Assessment of the predictive ability of standard and refined laser-induced incandescence models against experimental databases from the literature – A benchmarking analysis of commonly used modeling approaches
Bibliographic record
Abstract
Laser-induced incandescence (LII) has become a workhorse of particulate measurement. Interpreting measured signals properly, while inferring soot properties and/or physical parameters needed for signal simulations, however, requires developing modeling tools capable of predicting the radiative emission from laser-heated soot. Although significant effort has gone into gaining an in-depth understanding of the physical processes driving the LII phenomenon, the validity of current models, which are based on soot unsteady nanoscale heat and mass balances, is still subject to large uncertainties. The variability in the results from different simulation tools notably stems from their widely diverging formulations and parameterizations. Efforts must thus be directed at determining the critical energy and mass balance mechanisms, formulating the equations accounting for these mechanisms, estimating underlying parameters, and proposing adapted model validation protocols. To address these issues, the present work, which first proposes a detailed review of LII modeling approaches commonly used in the literature, aims at assessing the predictive capability of a series of LII simulation tools against various published datasets. Overall, 21 model formulations and 236 parameterizations were tested, and to the best of the authors’ knowledge, this benchmarking analysis ranks as the most comprehensive of its kind. This paper also includes sensitivity analyses focusing on the values and/or expressions used to represent the thermal and mass accommodation coefficients as well as the density, heat capacity and absorption properties of soot, while analyzing the impact of the formulation used to account for the annealing, oxidation, sublimation and thermionic emission processes. To conclude, the predictive capability of a comprehensive model integrating terms representing the saturation of linear, single- and multiphoton absorption processes, non-thermal photodesorption of carbon clusters and corrective factors accounting for the shielding effect and multiple scattering within aggregates, was evaluated against data collected in laminar and turbulent spray flames of gaseous and liquid fuels stabilized under both atmospheric and high-pressure conditions. Although this work does not set out to identify a model which should be considered as universally valid, it still contributes to highlighting the potential strengths and weaknesses of particular models and sub-models, depending on targeted applications, while proposing insights into how to parameterize them. The detailed analysis proposed should thus be of interest for the LII community, notably by paving the way for future experimental and modeling works to be undertaken in order to improve our understanding of the fundamental mechanisms at play during LII and determining the underlying parameters.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".