Computation of Particle and Scalar Transport for Complex Geometry Turbulent Flows  

Oleh:

Tucker, P. G.

Jenis: Article from Bulletin/Magazine
Dalam koleksi: Journal of Fluids Engineering vol. 123 no. 2 (2001), page 372-381.
Topik: computational; computation; particle; scalar; transport; geometry; turbulent flows

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: JJ89.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The prediction of particle and scalar transport in a complex geometry with turbulent flow driven by fans is considered. The effects of using different turbulence models, anisotropy, flow unsteadiness, fan swirl, and electrostatic forces on particle trajectories are shown. The turbulence models explored include k – l, zonal k– E / k – l, and non linear eddy viscosity models. Particle transport is predicted using a stochastic technique. A simple algorithm to compute electrostatic image forces acting on particles, in complex geometries, is presented. Validation cases for the particle transport and fluid flow model are shown. Comparison is made with new smoke flow visualization data and particle deposition data. Turbulence anisotropy, fan swirl, and flow unsteadiness are shown to significantly affect particle paths as does the choice of isotropic turbulence model. For lighter particles, electrostatic forces are found to have less effect. Results suggest, centrifugal forces, arising from regions of strong streamline curvature, play a key particle deposition role. They also indicate that weaknesses in conventional eddy viscosity based turbulence models make the accurate prediction of complex geometry particle deposition a difficult task. Axial fans are found in many fluid systems. The sensitivity of results to their modeling suggests caution should be used when making predictions involving fans and that more numerical characterization studies for them could be carried out (especially when considering particle deposition). Overall, the work suggests that, for many complex - engineering systems, at best (without excessive model calibration time), only qualitative particle deposition information can be gained from numerical predictions.

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