A novel soot concentration field estimator applied to sooting ethylene/air laminar flames
Why this work is in the frame
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Bibliographic record
Abstract
Soot formation modeling, when incorporated into computational fluid dynamics of industrial devices, can be numerically prohibitive. Nonetheless, there remains a significant push to predict soot formation, so as to aid in environmentally sustainable design. The present work features a redesign of an inexpensive soot estimator, that has been developed and applied to laminar flames with great success. It is much more accurate and efficient than previous versions. The soot estimator consists of a library generated from validated sooting flame models, in which the Lagrangian histories of soot-containing fluid parcels are stored. The library is used in post-processing to estimate soot concentrations. For the first time, the estimator framework is used to predict the entire soot field. Also, important parameters to the estimator technique are analyzed. This work is conducted for nine different sooting ethylene/air coflow diffusion flames. The framework successfully predicts the entire soot field. When the data from many flames were combined into one library based on mixture fraction, temperature, and $H_2 $ histories, it could predict all flames with high accuracy. Finally, two scenarios were considered to assess the framework with an independent set of data, and the predictor presented very good accuracy in capturing soot formation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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