Different conditional source-term estimation formulations applied to turbulent nonpremixed jet flames with varying levels of extinction
Bibliographic record
Abstract
The objective of the present study is to investigate two new formulations of the Conditional Source-term Estimation (CSE) model using Reynolds Averaged Navier Stokes (RANS) calculations applied to Sandia flames D and F. The first method relies on a first-order Tikhonov regularisation and the second approach denoted by CSEBP, includes Bernstein polynomials to approximate the conditional averages. Current predictions for temperature, main product and minor species are consistent with previously published CSE results with a different implementation. However, smoother conditional profiles are obtained with less a priori information. Both formulations have good predictions for flame D with minor discrepancies near the inlet and one position downstream, with occasional small advantages for CSEBP. In contrast to previous RANS-CSE attempts, stable solutions are obtained for flame F in good agreement with the experiments. Considering the RANS and single conditioning limitations to capture transient effects, both formulations predict the changes of conditional averages and Favre averaged quantities from flame D to F well, except at one location where the predicted re-ignition occurs earlier than what is seen in the experiments. Additionally, the computational cost of the CSE routine is decreased significantly from 85% of the total computational cost to only 10% for the first formulation and under 3% for CSEBP by means of using hash tables for storing the results of interpolations from the chemistry tables and avoiding on-the-fly interpolations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".