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RESEARCH PAPERS: Radiative Heat Transfer

Photon Monte Carlo Simulation for Radiative Transfer in Gaseous Media Represented by Discrete Particle Fields

[+] Author and Article Information
Anquan Wang

Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA 16802

Michael F. Modest1

Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA 16802mfm6@psu.edu

1

To whom all correspondence should be addressed.

J. Heat Transfer 128(10), 1041-1049 (Mar 21, 2006) (9 pages) doi:10.1115/1.2345431 History: Received October 03, 2005; Revised March 21, 2006

Monte Carlo ray-tracing schemes have been developed for the evaluation of radiative heat transfer for problems, in which the participating medium is represented by discrete point masses, such as the flow field and scalar fields in PDF Monte Carlo methods frequently used in combustion modeling. Photon ray tracing in such cases requires that an optical thickness is assigned to each of the point masses. Two approaches are discussed, the point particle model (PPM), in which the shape of particle is not specified, and the spherical particle model (SPM) in which particles are assumed to be spheres with specified radiation properties across their volumes. Another issue for ray tracing in particle fields is the influence region of a ray. Two ways of modeling a ray are proposed. In the first, each ray is treated as a standard volume-less line. In the other approach, the ray is assigned a small solid angle, and is thus treated as a cone with a decaying influence function away from its centerline. Based on these models, three different interaction schemes between rays and particles are proposed, i.e., line-SPM, cone-PPM and cone-SPM methods, and are compared employing several test problems.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 8

2D CDS representation with linear mass distribution for a homogeneous medium

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Figure 7

Averaged boundary flux error versus number of particles in computation domain; cone-PPM scheme with one ray per particle; 50×N particles; uniform particle mass; Case (1)

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Figure 6

Averaged boundary flux error versus number of rays per particle; cone-PPM scheme; 50×10,000 particles; uniform particle mass; Case (1)

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Figure 5

Computational time at different particle number densities; 50×10,000 equally sized particles; 1ray∕particle; homogeneous medium

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Figure 4

Figure of merit (FoM) of cone ray models at different cone opening angles; 50×10,000 equally sized particles; 1ray∕particle; homogeneous medium

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Figure 3

(a) Cone-PPM scheme; (b) Cone-SPM scheme

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Figure 2

Relative density distribution on a cross section of a 3D CDS representation for a homogeneous medium

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Figure 1

(a) PPM representation of a 2D medium; (b) SPM representation of a sub-region in (a)

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