Modelling of Mixed-mode Combustion using Multiple Mapping Conditioning
Bibliographic record
Abstract
The aim of the present work is to investigate and develop the Multiple Mapping Conditioning (MMC) modelling framework in conjunction with LES into a reliable modelling tool for mixed-mode turbulent combustion. Recent studies show promising results using this approach for diffusion flames and it is important to expand its usability to more complex flame configurations involving finite-rate effects and mixed modes of combustion. In the first part of this thesis fundamental combustion modelling principles are presented followed by the description of the MMC model for turbulent combustion. From this point, this thesis develops a set of computational elements to produce a more general code using object-oriented programming. As a result of these improvements, the implementation of different combustion solvers can be achieved in a fast and intuitive way. In the second part of the work, the different models developed for the specific combustion modes are described and this is followed by the application to two test cases: (i) a set of partially premixed piloted methane jet flames with local extinction and re-ignition, (ii) mixed mode combustion using the Sydney Burner with Inhomogeneous inlets (Flame H and Flame I). Results from these simulations show the requirement to expand and improve the available models in the presence of mixed mode combustion. One of these requirements is density coupling where a new approach is introduced and tested with excellent improvements. A second need, is the introduction of a MMC model to handle premixed flames, to address the challenges of mixed-mode combustion. Here, A recently proposed modified shadow position reference variable for premixed combustion is used and its characteristics are demonstrated in 3D simulations of two Bunsen piloted jet configurations studied in Toronto and in Aachen. The findings of this work suggest that the newly expanded MMC model is an excellent approach to produce improved models able to describe more complex combustion regimes, while keeping simulations tractable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".