Sensitivity of key Simulation Parameters on Flame Propagation in Obstructed Chamber: Effect of XiFOAM Discretization Schemes
Bibliographic record
Abstract
This paper uses the OpenFOAM Computational Fluid Dynamics (CFD) code to study the turbulent premixed flame propagation characteristics inside a partially open duct filled with obstacles.The simulations were performed using a twodimensional model with realizable k-ε turbulence modelling and Flame Surface Density (FSD) model proposed by Weller et al. for Combustion modelling.The solver uses adaptive time stepping method coupled with a maximum value of the Courant number.Initially the simulations were carried out with first order upwind scheme for divergence terms, second order Crank Nicolson method for time discretization and PIMPLE solver (with outer correctors set to 200 with residual for outer correctors set to 10 -4 ) for pressure-velocity coupling.The solution with these schemes resulted in impractical dependence of overpressure peak on the initial values of simulation parameters: turbulent kinetic energy 'k', initial time step size 'Δt', mesh size 'Δx' as well as maximum value for Courant number of the flow 'maxCo'.The k values tested are 0.5, 0.1, 0.05 and 0.01, as at 0.01 the pressure peak was negligible and far delayed.Similar results have been obtained for above mentioned parameters.The discretization schemes were updated to a second order linear scheme for divergence terms and a first order Euler method for temporal terms.The pressure velocity coupling was updated to iterative PISO algorithm (PIMPLE in OpenFOAM, with outer correctors of three).The updated solver was then tested against the experimental results to analyse the dependence of pressure peak on the above-mentioned simulation parameters.It was found that the unexpected dependence on all the parameters was eliminated and the solver provided reasonably good qualitative agreement with the experimental results.Effect of each of the discretization schemes is also tested individually.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".