Validation of a novel open-source work-flow for the simulation of packed-bed reactors

Boccardo, Gianluca; Augier, Frédéric; Haroun, Yacine; Ferré, Daniel Curulla; Marchisio, Daniele

doi:10.1016/j.cej.2015.05.032

Cited by 119 publications

(74 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Next, a loop over the number of cases is entered which starts with packing and mesh generation. As proposed by Boccardo et al [9], the individual particle positions within the random packed beds are determined using the open- source animation software BlenderÔ, relying on a rigidbody approach integrated through the Bullet physics library [13]. Based on Blender's python interface and extensive scripting functionality, packing generation is carried out by a python script, which is part of the base case and updated according to user input in the very first step of the simulation routine.…”

Section: Methodsmentioning

confidence: 99%

“…Simulations on the pressure drop in random packed bed adsorbers presented by Haddadi et al were based on an in‐house DEM code for packing generation and OpenFOAM® for meshing and flow simulation and validated experimentally. An open‐source based work flow using Blender™ for packing generation was put forward by Boccardo et al . Mesh generation and laminar flow simulation of water through random packed beds of spheres and cylinders were conducted with OpenFOAM® and pressure drop results were in agreement with the Ergun equation.…”

Section: Introductionmentioning

confidence: 99%

“…Simulations on the pressure drop in random packed bed adsorbers presented by Haddadi et al [8] Boccardo et al [9]. Mesh generation and laminar flow simulation of water through random packed beds of spheres and cylinders were conducted with OpenFOAM and pressure drop results were in agreement with the Ergun equation.…”

Section: Introductionmentioning

confidence: 99%

“…The random packing is generated with BlenderÔ 2.76b [10], mesh generation and simulation are performed with Open-FOAM v2.4.0 [11] whereas ParaView 4.1.0 [12] is used for data analysis and visualization. Whereas part of the used methodology is clearly in accordance with Boccardo et al [9], the focus here is on the automation of the described routine, explicitly including parameter variations as starting point of individual simulation runs within one routine. Also, a heterogeneous reaction at the catalytic pellet surface is considered in addition.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Scripting as an Approach to Automated CFD Simulation for Packed Bed Catalytic Reactor Modeling

Fernengel

Habla

Hinrichsen

2018

Chemie Ingenieur Technik

View full text Add to dashboard Cite

In recent years computational fluid dynamics (CFD) has become a widely applied tool for the simulation of catalytic fixed‐bed reactors and numerous authors have contributed towards methods for random packed bed generation and flow simulation. The presented approach focuses on an automated simulation routine realized through bash and python scripting covering multiple entire simulation runs including preprocessing, simulation as well as post‐processing based on open‐source software. As an example, the routine is demonstrated with a case study on catalytic fixed‐bed reactors.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scripting as an Approach to Automated CFD Simulation for Packed Bed Catalytic Reactor Modeling

Fernengel

Habla

Hinrichsen

2018

Chemie Ingenieur Technik

View full text Add to dashboard Cite

show abstract

“…: comparison of fluid pressure drop obtained from pore-scale simulations with Ergun's law predictions). Much more information on the creation of this packings, and the validation procedure just described, can be found in our previous work [23].…”

Section: Random Packingsmentioning

confidence: 99%

On the predictivity of pore-scale simulations: Estimating uncertainties with multilevel Monte Carlo

Icardi

Boccardo

Tempone

2016

Advances in Water Resources

Self Cite

View full text Add to dashboard Cite

A fast method with tunable accuracy is proposed to estimate errors and uncertainties in porescale and Digital Rock Physics (DRP) problems. The overall predictivity of these studies can be, in fact, hindered by many factors including sample heterogeneity, computational and imaging limitations, model inadequacy and not perfectly known physical parameters. The typical objective of pore-scale studies is the estimation of macroscopic effective parameters such as permeability, effective diffusivity and hydrodynamic dispersion. However, these are often non-deterministic quantities (i.e., results obtained for specific pore-scale sample and setup are not totally reproducible by another "equivalent" sample and setup). The stochastic nature can arise due to the multi-scale heterogeneity, the computational and experimental limitations in considering large samples, and the complexity of the physical models. These approximations, in fact, introduce an error that, being dependent on a large number of complex factors, can be modeled as random. We propose a general simulation tool, based on multilevel Monte Carlo, that can reduce drastically the computational cost needed for computing accurate statistics of effective parameters and other quantities of interest, under any of these random errors. This is, to our knowledge, the first attempt to include Uncertainty Quantification (UQ) in pore-scale physics and simulation. The method can also provide estimates of the discretization error and it is tested on three-dimensional transport problems in heterogeneous materials, where the sampling procedure is done by generation algorithms able to reproduce realistic consolidated and unconsolidated random sphere and ellipsoid packings and arrangements. A totally automatic workflow is developed in an opensource code [1], that include rigid body physics and random packing algorithms, unstructured mesh discretization, finite volume solvers, extrapolation and post-processing techniques. The proposed method can be efficiently used in many porous media applications for problems such as stochastic homogenization/upscaling, propagation of uncertainty from microscopic fluid and rock properties to macro-scale parameters, robust estimation of Representative Elementary Volume size for arbitrary physics.

show abstract

Experimental and numerical investigation of structure and hydrodynamics in packed beds of spherical particles

et al. 2018

View full text Add to dashboard Cite

In chemical industry, flows often occur in nontransparent equipment, for example in steel pipelines and vessels. Magnetic resonance imaging is a suitable approach to visualize the flow, which cannot be performed with classical optical techniques, and obtain quantitative data in such cases. It is therefore a unique tool to noninvasively study whole-field porosity and velocity distributions in opaque single-phase porous media flow. In this article, experimental results obtained with this technique, applied to the study of structure and hydrodynamics in packed beds of spherical particles, are shown and compared with detailed computational fluid dynamics simulations performed with an in-house numerical code based on an immersed boundary method-direct numerical simulation approach. Pressure drop and the radial profiles of porosity and axial velocity of the fluid for three packed beds of spheres with different sizes were evaluated, both experimentally and numerically, in order to compare the two approaches.

show abstract

Validation of a novel open-source work-flow for the simulation of packed-bed reactors

Cited by 119 publications

References 52 publications

Scripting as an Approach to Automated CFD Simulation for Packed Bed Catalytic Reactor Modeling

Scripting as an Approach to Automated CFD Simulation for Packed Bed Catalytic Reactor Modeling

On the predictivity of pore-scale simulations: Estimating uncertainties with multilevel Monte Carlo

Experimental and numerical investigation of structure and hydrodynamics in packed beds of spherical particles

Contact Info

Product

Resources

About