Exploring soot inception rate with stochastic modelling and machine learning
Bibliographic record
Abstract
<p>A diverse range of polycyclic <a href="https://www.sciencedirect.com/topics/chemical-engineering/aromatic-compound" target="_blank">aromatic compounds</a> (PACs) is thought to exist in flame environments before and during soot inception. This work seeks to develop a machine learning (ML)-based soot inception model that considers detailed and diverse PAC properties such as <a href="https://www.sciencedirect.com/topics/chemical-engineering/oxygenation" target="_blank">oxygenation</a>, aliphatic content, radical character, size, and shape. To this end, temporal rates of change of PAC properties were computed by the stochastic modelling code SNapS2 and used as input to an ML model that predicts soot inception rate. The model is trained using experimentally-derived soot inception rates for three atmospheric pressure laminar premixed ethylene/air flames. An ML model (kernel ridge regression with a linear kernel) was developed to predict the soot inception rate in the three <a href="https://www.sciencedirect.com/topics/engineering/premixed-flame" target="_blank">premixed flames</a>. The soot inception rate predictions from this SNapS2-informed ML model outperformed the predictions from both the advanced soot <a href="https://www.sciencedirect.com/topics/engineering/computational-fluid-dynamic-modeling" target="_blank">modelling CFD</a> code CoFlame and an ML model which used CFD-determined inputs (temperature and species concentrations). The final model had an R^2 value of approximately 0.71 and a <a href="https://www.sciencedirect.com/topics/engineering/mean-absolute-error" target="_blank">mean absolute error</a> approximately 25% of the target values. The performance of the SNapS2-informed model suggests that detailed PAC properties are important to consider in inception modelling. While expanding this approach to other types of flames and fuels is crucial for future improvement to the model’s accuracy and generality, this methodology provides a successful framework for the current system. The success of this method demonstrates that ML can offer improvements in accuracy compared to current <a href="https://www.sciencedirect.com/topics/engineering/computational-fluid-dynamics" target="_blank">CFD</a> inception models and the highlights the potential for ML in soot predictions.</p>
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".