Development and testing of a soot particle concentration estimator using Lagrangian post-processing
Why this work is in the frame
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Bibliographic record
Abstract
Soot emissions from combustion devices are known to have harmful effects on the environment and human health. As the transportation industry continues to expand, the development of techniques to reduce soot emissions remains a significant goal of researchers and industry. In order for current soot modeling techniques to be reliably accurate, they must incur an intractably high computational cost. This project leverages existing knowledge in soot modeling and soot formation fundamentals to develop a stand-alone, computationally inexpensive soot concentration estimator to be linked to Computational Fluid Dynamics simulations as a post-processor. Preliminary development and testing of the estimator is presented here for laminar flames. As soot properties cannot be determined by local conditions, the estimator consists of a library generated using the hystereses of soot-containing fluid parcels, which relates soot concentration to the aggregated gas-phase environment histories to which a fluid parcel has been exposed. The estimator can be used to relate soot concentration to computed parcel hystereses through interpolation techniques. The estimator shows the potential ability to produce accurate results with very low computational cost in laminar coflow diffusion flames. Results also show that as flame data representing a broader set of conditions (temperature, mixture fraction, residence time, etc.) are added to the library, the estimator becomes applicable to a wider range of flames.
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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 | 0.000 |
| 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