Journal of the Computational Structural Engineering Institute of Korea (한국전산구조공학회논문집)

Computational Structural Engineering Institute of Korea (한국전산구조공학회)
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Effects of Design Parameters of Mixer Blades on Particle Mixing Performance

혼합기 블레이드 설계변수에 따른 입자의 혼합성능 연구

  • Hwang, Seon-Pil (Department of Mechanical Engineering, Korea Maritime and Ocean University) ;
  • Park, Sanghyun (Department of Mechanical Engineering, Korea Maritime and Ocean University) ;
  • Sohn, Dongwoo (Department of Mechanical Engineering, Korea Maritime and Ocean University)
  • 황선필 (한국해양대학교 기계공학과) ;
  • 박상현 (한국해양대학교 기계공학과) ;
  • 손동우 (한국해양대학교 기계공학과)
  • Received : 2017.07.12
  • Accepted : 2017.07.20
  • Published : 2017.08.31
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Abstract

This paper is concerned with the evaluation of mixing performance of a particle mixer, which consists of a vertical cylindrical vessel and a rotating impeller with several blades. We consider four design variables for the mixer blades, such as the angle, length, and number of blades, and the gap between the blades and the vessel bottom. The particle mixing process due to the impeller rotation is simulated using the discrete element method, and the mixing performance is quantitatively evaluated by introducing a mixing index. Analyzing the main effects and interactions of the four design variables through the design-of-experiments approach, it is concluded that the blade angle has the most dominant influence on the mixing performance whereas the gap has no significant influence. In addition, we determine the best combination of design parameters to maximize the mixing performance.

본 논문에서는 원통형 혼합기를 대상으로 블레이드의 각도, 길이, 개수 및 블레이드와 탱크 바닥과의 간극을 설계변수로 선정하고, 각각의 설계변수가 혼합성능에 미치는 영향을 분석하였다. 이산요소법을 이용하여 임펠러 회전에 의한 고체 입자의 혼합공정을 해석하였으며, 혼합지수를 도입하여 혼합성능을 정량적으로 평가하였다. 다양한 설계변수의 조합을 고려한 실험계획법으로 설계변수의 주효과와 교호작용을 분석함으로써, 블레이드 각도가 입자의 혼합성능에 가장 지배적인 영향을 미치며 간극의 영향은 상대적으로 작다는 결론을 도출할 수 있었다. 또한 가장 우수한 혼합성능을 보이는 설계변수의 조합을 제시하였다.

Keywords

References

  1. Boonkanokwong, V., Remy, B., Khinast, J.G., Glasser, B.J. (2016) The Effect of the Number of Impeller Blades on Granular Flow in a Bladed Mixer, Powder Technol., 302, pp.333-349. https://doi.org/10.1016/j.powtec.2016.08.064
  2. Chandratilleke, G.R., Yu, A.B., Stewart, R.L., Bridgwater, J. (2009) Effect of Blade Rake Angle and Gap on Particle Mixing in a Cylindrical Mixer, Powder Technol., 193(3), pp.303-311. https://doi.org/10.1016/j.powtec.2009.03.007
  3. Cho, M. (2017) Particle-Mixing Simulations Using DEM and Comparison of the Performance of Mixing Indices, Trans. Korean Soc. Mech. Eng., B, 41(2), pp.145-152. https://doi.org/10.3795/KSME-B.2017.41.2.145
  4. Cho, M., Dutta, P., Shim, J. (2017) A Non-sampling Mixing Index for Multicomponent Mixtures, Powder Technol., 319, pp.434-444. https://doi.org/10.1016/j.powtec.2017.07.011
  5. Choi, Y., Choi, J., Kim, D., Hur, N. (2013) A Numerical Analysis on Mixing Performance for Various Types of Turbine Impeller in a Stirred Vessel, Korea Soc. Fluid Mach., 16(1), pp.47-55. https://doi.org/10.5293/kfma.2013.16.1.047
  6. Cleary, P.W., Sinnott, M.D. (2008) Assessing Mixing Characteristics of Particle-Mixing and Granulation Devices, Particuology, 6(6), pp.419-444. https://doi.org/10.1016/j.partic.2008.07.014
  7. Cundall, P.A., Strack, O.D.L. (1979) A Discrete Numerical Model for Granular Assemblies, Geotechnique, 29(1), pp.47-65. https://doi.org/10.1680/geot.1979.29.1.47
  8. Hassanpour, A., Tan, H., Bayly, A., Gopalkrishnan, P., Ng, B., Ghadiri, M. (2011) Analysis of Particle Motion in a Paddle Mixer using Discrete Element Method (DEM), Powder Technol., 206(1-2), pp.189-194. https://doi.org/10.1016/j.powtec.2010.07.025
  9. Jeong, J.-Y., Choi, E.-H., Kang, Y.-J., Noh, Y., Lim, O-K. (2017) Influence of Design Variables on Flow Characteristics of Poppet Valve Using Analysis of Means, J. Comput. Struct. Eng. Ins. Korea, 30(3), pp.239-248. https://doi.org/10.7734/COSEIK.2017.30.3.239
  10. Lee, S.-H., Yu, E. (2017) Model Updating of a RC Frame Building using Response Surface Method and Multiobjective Optimization, J. Comput. Struct. Eng. Ins. Korea, 30(1), pp.39-46. https://doi.org/10.7734/COSEIK.2017.30.1.39
  11. Nguyen, D.H., Kang, N., Park, J. (2013) Validation of Partially Flexible Rod Model Based on Discrete Element Method Using Beam Deflection and Vibration, Powder Technol., 237, pp.147-152. https://doi.org/10.1016/j.powtec.2013.01.038
  12. Pantaleev, S., Yordanova, S., Janda, A., Marigo, M., Ooi, J.Y. (2017) An Experimentally Validated DEM Study of Powder Mixing in a Paddle Blade Mixer, Powder Technol., 311, pp.287-302. https://doi.org/10.1016/j.powtec.2016.12.053
  13. Poux, M., Fayolle, P., Bertrand, J., Bridoux, D., Bousquet, J. (1991) Powder Mixing: Some Practical Rules Applied to Agitated Systems, Powder Technol., 68(3), pp.213-234. https://doi.org/10.1016/0032-5910(91)80047-M
  14. Rhodes, M.J. (2013) Introduction to Particle Technology, John Wiley & Sons, England, p.474.
  15. Song, G., Park, J. (2014) Discrete Element Method for Emergency Flow of Pedestrian in S-Type Corridor, J. Nanosci. & Nanotechnol., 14(10), pp.7469-7476. https://doi.org/10.1166/jnn.2014.9555
  16. Wen, Y., Liu, M., Liu, B., Shao, Y. (2015) Comparative Study on the Characterization Method of Particle Mixing Index Using DEM Method, Procedia. Eng., 102, pp.1630-1642. https://doi.org/10.1016/j.proeng.2015.01.299
  17. Zhang, H.W., Zhou, Q., Muhlhaus, H. (2011) A DEM Study on the Effective Thermal Conductivity of Granular Assemblies, Powder Technol., 205(1-3), pp.172-183. https://doi.org/10.1016/j.powtec.2010.09.008