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A case study in multi‐scale model reduction: The effect of cell density on catalytic converter performance
Author(s) -
Fadic Anton,
Nien TengWang,
Mmbaga Joseph,
Hayes Robert E.,
Votsmeier Martin
Publication year - 2014
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.22023
Subject(s) - reduction (mathematics) , scale (ratio) , converters , catalytic combustion , transient (computer programming) , monolith , methane , range (aeronautics) , combustion , catalytic converter , computer science , full scale , steady state (chemistry) , catalysis , computational fluid dynamics , diffusion , simulation , process engineering , materials science , mechanics , chemistry , engineering , physics , mathematics , electrical engineering , thermodynamics , voltage , composite material , operating system , biochemistry , geometry , quantum mechanics , computer vision , organic chemistry
One of the challenges of full‐scale computer simulation of a catalytic reactor is to consider the different scales involved in the problem in a practical fashion. In a monolith catalytic converter, these scales range from the molecular scale for the reactions, through the pore scale, washcoat scale, channel scale, and finally the full converter scale. This paper describes the implementation of a model reduction methodology using look‐up tables to perform a consistent comparison of six different catalytic converters used for the catalytic combustion of methane. A detailed mechanistic model for methane combustion is used. Diffusion in the non‐uniform washcoat is considered. The converters have different cell densities and wall thicknesses. Steady state and transient light‐off simulations are performed. Efficient computational speed is achieved by successive model reduction, which allows the preservation of detailed small‐scale information. The results obtained show that there is a non‐intuitive relationship between the various operating parameters, which can only be deduced from a comprehensive model.