Effects of Lewis number on conditional fluid velocity statistics in low Damköhler number turbulent premixed combustion: A direct numerical simulation analysis
Bibliographic record
Abstract
The effects of global Lewis number Le on the statistics of fluid velocity components conditional in unburned reactants and fully burned products in the context of Reynolds Averaged Navier Stokes simulations have been analysed using a Direct Numerical Simulations (DNS) database of statistically planar turbulent premixed flames with a low Damköhler number and Lewis number ranging from 0.34 to 1.2. The conditional velocity statistics extracted from DNS data have been analysed with respect to the well-known Bray-Moss-Libby (BML) expressions which were derived based on bi-modal probability density function of reaction progress variable for high Damköhler number flames. It has been shown that the Lewis number substantially affects the mean velocity and the velocity fluctuation correlation conditional in products, with the effect being particularly pronounced for low Le. As far as the mean velocity and the velocity fluctuation correlation conditional in reactants are concerned, the BML expressions agree reasonably well with the DNS data reported in the present work. Based on a priori analysis of present and previously reported DNS data, the BML expressions have been empirically modified here in order to account for Lewis number effects, and the non-bimodal distribution of reaction progress variable. Moreover, it has been demonstrated for the first time that surface averaged velocity components and Reynolds stresses conditional in unburned reactants can be modelled without invoking expressions involving the Lewis number, as these surface averaged conditional quantities remain approximately equal to their conditionally averaged counterparts in the unburned mixture.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".