Parameterization of a Refined Model Aimed at Simulating Laser-Induced Incandescence of Soot Using a Visible Excitation Wavelength of 532 nm
Bibliographic record
Abstract
Laser-induced incandescence (LII) is one of the most powerful techniques for soot detection in combustion media. It is therefore commonly used to perform experiments in lab-scale flames and industrial combustors with a view to elucidating the formation mechanisms leading to combustion-generated fine carbonaceous particles while assessing their intrinsic properties. Quantitatively interpreting LII measurements, however, requires a firm knowledge of the optical properties of soot, including their wavelength-dependent absorption function (). Among the approaches used to evaluate such a crucial parameter, one can implement a LII model to derive the value which has to be set to reproduce a series of LII signals measured in a well-characterized environment. In this context, the present work aims at parameterizing a refined LII model built upon a comprehensive version of soot heat- and mass-balance equations for assessment when using a visible excitation wavelength of 532 nm. The proposed model integrates terms representing the saturation of linear, single- and multi-photon absorption processes, cooling by sublimation, conduction, radiation and thermionic emission, in addition to mechanisms depicting soot oxidation and annealing, non-thermal photodesorption of carbon clusters, as well as corrective factors accounting for the shielding effect and multiple scattering (MS) within aggregates. To parameterize this advanced simulation tool, an optimization procedure coupling design of experiments with a genetic algorithm-based solver was implemented. Doing so allowed to estimate the values of different factors involved in absorption and sublimation terms, including the multi-photon absorption cross-section for C2 photodesorption, the saturation coefficients for linear- and multi-photon absorption, as well as the value. Obtained parameters turned out to be well-suited to reproduce a set of LII signals acquired in a Diesel flame. While leading to predictions merging on a single curve with measured data, the so-parameterized model notably led to infer values of 0.3 and 0.38 when considering or neglecting MS within aggregates, respectively. Finally, the / ratio estimated based on data collected herein and in a former modeling work was found to be consistent with results issued from two-excitation wavelength LII measurements previously reported in the literature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".