is investigated both experimentally and numerically using non‐reactive nitrous oxide (N2O) as a surrogate for CO2. To capture motion of the gas‐liquid interface, a volume of fluid method is employed with a one‐fluid formulation to compute the mass transfer between the two phases. Parallel bench‐scale experiments are designed and conducted to validate and calibrate the CFD models using a general Bayesian calibration approach. Two important transport parameters, Henry's constant and gas diffusivity, are calibrated to produce the posterior distributions, which will be used as input for the second unit problem to address the chemical absorption of CO2 across the MEA falling film, where both mass transfer and chemical reaction are involved. Mass transfer coefficients predicted by CFD are also compared with those predicted by the traditional/empirical correlations under both steady and wavy falling film conditions. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd.

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“…Here we present a general approach for calibration in situations where there are mul- This calibration approach is then applied to the bubbling fluidized bed problem that motivated its design. While some of the scientific results of this specific problem have been presented in Lane et al (2014), the focus here is on the presentation of the statistical methodology and analysis. Finally, the proposed approach is tested in a controlled simulation study and performance is compared to the Gaussian Process Models for Simulation Analysis (GPMSA) approach of (Higdon et al 2008) which has also been extended here to allow for categorical calibration parameters.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA

Storlie¹,

Lane²,

Ryan³

et al. 2015

Journal of the American Statistical Association

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It has become commonplace to use complex computer models to predict outcomes in regions where data does 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 model of a bubbling fluidized which is used as an absorber in a CO 2 capture system.

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Numerical modeling and uncertainty quantification of a bubbling fluidized bed with immersed horizontal tubes

Cited by 27 publications

References 36 publications

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

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

Hierarchical calibration and validation for modeling bench‐scale solvent‐based carbon capture. Part 1: Non‐reactive physical mass transfer across the wetted wall column

Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA

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