Turbulent premixed combustion simulation with Conditional Source-term Estimation and Linear-Eddy Model formulated PDF and SDR models
Bibliographic record
Abstract
Computational fluid dynamics (CFD) is indispensable in the development of complex engines due to its low cost and time requirement compared to experiments. Nevertheless, because of the strong coupling between turbulence and chemistry in premixed flames, the prediction of chemical reaction source terms continues to be a modelling challenge. This work focuses on the improvement of turbulent premixed combustion simulation strategies requiring the use of presumed probability density function (PDF) models. The study begins with the development of a new PDF model that includes the effect of turbulence, achieved by the implementation of the Linear-Eddy Model (LEM). Comparison with experimental burners reveals that the LEM PDF can capture the general PDF shapes for methane-air combustion under atmospheric conditions with greater accuracy than other presumed PDF models. The LEM is additionally used to formulate a new, pseudo-turbulent scalar dissipation rate (SDR) model. Conditional Source-term Estimation (CSE) is implemented in the Large Eddy Simulation (LES) of the Gülder burner as the closure model for the chemistry-turbulence interactions. To accommodate the increasingly parallel computational environments in clusters, the CSE combustion module has been parallelised and optimised. The CSE ensembles can now dynamically adapt to the changing flame distributions by shifting their spatial boundaries and are no longer confined to pre-allocated regions in the simulation domain. Further, the inversion calculation is now computed in parallel using a modified version of an established iterative solver, the Least-Square QR-factorisation (LSQR). The revised version of CSE demonstrates a significant reduction in computational requirement — a reduction of approximately 50% — while producing similar solutions as previous implementations. The LEM formulated PDF and SDR models are subsequently implemented in conjunction with the optimised version of CSE for the LES of a premixed methane-air flame operating in the thin reaction zone. Comparison with experimental measurements of temperature reveals that the LES results are very comparable in terms of the flame height and distribution. This outcome is encouraging as it appears that this work represents a significant step towards the correct direction in developing a complete combustion simulation strategy that can accurately predict flame characteristics in the absence of ad hoc parameters.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".