Application of Tabulated Chemistry to Laminar Co-flow Diffusion Flames at Atmospheric and Elevated Pressures
Bibliographic record
Abstract
Numerical simulations of combustion can be very computationally expensive, particularly when combustion of the complex fuels frequently found in aircraft gas turbine engines is considered. The large chemical mechanisms required to simulate flames involving these fuels become computationally prohibitive when realistic combustion conditions are used. In order to render these simulations tractable, this thesis investigates chemistry tabulation techniques, which have the potential to significantly reduce the computational costs of simulating reacting flows. In this thesis, four different tabulation techniques will be assessed by applying the methods to the prediction of steady, laminar, co-flowflames. The tabulation techniques considered are the flame prolongation of intrinsic low dimensional manifold (FPI) method, the steady laminar flamelet method (SLFM), the flamelet/progress variable (FPV) method, and the radiative flame prolongation of intrinsic low dimensional manifold (RFPI) method. These techniques are all applied to ethylene flames at atmospheric pressure, methane flames at 5 atm and 10 atm, and Jet A surrogate flames at atmospheric pressure. The results are compared to those obtained for the same case obtained using detailed chemistry, both with and without low-Mach preconditioning. This comparison facilitates the discussion of the relative merits of the tabulation techniques in relation to each other and to detailed chemistry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".