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Research Papers: Heat and Mass Transfer

Computation of Effective Thermal Conductivity of Powders for Selective Laser Sintering Simulations

[+] Author and Article Information
Daniel Moser

Department of Mechanical Engineering,
The University of Texas at Austin,
204 East Dean Keeton Street, Stop C2200,
ETC II 5.160,
Austin, TX 78712
e-mail: danrmoser@utexas.edu

Sreekanth Pannala

Corporate Research and Innovation,
SABIC Americas,
1600 Industrial Blvd.,
Sugar Land, TX 77478

Jayathi Murthy

Henry Samueli School of Engineering and
Applied Science,
University of California, Los Angeles,
7400 Boelter Hall,
Los Angeles, CA 90095

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received June 4, 2015; final manuscript received April 4, 2016; published online May 3, 2016. Assoc. Editor: Wilson K. S. Chiu.

J. Heat Transfer 138(8), 082002 (May 03, 2016) (9 pages) Paper No: HT-15-1397; doi: 10.1115/1.4033351 History: Received June 04, 2015; Revised April 04, 2016

In this work, a discrete element model (DEM) is developed and implemented in the open source flow solver MFiX to simulate the effective thermal conductivity of powder beds for selective laser sintering (SLS) applications, considering scenarios common in SLS such as thin beds, high temperatures, and degrees of powder consolidation. Random particle packing structures of spherical particles are generated and heat transfer between the particles is calculated. A particle–particle contact conduction model, a particle–fluid–particle conduction model, and a view factor radiation model using ray-tracing for calculation of view factors and assuming optically thick particles are used. A nonlinear solver is used to solve for the particle temperatures that drive the net heat transfer to zero for a steady state solution. The effective thermal conductivity is then calculated from the steady state temperature distribution. Results are compared against previously published experimental measurements for powder beds and good agreement is obtained. Results are developed for the impacts of very high temperatures, finite bed depth, consolidation, Young's modulus, emissivity, gas conductivity, and polydispersity on effective thermal conductivity. Emphasis is placed on uncertainty quantification in the predicted thermal conductivity resulting from uncertain inputs. This allows SLS practitioners to control the inputs to which the thermal response of the process is most sensitive.

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References

Figures

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Particle–particle conduction

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Particle–fluid–particle conduction

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Particle bed temperature profile

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Fig. 6

Comparison of model predictions with experimental results of Slavin et al. [7]

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Variation of effective thermal conductivity with temperature

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Variation of effective thermal conductivity with gas conductivity

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Fig. 9

Variation of effective thermal conductivity with Young's modulus

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Fig. 10

Variation of effective thermal conductivity with emissivity

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Variation of effective thermal conductivity with bed height

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Variation of effective thermal conductivity with particle size standard deviation

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