Numerical Study of Temperature and Incandescence Intensity of Nanosecond Pulsed-Laser Heated Soot Particles at High Pressures
Bibliographic record
Abstract
Histories of temperature and incandescence intensity of nanosecond pulsed-laser heated soot particles of polydispersed primary particles and aggregate sizes were calculated using an aggregate-based heat transfer model at pressures from 1 atm up to 50 atm. The local gas temperature, distributions of soot primary particle diameter and aggregate size assumed in the calculations were similar to those found in an atmospheric laminar diffusion flame. Relatively low laser fluences were considered to keep the peak particle temperatures below about 3400 K to ensure negligible soot particle sublimation. The shielding effect on the heat conduction between aggregated soot particles and the surrounding gas was accounted for based on results of direct simulation Monte Carlo calculations. After the laser pulse, the temperature of soot particles with larger primary particles or larger aggregates cools down slower than those with smaller primary particles or smaller aggregates due to smaller surface area-to-volume ratios. The effective temperature of soot particles in the laser probe volume was calculated based on the ratio of thermal radiation intensities of the soot particle ensemble at 400 and 780 nm. Due to the reduced mean free path of molecules with increasing pressure, the heat conduction between soot particles and the surrounding gas shifts from the free-molecular to the transition regime. Consequently, the rate of conduction heat loss from the soot particles increases significantly with pressure. The lifetime of laser-induced incandescence (LII) signal is significantly reduced as the pressure increases. At high pressures, the time resolved soot particle temperature is very sensitive to both the primary particle diameter and the aggregate size distributions, implying the time-resolved LII particle sizing techniques developed at atmospheric pressure lose their effectiveness at high pressures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".