Effects of Detection Wavelengths on Soot Volume Fraction Measurements Using the Auto-Compensating LII Technique
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Bibliographic record
Abstract
Soot particles in the detection volume in general have different temperatures due to non-uniform laser fluence and particle size distribution. The thermal radiation intensity displays different temperature dependence at different wavelengths. The effective soot temperature inferred from the ratio of laser-induced incandescence (LII) signals in the auto-compensating LII (AC-LII) technique is dependent on the detection wavelengths and affects the measured soot volume fraction. This paper numerically investigates the effects of detection wavelengths on the inferred soot effective temperature and volume fraction under conditions relevant to laminar diffusion flames at atmospheric pressure and for three representative laser fluence distributions. Numerical calculations were conducted using an LII model for a laser pulse of 5.8 ns FWHM and 1064 nm and assuming a lognormal primary particle size distribution and neglecting the aggregation effect. LII signals were modeled at four wavelength bands centered at 420, 560, 680, and 790 nm and the effective soot temperature was derived over three pairs of LII signals, namely [420, 560 nm], [560, 680 nm], and [680, 790 nm]. The two shortest and two longest detection wavelengths respectively result in the highest and lowest effective soot temperature when the laser fluence is non-uniform. The inferred soot volume fraction displays the opposite trend as the effective soot temperature. The effective soot temperature is biased toward the highest values and AC-LII always underestimates the soot volume fraction. The detection wavelengths should be carefully selected to minimize the impact of non-uniform laser fluence and at the same time to maximize the accuracy of effective soot temperature.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it