Numerical simulations of transfer and transport properties inside packed beds of spherical particles

Augier, Frédéric; Idoux, F.; Delenne, Jean‐Yves

doi:10.1016/j.ces.2009.09.059

Cited by 121 publications

(68 citation statements)

References 30 publications

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“…In reality, the flow is seldom one-dimensional and a number of experimental studies have been conducted to determine the radial velocity profile and the effective radial thermal conductivity. Recently, several CFD and multi-physics simulations based upon the Navier Stokes equations have been used to more explicitly study these effects [10,5,16,4,15]. These studies have used as many as 30 million cells and required up to 11 hours of cpu time.…”

Section: Packed Bedsmentioning

confidence: 99%

A Study of the discrepancy function when modeling a packed bed using Variational Bayes and Gaussian processes

Sien

Emery

2018

J. Phys.: Conf. Ser.

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Abstract. Estimating properties using maximum likelihood (MLE) gives point values and a rough idea of the uncertainty in the parameters if their distributions are approximately normal. More accurate results require a Monte Carlo approach, often using the Markov Chain Monte Carlo (MCMC) method, which for complex models is unrealistic because of the computational expense. The Variational Bayes approach gives results comparable to MCMC with only a few evaluation of the model. When the model is imprecise or expensive to evaluate, Gaussian processes provide one means of analyzing the noise to determine the uncertainties.

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Section: Packed Bedsmentioning

confidence: 99%

A Study of the discrepancy function when modeling a packed bed using Variational Bayes and Gaussian processes

Sien

Emery

2018

J. Phys.: Conf. Ser.

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“…Figure 7 reports different CFD calculations of trickling flows between spherical particles, operated on different scales. A qualitative comparison with experiments is proposed: -3-particle-scale 3D simulation (Augier et al, 2010a); -36-particle-scale pseudo-2D simulation (Horgue et al, 2013); -over 300-particle-scale simulation performed in 2014 on a 64-processor cluster. More quantitative comparisons have also been reported in the cited papers.…”

Section: Solvent Screening Toolmentioning

confidence: 99%

“…Realistic particle loadings can be simulated by mechanical models (Zou, 1995;Augier et al, 2010a) or characterised by non-intrusive analysis such as MRI or X-Tomography, leading to the extraction of usable meshing domains for simulation (Sederman and Gladden, 2001;Larachi et al, 2014). Otherwise, in the case of multi-fluid flows, interface reconstructive models are of special interest.…”

Section: Solvent Screening Toolmentioning

confidence: 99%

CFD Applied to Process Development in the Oil and Gas Industry – A Review

Raynal

Augier

Bazer-Bachi

et al. 2015

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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-Computational Fluid Dynamics (CFD) is increasingly used in the oil and gas industry. The present article aims to show how CFD can be used at all steps of the development of a new process, with a focus on refining technologies. Those different steps consist first of setting up tools that will be used during the development phase, second of obtaining data in complement with experiments required for the process development, and finally, of troubleshooting actions or technology developments that will make the process even more efficient. A large number of applications corresponding to various flow configurations, single-phase or gas-liquid, gas-solid or even gas-liquid-solid, characterised by significantly different scales and requiring adapted simulation approaches, are discussed based on original results and a review of the literature. Perspectives are given in particular on the multi-scale approach and physical phenomena coupling.Résumé -La simulation numérique des écoulements appliquée au développement de procédés dans le monde de l'industrie du pétrole et du gaz -une revue -La simulation numérique des écoulements, ou Computational Fluid Dynamics (CFD), est un outil de plus en plus employé dans les secteurs pétrolier et gazier. Cet article permet d'illustrer comment l'outil numérique est utilisé lors des différents stades de développement d'un nouveau procédé, en particulier dans le monde du raffinage. Ces différentes phases correspondent tout d'abord à la mise au point des équipements qui seront utilisés pour le développement, puis au complément des essais lors de la phase de développement proprement dite et enfin à des actions support pour la résolution de problèmes opératoires ou l'amélioration de technologies qui permettront d'optimiser le procédé. De nombreuses applications correspondant à différents types d'écoulements, monophasique ou gaz-liquide, gazsolide et gaz-liquide-solide, caractérisées par des échelles de résolution différentes et requérant des approches de simulations adaptées sont discutées sur la base de résultats originaux et d'une revue de travaux déjà publiés. Des perspectives sont aussi données tant sur les aspects approche multi-échelles que sur le couplage de phénomènes.

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“…The flow in an unstructured packing of poly-disperse spherical particles with a value of polydispersity deviating by 14% from the mean diameter was considered by Augier et al [63]. A dense packing of spheres was generated using DEM, neglecting the friction between particles and between the particles and the walls.…”

Section: Two-phase (Heterogeneous) Modelsmentioning

confidence: 99%

Simulation of thermal conversion of solid fuel by the discrete particle method

Peters¹,

Džiugys²,

Navakas³

2011

Lith. J. Phys.

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We introduce the discrete particle method (DPM) that derives from the discrete element method (DEM). This is an advanced numerical simulation tool that takes into account both motion and chemical conversion of granular material, such as coal or biomass, in furnaces in conjunction with computational fluid dynamics (CFD). However, predictions of solely motion or conversion in a decoupled mode are also applicable. The DPM uses object oriented computational techniques that support objects representing three-dimensional particles of various shapes, size, and physical properties of particle material. This makes DPM a highly versatile tool in dealing with a variety of problems related to different industrial applications of granular matter. A review of literature concerning different approaches to mass and heat transfer in packed beds is presented.

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Numerical simulations of transfer and transport properties inside packed beds of spherical particles

Cited by 121 publications

References 30 publications

A Study of the discrepancy function when modeling a packed bed using Variational Bayes and Gaussian processes

A Study of the discrepancy function when modeling a packed bed using Variational Bayes and Gaussian processes

CFD Applied to Process Development in the Oil and Gas Industry – A Review

Simulation of thermal conversion of solid fuel by the discrete particle method

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