Numerical Simulation through Fluent Of a Cold, Swirling Particle Flow in a Combustion Chamber
Bibliographic record
Abstract
The study aims to first model the cold, confined and swirling flow in a magnesium burner. One of the most important features of the experimental burner is the presence of a significant recirculation zone, which is crucial for stabilizing the flame in the combustion chamber, Before modelling the combustion reaction and the flame, a first step is the simulation of the cold monophasic airflow and the accurate simulation of the recirculation zone. To validate the numerical simulations performed with the ANSYS Fluent software, experimental velocity measurements were first made in a 1:1 scale PMMA replica of the experimental burner. A constant temperature hot-wire anemometer was used to determine radial profiles of axial velocity. A low swirl case (S=0.13) was first considered because of its apparent simplicity. Simulation results obtained using different eddy viscosity models were compared to the experimental data and the Standard k- model proved to predict the velocity profiles and the central recirculation zone with the most accuracy. A high swirl case was then studied (S=2.94), corresponding to the conditions occurring in the experimental burner. In this case, the turbulence and its anisotropy appeared to be too strong for the eddy viscosity models previously used, and they failed to provide a converging solution. The RSM model was better suited for the task and could predict the position, size and shape of the central toroidal recirculation zone with acceptable accuracy, although important errors were still observed for the velocity values. Part of the inaccuracies could be explained by the usage of first-order discretization schemes. Higher discretization orders (second or higher order) could not be used in this case due to induced numerical instabilities, which could not be reduced despite several attempts,
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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 | 0.000 |
| 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".