Article (Scientific journals)
XDEM employed to predict reduction of tungsten oxide in a dry hydrogen atmosphere
Estupinan Donoso, Alvaro Antonio; Peters, Bernhard
2014In International Journal of Refractory Metals and Hard Materials
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Keywords :
Tungsten oxide reduction; xdem; dem
Abstract :
[en] Abstract The Extended Discrete Element Method (XDEM) is a novel concept to model tungsten oxides reduction. The concept extends the classical discrete element method (DEM) with additional properties such as the thermodynamic state. Moreover, the concept treats a solid phase represented by particles, and a fluid phase as two distinguished phases that are coupled through heat, mass and momentum transfer. hydrogen atmosphere is modelled by a direct oxygen removal from the solid oxides mechanism for which temperature and reaction progress is described by the Discrete Particle Method (DPM). An outstanding feature of the herein proposed numerical concept is that powder particles are treated as individual entities which are described by its thermodynamic state, e.g. temperature and species distribution within the particle. Therefore, it allows a detailed and accurate characterisation of isothermal literature experimentation with a high degree of accuracy. Therefore, the current approach provides a new and deep insight into the process, because particle temperatures, concentration of species and interaction of particles with the environment are inaccessible in a furnace during experiments.
Disciplines :
Materials science & engineering
Author, co-author :
Estupinan Donoso, Alvaro Antonio  ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Peters, Bernhard ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Language :
English
Title :
XDEM employed to predict reduction of tungsten oxide in a dry hydrogen atmosphere
Publication date :
2014
Journal title :
International Journal of Refractory Metals and Hard Materials
ISSN :
0263-4368
Publisher :
Elsevier, United Kingdom
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
Available on ORBilu :
since 27 August 2014

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