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Phoenix, AZ, United States

Li T.,U.S. National Energy Technology Laboratory | Li T.,URS Corporation | Gel A.,U.S. National Energy Technology Laboratory | Gel A.,ALPEMI Consulting LLC | And 3 more authors.
Powder Technology | Year: 2014

In this work, a detailed grid refinement study was carried out for two well-documented circulating fluidized bed (CFB) systems with the focus on grid convergence of 2D numerical simulations. It is demonstrated that the grid convergence of numerical simulations depends on the flow field variable chosen for verification. For axial pressure gradient, this study shows that no general rule for grid size is available to guarantee the grid-independent results. In addition, the inlet and outlet configuration used in the 2D simulations shows a significant impact on the grid convergence. A 3D grid study is also presented with the intent to probe the differences between 2D and 3D numerical simulations with respect to the grid convergence. For the case considered in this study, the 3D simulation demonstrates better grid convergent behavior than the 2D simulation with comparable grid sizes. © 2014 Elsevier B.V. Source


Li T.,U.S. National Energy Technology Laboratory | Li T.,URS Corporation | Gel A.,U.S. National Energy Technology Laboratory | Gel A.,ALPEMI Consulting LLC | And 3 more authors.
Powder Technology | Year: 2014

In this work, a detailed grid refinement study was carried out for two well-documented circulating fluidized bed (CFB) systems with the focus on grid convergence of 2D numerical simulations. It is demonstrated that the grid convergence of numerical simulations depends on the flow field variable chosen for verification. For axial pressure gradient, this study shows that no general rule for grid size is available to guarantee the grid-independent results. In addition, the inlet and outlet configuration used in the 2D simulations shows a significant impact on the grid convergence. A 3D grid study is also presented with the intent to probe the differences between 2D and 3D numerical simulations with respect to the grid convergence. For the case considered in this study, the 3D simulation demonstrates better grid convergent behavior than the 2D simulation with comparable grid sizes. © 2014 Elsevier B.V. Source


Li T.,U.S. National Energy Technology Laboratory | Gel A.,U.S. National Energy Technology Laboratory | Gel A.,ALPEMI Consulting LLC | Syamlal M.,U.S. National Energy Technology Laboratory | And 2 more authors.
Industrial and Engineering Chemistry Research | Year: 2010

This study demonstrates an approach to effectively combine high- and low-resolution simulations for design studies of industrial coal gasifier. The flow-field data from a 10 million cell full-scale simulation of a commercial-scale gasifier were used to construct a reduced configuration to economically study the coal injection in detail. High-resolution numerical simulations of the coal injection were performed using the open-source code MFIX running on a high performance computing system. Effects of grid resolution and numerical discretization scheme on the predicted behavior of coal injection and gasification kinetics were analyzed. Pronounced differences were predicted in the devolatilization and steam gasification rates because of different discretization schemes, implying that a high-order numerical scheme is required to predict well the unsteady gasification process on an adequately resolved grid. Computational costs for simulations of varying resolutions are presented to illustrate the trade-off between the accuracy of solution and the time-to-solution, an important consideration when engineering simulations are used for the design of commercial-scale units. © 2010 American Chemical Society. Source


Gel A.,U.S. National Energy Technology Laboratory | Gel A.,ALPEMI Consulting LLC | Chaudhari K.,U.S. National Energy Technology Laboratory | Chaudhari K.,West Virginia University | And 4 more authors.
Powder Technology | Year: 2014

The focus of this research is to study sensitivity of input parameters in terms of chemical reaction kinetics of coal devolatilization using non-intrusive uncertainty quantification (UQ) methods. The effects of heating rate, pressure, and temperature on coal devolatilization have been considered. Variations in coal devolatilization kinetics and product yields were captured via Carbonaceous Chemistry for Computational Modeling (C3M) for operating conditions similar to the transport gasifier using PC Coal Lab (PCCL) kinetic package. Temperature, pressure and heating rate were considered as three input parameters, while the quantities of interest or response variables were mass fractions of CO, CO2, H2, tar, H2O, and CH4 along with total volatile yield. A direct Monte Carlo-simulation-based approach was employed to perform the UQ analysis. The correlations among the response variables were investigated by computing a correlation matrix that supports the findings of yield of devolatilization reported by various experiments in the literature. Sensitivity study of the input parameters was analyzed by using the Sobol Total Indices methodology implemented in PSUADE, an open source UQ toolbox. These findings clearly demonstrate the pronounced effect of temperature on coal devolatilization product yields, and hence will be considered as a key parameter in future studies. The preliminary study presented in this paper paves a path for incorporating uncertainty caused by chemical reaction kinetics in computational fluid dynamics based modeling of coal gasifier systems and scale-up studies. © 2014 Elsevier B.V. Source


Chaudhari K.,U.S. National Energy Technology Laboratory | Chaudhari K.,West Virginia University | Turton R.,U.S. National Energy Technology Laboratory | Turton R.,West Virginia University | And 11 more authors.
29th Annual International Pittsburgh Coal Conference 2012, PCC 2012 | Year: 2012

The features and capabilities of Carbonaceous Chemistry for Computational Modeling (C3M) were introduced during 2011 International Pittsburgh Coal Conference. Since then, the development of C3M has progressed significantly. The new features of C3M will be discussed in this presentation. Specifically, the new modifications in C3M's kinetic expressions and its CFD compatibility will be discussed. Currently, coal/biomass/petcoke gasification process simulation is linked to computational fluid dynamic (CFD) software programs such as such as Multiphase Flow with Interphase Exchanges (MFIX) developed at NETL, ANSYS-Fluent by ANSYS Inc. and Barracuda by CPFD Software with chemical kinetics and laboratory data. These kinetic expressions describe the fundamental steps taking place in the gasification of coal/petcoke/biomass, namely, devolatilization, tar-gas chemistry, soot formation, and the subsequent heterogeneous and homogeneous gasification and combustion reactions. For this purpose, the kinetic data generated through a number of detailed models such as METC Gasifier Advanced Simulation (MGAS), PC Coal Lab (PCCL), Chemical Percolation Model for Coal Devolatilization (CPD), Solomon's Functional-Group, Depolymerization, Vaporization, Cross-linking (FGDVC) model, or experimental data currently being generated at NETL can be used as input. C3M provides the option to select the fuel type that includes a wide variety of coals, biomass and petcoke. The kinetic packages used to generate the kinetic expressions for various reactions along with fuels are: • Devolatilization: MGAS (coal), PCCL (coal/biomass/petcoke), CPD (coal), FGDVC (coal), experimental data (coal/biomass). • Tar cracking: MGAS (coal), PCCL (coal/petcoke), FGDVC (coal), experimental data (coal/biomass). • Char gasification: MGAS (coal), PCCL (coal/petcoke/biomass), experimental data (coal/biomass). • Char oxidation: MGAS (coal), PCCL (coal/petcoke/biomass). • Soot formation: PCCL (coal/petcoke/biomass), CPD (coal). • Soot oxidation and gasification: PCCL (coal/petcoke/biomass). The C3M graphical user interface (GUI) has been modified to allow users to run the various kinetic models and evaluate graphically the effect different fuels and/or gasifier operating conditions have on gasification kinetics along with the yield of product species. After displaying the data, the C3M GUI allows the user to select kinetic information from a particular kinetic model and then correctly formats the data and seamlessly integrates it into the CFD input files thus allowing the user to run the CFD code without extensive development effort for implementing the governing reactions. Uncertainty Quantification (UQ) analysis for coal gasification process is a unique feature provided by C3M. With the UQ analysis capability, a user can better understand the effect of variations in operating conditions and fuel properties in the product yields and reaction rates. This is achieved through a Monte Carlo type simulation consisting of many sampling runs on the kinetic packages available in C3M by randomly drawing values for input parameters and statistically analyzing the output. The fact that computational cost per sample is very cheap, direct Monte Carlo simulation is possible as opposed to a surrogate model based sampling approach, which is commonly used when the computational cost per sample is high. This UQ work is on-going and will be extended to the CFD packages in the future. Source

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