International Scientific Journal

Thermal Science - Online First

External Links

online first only

Determining of geometrical characteristic parameters of particle fractal aggregates from light scattering measurement signals

Two kind of light scattering measurement methods, i.e. the forward light scattering measurement (FLSM) method and the angular light scattering measurement (ALSM) method, are applied to reconstruct the geometrical morphology of particle fractal aggregates. An improved Attractive and Repulsive Particle Swarm Optimization (IARPSO) algorithm is applied to reconstruct the geometrical structure of fractal aggregates. It has been confirmed to show better convergence properties than the original Particle Swarm Optimization (PSO) algorithm and the Attractive and Repulsive Particle Swarm Optimization (ARPSO) algorithm. Compared with the FLSM method, the ASLM method can obtain more accurate and robust results as the distribution of the fitness function value obtained by the ALSM method is more satisfactory. Meanwhile, the retrieval accuracy can be improved by increasing the number of measurement angles or the interval between adjacent measurement angles even when the random noises are added. All the conclusions have important guiding significance for the further study of the geometry reconstruction experiment of fractal aggregates.
PAPER REVISED: 2020-06-29
PAPER ACCEPTED: 2020-07-02
  1. Modest, M.F., The weighted-sum-of-gray-gases model for arbitrary solution methods in radiative transfer, Asme Transactions Journal of Heat Transfer,113:3(1991),3,pp. 650-656.
  2. Mishchenko, M.I., et al., Scattering, absorption, and emission of light by small particles. Cambridge university press.2002.
  3. Liu, L., et al., A study of radiative properties of fractal soot aggregates using the superposition T-matrix method, Journal of Quantitative Spectroscopy and Radiative Transfer,109(2008),15,pp. 2656-2663.
  4. He, Z.Z., et al., Investigation on the effect of fractal soot aggregation on radiative transfer in homogeneous gas-soot mixture using full-spectrum k-distribution method, Journal of Thermal Science and Technology,14(2019),1,pp. JTST0001-JTST0001.
  5. Kholghy, M.R., et al., Comparison of multiple diagnostic techniques to study soot formation and morphology in a diffusion flame, Combustion & Flame,176(2017),pp. 567-583.
  6. Gwaze, P., et al., Comparison of three methods of fractal analysis applied to soot aggregates from wood combustion, Journal of Aerosol Science,37(2006),7,pp. 820-838.
  7. Bescond, A., et al., Automated Determination of Aggregate Primary Particle Size Distribution by TEM Image Analysis: Application to Soot, Aerosol Science and Technology,48(2014),8,pp. 831-841.
  8. Caumontprim, C., et al., Measurement of aggregates' size distribution by angular light scattering, Journal of Quantitative Spectroscopy & Radiative Transfer,126(2013),S1,pp. 140-149.
  9. Sorensen, C., Light scattering by fractal aggregates: a review, Aerosol Science & Technology,35(2001),2,pp. 648-687.
  10. Steinmetz, S.A., et al., Soot particle size measurements in ethylene diffusion flames at elevated pressures, Combustion & Flame,169(2016),pp. 85-93.
  11. He, Z.Z., et al., Application of the steady-state and unsteady-state lasers in reconstructing the geometrical characteristic parameters of soot fractal aggregates, Optik-International Journal for Light and Electron Optics,202(2020),pp. 163720.
  12. Qi, H., et al., Inverse radiation analysis of a one-dimensional participating slab by stochastic particle swarm optimizer algorithm, International Journal of Thermal Sciences,46(2007),7,pp. 649-661.
  13. Riget, J. and Vesterstrøm, J.S., A diversity-guided particle swarm optimizer-the ARPSO, Report No.2002-02, University of Aarhus, Aarhus, Denmark, 2002
  14. Qi, H., et al., Inversion of particle size distribution by spectral extinction technique using the attractive and repulsive particle swarm optimization algorithm, Thermal Science,19(2015),6,pp. 2151-2160.
  15. Ren, Y.T., et al., Simultaneous retrieval of the complex refractive index and particle size distribution, Opt. Express, 23(2015),15,pp. 19328-19337.
  16. Zhang, B., et al., Solving inverse problems of radiative heat transfer and phase change in semitransparent medium by using Improved Quantum Particle Swarm Optimization, International Journal of Heat and Mass Transfer, 85(2015),pp. 300-310.
  17. Härdle, W.K. and Simar, L., Applied Multivariate Statistical Analysis, Technometrics,47(2005),4,pp. 15 517-517.