Large eddy simulations of a buoyant turbulent line flame using conditional source-term estimation (CSE)
Bibliographic record
Abstract
The objective of this paper is to assess the capabilities of the Conditional Source-term Estimation (CSE) approach applied to a laboratory scaled turbulent buoyant flame without extinction. CSE is coupled with the Large Eddy Simulation (LES) solver, FireFOAM. Tabulated detailed chemistry is included. Radiation is treated using the optically thin model using four different implementations and the effect of subgrid scale (sgs) Turbulence-Radiation Interactions (TRI) is considered. Predictions of time-averaged temperatures and corresponding root mean square (rms) are compared with the experimental measurements at several locations. Further, flame shape, unconditional and conditional species mass fractions, axial velocity and mixture fraction are also examined for qualitative analysis. The predicted temperatures are in good agreement with the experimental data, except very close to the fuel inlet. The predicted temperatures increase more slowly compared to the experimental data in the first 0.09 m above the burner. The temperature rms is in reasonable agreement with experimental data with the peak being overpredicted by approximately 17%. The predicted flame height closely matches the experimental value. The temperature predictions are consistent with previously published results. Best predictions are obtained when the effect of sgs TRI is included. For this flame, the optically thin assumption is found to be valid, in agreement with previous investigations. The calculated radiative fraction is found to be close to the experimental value, but the postprocessed heat release rate is larger than the experimental finding. This first LES-CSE study provides a good foundation for further developments, for example more detailed radiation models, for more complex fire cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".