Laser-Induced Incandescence and Complementary Diagnostics for Flame Soot Characterization

摘要: This work has been aimed at developing and applying laser-induced incandescence (LII) for flame soot characterization. The basic principle of LII is rapid heating the particles to temperatures 3500-4000 K by short laser pulses. Thereby intensity increased. By detection this increased analysis detected signal, volume fraction, particle size optical properties can be evaluated. Additionally, both probing techniques have utilized in combination with studies specific properties. LII applied characterization different laboratory flames interest studies. distribution flat premixed ethylene/air on McKenna burners was found deviate somewhat from predicted one-dimensional behavior, where no variation supposed seen radially. A also depending choice co-flow gas. partially burning vaporized n-heptane n-decane diluted unstrained CH4/O2 diffusion were characterized terms fraction distributions. As ageing process followed as height above burner (HAB) flames, these measurement targets formation. Significant differences between newly formed nascent more mature soot. signal response nucleated low-sooting what commonly seen. Instead displaying an S-shaped fluence curve (signal vs. energy) showed almost linear trend. Even though results are challenging interpret, they show potential a diagnostic technique investigations particles. Evaluation absorption function, E(m), significant increase maturity, approaching nearly constant value ~0.35 similar trend when combining elastic light scattering measuring sublimation threshold i.e. onset sublimation. evaluated temperature maturity reach essentially ~3400 soot. When studying processes affecting decay time time-resolved signals, increasing level aggregation time. plausible explanation aggregate shielding effect, effectively decreasing heat conduction rate rotational coherent anti-Stokes Raman spectroscopy, local gas effect could measured. ~100 4 ppm ~2000 K, signal. If not accounting effects signals e.g. evaluations, will lead over prediction sizes.