Development of the Conditional Source-term Estimation (CSE) framework including finite-rate kinetics and non-prescribed radiation applied to a methanol pool fire
Bibliographic record
Abstract
The objective of this paper is to assess the new developments of Conditional Source-term Estimation (CSE) framework with improved radiation modeling in large eddy simulation applied to a medium-scale methanol pool fire. Tabulated detailed chemistry is included. Radiation is treated in two ways. The first is the optically thin approximation neglecting absorption. The second is through solution of the radiative transfer equation to account for absorption. The weighted-sum of gray-gases model is included in both cases and turbulence–radiation interactions are accounted for. Predictions of the time-averaged and root mean square temperature and velocity profiles, are compared with the experimental measurements at several locations. On the centerline, time-averaged temperature predictions agree well with the experiments except close to the pool where the temperatures are underpredicted. The predicted radial profiles are close to the experiments. The centerline species concentrations show a general agreement in trends, but there are some discrepancies especially towards the downstream regions of the fire. Sources of discrepancy are discussed. Overall, the different treatments of radiation show comparable predictions, but larger benefits of the current CSE framework are expected for more complex scenarios. Novelty and Significance Statement The novelty of the present study is the development of a combined turbulent combustion modeling framework, Conditional Source-term Estimation (CSE), including detailed chemical kinetics with advanced radiation models through solution of the radiative transfer equation. In this paper, absorption effects are included in the radiative fluxes, the optically thin assumption is discussed and turbulence–radiation interactions are accounted for. Inclusion of these coupled thermo-chemical–physical phenomena within CSE and using conditional averages in the context of pool fire simulation is a significant contribution. A rigorous validation is performed for a well-documented medium-scale methanol pool fire. This study lays the foundations for more complex fire scenarios of different scales, fuels and conditions.
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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 | 0.002 | 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".