Numerical studies on the combustion properties of char particle clusters

Liu, Xiang Jun; Li, Li

doi:10.1016/j.ijheatmasstransfer.2009.06.009

Cited by 16 publications

(6 citation statements)

References 22 publications

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“…Moreover, some recent experimental and numerical works on the ignition and combustion of coal particle cluster also found that there should be an optimum coal particle number in the cluster for the shortest ignition delay time, and the existence of the optimum value should be related to the transition from heterogeneous ignition to homogeneous ignition [15e21]. The experimental results by Du et al [15] indicated that homogenous ignition (ignition of volatile) occurs for a dense cluster while heterogeneous ignition (ignition of char) occurs for a dilute cloud, and the following modeling results by others also suggested that with the increasing in the coal particle concentration, the gas temperature distribution within the cluster tended to be uniform and homogeneous ignition was enhanced while heterogeneous ignition was retarded [18,20]. It should be noted that the optimum concentration was mainly found under the condition of homogenous ignition for dense cloud, while under the condition of heterogeneous ignition for dilute cloud this optimum value was not found.…”

Section: Introductionmentioning

confidence: 99%

Determining the optimum coal concentration in a general tangential-fired furnace with rich-lean burners: From a bench-scale to a pilot-scale study

Wang

Tan

Yan

et al. 2014

Applied Thermal Engineering

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Section: Introductionmentioning

confidence: 99%

Determining the optimum coal concentration in a general tangential-fired furnace with rich-lean burners: From a bench-scale to a pilot-scale study

Wang

Tan

Yan

et al. 2014

Applied Thermal Engineering

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“…Understanding mass transport processes as well as the fluid flow in such complex heterogeneous systems is of tremendous importance to improve performance and facilitate optimal design of process equipment. However, the occurrence of particle clusters is observed − and well accepted to have strong impact on the hydrodynamics − and consequently the mass transport behavior. − The occurrence of particle clusters was first reported by Yerushalmi et al, and later Tsuo and Gidaspow studied the underlying principles for the formation and propagation of clusters. Although there is no firmly established definition for a cluster, ,,,− one feature is universally accepted that a cluster is composed of particles and possesses an internal solid volume fraction significantly larger than its surroundings (which is normally below 0.1 in CFB).…”

Section: Introductionmentioning

confidence: 99%

Direct Numerical Simulation of Fluid Flow and Mass Transfer in Particle Clusters

Peters

Kuipers

2018

Ind. Eng. Chem. Res.

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In this paper, an efficient ghost-cell based immersed boundary method is applied to perform direct numerical simulation (DNS) of mass transfer problems in particle clusters. To be specific, a nine-sphere cuboid cluster and a random-generated spherical cluster consisting of 100 spheres are studied. In both cases, the cluster is composed of active catalysts and inert particles, and the mutual influence of particles on their mass transfer performance is studied. To simulate active catalysts the Dirichlet boundary condition is imposed at the external surface of spheres, while the zero-flux Neumann boundary condition is applied for inert particles. Through our studies, clustering is found to have negative influence on the mass transfer performance, which can be then improved by dilution with inert particles and higher Reynolds numbers. The distribution of active/inert particles may lead to large variations of the cluster mass transfer performance, and individual particle deep inside the cluster may possess a high Sherwood number.

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“…Fully resolved simulation (FRS) or true direct numerical simulation has been adopted for studying the combustion characteristics of solid fuel at a particle level . Lui et al simulated a uniformly fixed distributed particle group combustion. We simulated moving particle combustion using a fictitious domain method (FDM), which adopts a set of unified mesh and governing equations describing both the gas and solid phases. , In addition, research on the drag force of particles in a combustion environment has progressed steadily …”

Section: Introductionmentioning

confidence: 99%

Modeling of Combustion Characteristics of Particles in Transient Gas–Solid Reacting Flow via a Machine Learning Approach

Zhang

You

2022

Ind. Eng. Chem. Res.

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Particle group combustion presents a strong temporal and spatial inhomogeneity owing to the complicated interphase interactions. Based on the data set from the fictitious domain method, the recurrent fully connected and convolutional parallel neural network (R-FC&CNN) architecture and its two comparable simplified models, that is, the recurrent fully connected neural network (R-FCNN) and the recurrent convolutional neural network (R-CNN) architectures, were constructed for predicting the gas−solid momentum exchange coefficient, β (kg•s −1 • m −3 ), average combustion rate per unit surface area of particles, r A / c (kg• s −1 •m −2 ), and comprehensive NaCl release parameter, γ, selectively. A time sequence of average particle temperature, T̅ (K), and particle volume fraction, ε, which can be extended in the matrix form, were constructed as the features selectively according to their correlation with the target physical quantity. The average relative error, δ̅ , and coefficient of determination, R 2 , were used as the evaluators. Through final testing, in the mild combustion domain, the R-CNN and R-FCNN models with simple structures showed good performance for β(δ̅ = 0.13, R 2 = 0.8) and r A / c (δ̅ = 0.04, R 2 = 0.84), respectively, while in the severe combustion domain, the R-FC&CNN model, with more complete features and functional structure, performed the best (for β, δ̅ = 0.12, R 2 = 0.85; for r A / c , δ̅ = 0.05, R 2 = 0.92; and for γ, = = R 0.05, 0.96 2). A fine-tuning and interpolated prediction method was developed to investigate further the model's expansibility. Both ensured acceptable performance on a new similar problem. In summary, the feasibility of physical meaningoriented machine learning--based model(s) for predicting the combustion characteristics of nonuniformly distributed particles was confirmed.

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Numerical studies on the combustion properties of char particle clusters

Cited by 16 publications

References 22 publications

Determining the optimum coal concentration in a general tangential-fired furnace with rich-lean burners: From a bench-scale to a pilot-scale study

Determining the optimum coal concentration in a general tangential-fired furnace with rich-lean burners: From a bench-scale to a pilot-scale study

Direct Numerical Simulation of Fluid Flow and Mass Transfer in Particle Clusters

Modeling of Combustion Characteristics of Particles in Transient Gas–Solid Reacting Flow via a Machine Learning Approach

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