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Optimal Design of Sheath Flow Nozzle Acceleration Section for Improving the Focusing Efficiency

집속효율 향상을 위한 외장유동노즐 가속 구간의 최적설계 연구

  • Lee, Jin-Woo (Graduate School of Mechanical Engineering, Sungkyunkwan University) ;
  • Jin, Joung-Min (Graduate School of Mechanical Engineering, Sungkyunkwan University) ;
  • Kim, Youn-Jea (School of Mechanical Engineering, Sungkyunkwan University)
  • 이진우 (성균관대학교 일반대학원 기계공학과) ;
  • 진정민 (성균관대학교 일반대학원 기계공학과) ;
  • 김윤제 (성균관대학교 기계공학부)
  • Received : 2019.07.05
  • Accepted : 2019.10.11
  • Published : 2019.12.05

Abstract

There is a need to use sheath flow nozzle to detect bioaerosol such as virus and bacteria due to their characteristics. In order to enhance the detection performance depending on nozzle parameters, numerical analysis was carried out using a commercial code, ANSYS CFX. Eulerian-lagrangian approach method is used in this simulation. Multiphase flow characteristics between primary fluid and solid were considered. The detection performance was evaluated based on the results of flow field in nozzle chamber such as focusing efficiency and swirl strength. In addition, Latin hypercube sampling(LHS) of design of experiment(DOE) was used for generating a near-random sampling. Then, the acceleration section is optimized using response surface method(RSM). Results show that the optimized model achieved a 6.13 % in a focusing efficiency and 11.47 % increase in swirl strength over the reference model.

Keywords

References

  1. Agranovski, V., Ristovski, Z., Hargreaves, M., Blackall, P. J., and Morawska, L. "Real-time Measurement of Bacterial Aerosols with the UVAPS: Performance Evaluation," Journal of Aerosol Science, Vol. 34, No. 3, pp. 301-317, 2003. https://doi.org/10.1016/S0021-8502(02)00181-7
  2. Bridgeman, J., Baker, A., Brown, D., and Boxall, J. B., "Portable LED Fluorescence Instrumentation for the Rapid Assessment of Potable Water Quality," Science of the Total Environment, 524, 338-346, 2015. https://doi.org/10.1016/j.scitotenv.2015.04.050
  3. Kim, K. H., Jahan, S. A., and Kabir, E., "A review on Human Health Perspective of Air Pollution with Respect to Allergies and Asthma," Environment International, 59, 41-52, 2013. https://doi.org/10.1016/j.envint.2013.05.007
  4. Cabalo, J. B., Sickenberger, R., Underwood, W. J., and Sickenberger, D. W., "Micro-UV Detector," Optically Based Biological and Chemical Sensing for Defence, International Society for Optics and Photonics, Vol. 5617, pp. 75-86, 2004. https://doi.org/10.1117/12.569260
  5. Pan, Y. L., Bowersett J., Hill, S. C., Pinnick, R. G. and Chang, R. K., "Nozzle for Focusing Aerosol Particles," ARL-TR-5026, U.S. Army Research Laboratory, 2009.
  6. Song, I. Y., Choi, S. K. and Kim, Y. J., "Optimal Design of a Sheath Flow Nozzle for Detecting Biological Warfare Agents," Journal of Nanoscience and Nanotechnology, Vol. 17, pp. 8360-8364, 2017. https://doi.org/10.1166/jnn.2017.15135
  7. Pan, Y. L., Kalume, A., Wang, C., & Santarpia, J. L., "Opto-aerodynamic Focusing of Aerosol Particles," Aerosol Science and Technology 52.1, 13-18, 2018. https://doi.org/10.1080/02786826.2017.1367090
  8. Lee, H., Jeong, Y. S., Choi, K., & Shin, W. G., "The Effect of Sheath Flow Rate on the Particle Trajectory Inside an Optical Cavity with Direct Flow Configuration," Journal of Aerosol Science, 114, 146-156, 2017. https://doi.org/10.1016/j.jaerosci.2017.09.016
  9. Choi, H., Kang, S., Jung, W., Jung, Y. H., Park, S. J., Kim, D. S., and Choi, M., "Controlled Electrostatic Focusing of Charged Aerosol Nanoparticles Via an Electrified Mask," Journal of Aerosol Science, 88, 90-97, 2015. https://doi.org/10.1016/j.jaerosci.2015.05.017
  10. Jin, J. M., Lee, J. W., Kang, M. S., K, H. C., and Kim, Y. J., "Effects of Sheath Flow Nozzle Shape on the Focusing Efficiency of Aerosol Particles," The KSFM Journal of Fluid Machinery, Vol. 22, No. 4, pp. 13-18, 2019.
  11. TANAKA, Z., and IINOYA, K., "New Approximate Equation of Drag Coefficient for Spherical Particles," Journal of Chemical Engineering of Japan, Vol. 3, No. 2, pp. 261-262, 1970. https://doi.org/10.1252/jcej.3.261
  12. ANSYS Help Version 18.1, 2018, Ansys Inc.
  13. Wang, G., & Bachalo, W. D., "The Effect of Swirl on the Velocity and Turbulence Fields of a Liquid Spray," Journal of Engineering for Gas Turbines and Power, Vol. 114, No. 1, pp. 72-81, 1992. https://doi.org/10.1115/1.2906309
  14. Ibrahim, A. A., and Jog, M. A., "Effect of Liquid and Air Swirl Strength and Relative Rotational Direction on the Instability of an Annular Liquid Sheet," Acta Mechanica 186.1-4, pp. 113-133, 2006. https://doi.org/10.1007/s00707-006-0368-x
  15. Fisher, R. A., "Design of Experiments," Br Med J, Vol. 1, No. 3923, pp. 554-554, 1936. https://doi.org/10.1136/bmj.1.3923.554-a
  16. Stein, M., "Large Sample Properties of Simulations Using Latin Hypercube Sampling," Technometrics, Vol. 29, No. 2, pp. 143-151, 1987. https://doi.org/10.1080/00401706.1987.10488205
  17. Kim, H. J., "Extended Central Composite Designs with the Axial Points Indicated by Two Numbers," The Korean Communications in Statistics, Vol. 9, pp. 595-605, 2002.
  18. Box, G. E. P. and Wilson, K. B., "On the Experimental Attainment of Optimum Conditions," Journal of the Royal Statistical Society B (Methodological), Vol. 13, pp. 1-45, 1951. https://doi.org/10.1111/j.2517-6161.1951.tb00067.x
  19. Basheer, I. A., and Hajmeer, M., "Artificial Neural Networks: Fundamentals, Computing, Design, and Application", Journal of Microbiological Methods, Vol. 43, No. 1, pp. 3-31, 2000. https://doi.org/10.1016/S0167-7012(00)00201-3
  20. Fonseca, C. M., and Fleming, P. J., "Genetic Algorithms for Multiobjective Optimization: Formulation Discussion and Generalization," Icga Vol. 93, pp. 416-423, 1993.
  21. Jo, Y. M., and Choi, S. I., "Shape Optimization of UCAV for Aerodynamic Performance Improvement and Radar Cross Section Reduction," Journal of Computational Fluids Engineering, Vol. 17, No. 4, pp. 56-68, 2012 https://doi.org/10.6112/kscfe.2012.17.4.056
  22. Cortes, O., Urquiza G. and Hernandez, J. A., "Optimization of Operating Conditions for Compressor Performance by Means of Neural Network Inverse," Applied Energy, Vol. 86, No. 11, pp. 2487-2493. 2009. https://doi.org/10.1016/j.apenergy.2009.03.001
  23. Kotani, M., Matsumoto H. and Kanagawa T., "Acoustic Diagnosis for Compressor with Hybrid Neural Network," IJCNN-91-Seattle International Joint Conference, Vol. 1, pp. 251-256, 1991.