Tabulated chemistry approaches for laminar flames: Evaluation of flame-prolongation of ILDM and flamelet methods
Bibliographic record
Abstract
The present study considers the performance of tabulation methods for numerical simulation of complex chemical kinetics in laminar combusting flows and compares their predictions to results obtained by direct calculation. Two tabulation methods are considered: the Flame Prolongation of Intrinsic low-dimensional manifold (FPI) method and Steady Laminar Flamelet Model (SLFM). The FPI method is of current interest as it is a potentially unifying approach capable of dealing with both premixed and non-premixed flames for gaseous fuels. SLFM tabulation methods are popular for non-premixed flames and form a good basis for comparing the performance of the FPI approach. The performance of each method is also evaluated by comparing the results to the direct simulation of the laminar flames using two chemical kinetic schemes: simplified chemistry involving five species and one reaction and detailed chemistry involving 53 species and 325 reaction steps. As part of the evaluation process, the computational cost of each method is also assessed. The laminar flames considered in this study include: freely propagating laminar premixed flames, a two-dimensional axisymmetric methane–air opposed-jet diffusion flame, and a two-dimensional axisymmetric methane–air co-flow diffusion flame. Both tabulation methods are implemented in a parallel adaptive mesh refinement (AMR) framework for solving the complete set of governing partial differential equations. These equations are solved using a fully-coupled finite-volume formulation on body-fitted multi-block quadrilateral mesh. Significant improvements in terms of reduced computational requirements, as measured by both storage and processing time, are demonstrated for the tabulated methods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".