Benchmark Of Models Commonly Used To Simulate LaserInduced Incandescence Of Soot
Bibliographic record
Abstract
This paper compares the predictive ability of 4 models designed to simulate the laser-induced incandescence of soot.To that end, we considered 2 standard models integrating terms representing heating by absorption of laser energy and cooling by radiation, sublimation and conduction, together with two refined ones, which include mechanisms accounting for soot annealing and oxidation, saturation of linear, single-and multi-photon absorption processes, and cooling by thermionic emission.Predictions by these models were compared with signals measured in an ethylene diffusion flame.Sensitivity analyses focusing on the key parameters influencing the LII phenomenon were, moreover, conducted.As highlights, this work showed that standard models fail to properly simulate the fluence dependence of LII signals in the high fluence regime.More sophisticated models better reproduce LII fluence curves.Further work is required, however, to properly parameterize refined models to enable them to reproduce LII time decays over a wide range of operating conditions.The identification of parameters of interest, such as those involved in multi-photon absorption and nonthermal photodesorption processes, should help guide future works to be undertaken.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".