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|Title:||A detailed particle model for polydisperse aggregate particles||Authors:||Lindberg, Casper S.
Manuputty, Manoel Y.
Yapp, Edward Kien Yee
|Keywords:||Engineering::Chemical engineering||Issue Date:||2019||Source:||Lindberg, C. S., Manuputty, M. Y., Yapp, E. K. Y., Akroyd, J., Xu, R. & Kraft, M. (2019). A detailed particle model for polydisperse aggregate particles. Journal of Computational Physics, 397, 108799-. https://dx.doi.org/10.1016/j.jcp.2019.06.074||Journal:||Journal of Computational Physics||Abstract:||The mathematical description of a new detailed particle model for polydisperse aggregate particles is presented. An aggregate particle is represented as a collection of overlapping spherical primary particles and the model resolves the composition, radius and position coordinates of each individual primary to form a detailed geometrical description of aggregate morphology. Particles transform under inception, coagulation, surface growth, sintering and coalescence processes. The new particle description is used to model the aerosol synthesis of titanium dioxide ((Figure presented.)) aggregates from titanium tetraisopropoxide (TTIP) precursor. (Figure presented.) particles are formed through collision-limited inception and growth reactions of (Figure presented.) from the gas-phase, produced from the thermal decomposition of TTIP. Coupling between the particle population balance and detailed gas-phase chemistry is achieved by operator splitting. A numerical study is performed by simulating a simple batch reactor test case to investigate the convergence behaviour of key functionals with respect to the maximum number of computational particles and splitting time step. Finally, a lab-scale hot wall reactor is simulated to demonstrate the advantages of a detailed geometrical description. Simulated particle size distributions were in reasonable agreement with experimental data. Further evaluation of the model and a parametric sensitivity study are recommended.||URI:||https://hdl.handle.net/10356/152274||ISSN:||0021-9991||DOI:||10.1016/j.jcp.2019.06.074||Rights:||© 2019 Elsevier Inc. All rights reserved. This paper was published in Journal of Computational Physics and is made available with permission of Elsevier Inc.||Fulltext Permission:||embargo_20211122||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCBE Journal Articles|
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