Large Eddy Simulations of a Medium-Scale Ethanol Pool Fire Using Conditional Source-Term Estimation Including Coupled Soot Radiation Effects
Bibliographic record
Abstract
The objective of this paper is to assess the combined capabilities of Conditional Source-Term Estimation with radiation and soot modeling in large eddy simulations of a medium-scale ethanol pool fire. Tabulated detailed chemistry is implemented including radiative losses. The radiative transfer equation is solved with the weighted sum of gray gas models to determine the gaseous absorption. Two subgrid soot models are considered using the laminar smoke point concept. The first approach calculates the soot formation and oxidation rates corrected to account for turbulent effects. The second determines the soot reaction rates in conditional space using analytical functions and the filtered reaction rates are determined by convolution with the filtered mixture fraction density function. Predictions of the time-averaged and root mean square temperature, species mole fractions and soot mass fraction are compared with experimental measurements. Numerical temperatures agree well with experiments except in the fire plume where some overpredictions are observed. Species concentrations match the measurements with some discrepancies observed in the products farther downstream, partly explained by the experimental uncertainty. The peak soot predicted with the first model is in reasonable agreement with the experiments but is much lower using the second approach. These differences are explained by the differences in the turbulence soot chemistry treatment and calibration of empirical constants. However, here, soot has a limited impact on the temperature, flow and mixing fields due to low soot concentrations produced by ethanol. The radiative heat fluxes are reasonably well predicted. Further validation is needed with additional experimental soot data.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".