Large eddy simulation of spark ignition of a bluff-body stabilized burner using a subgrid-ignition model coupled with FGM-based combustion models
Bibliographic record
Abstract
Purpose Safety improvement and pollutant reduction in many practical combustion systems and especially in aero-gas turbine engines require an adequate understanding of flame ignition and stabilization mechanisms. Improved software and hardware have opened up greater possibilities for translating basic knowledge and the results of experiments into better designs. The present study deals with the large eddy simulation (LES) of an ignition sequence in a conical shaped bluff-body stabilized burner involving a turbulent non-premixed flame. The purpose of this paper is to investigate the impact of spark location on ignition success. Particular attention is paid to the ease of handling of the numerical tool, the computational cost and the accuracy of the results. Design/methodology/approach The discrete particle ignition kernel (DPIK) model is used to capture the ignition kernel dynamics in its early stage of growth after the breakdown period. The ignition model is coupled with two combustion models based on the mixture fraction-progress variable formulation. An infinitely fast chemistry assumption is first done, and the turbulent fluctuations of the progress variable are captured with a bimodal probability density function (PDF) in the line of the Bray–Moss–Libby (BML) model. Thereafter, a finite rate chemistry assumption is considered through the flamelet-generated manifold (FGM) method. In these two assumptions, the classical beta-PDF is used to model the temporal fluctuations of the mixture fraction in the turbulent flow. To model subgrid scale stresses and residual scalars fluxes, the wall-adapting local eddy (WALE) and the eddy diffusivity models are, respectively, used under the low-Mach number assumption. Findings Numerical results of velocity and mixing fields, as well as the ignition sequences, are validated through a comparison with their experimental counterparts. It is found that by coupling the DPIK model with each of the two combustion models implemented in a LES-based solver, the ignition event is reasonably predicted with further improvements provided by the finite rate chemistry assumption. Finally, the spark locations most likely to lead to a complete ignition of the burner are found to be around the shear layer delimiting the central recirculation zone, owing to the presence of a mixture within flammability limits. Research limitations/implications Some discrepancies are found in the radial profiles of the radial velocity and consequently in those of the mixture fraction, owing to a mismatch of the radial velocity at the inlet section of the computational domain. Also, unlike FGM methods, the BML model predicts the overall ignition earlier than suggested by the experiment; this may be related to the overestimation of the reaction rate, especially in the zones such as flame holder wakes which feature high strain rate due to fuel-air mixing. Practical implications This work is adding a contribution for ignition modeling, which is a crucial issue in various combustion systems and especially in aircraft engines. The exclusive use of a commercial computational fluid dynamics (CFD) code widely used by combustion system manufacturers allows a direct application of this simulation approach to other configurations while keeping computing costs at an affordable level. Originality/value This study provides a robust and simple way to address some ignition issues in various spark ignition-based engines, namely, the optimization of engines ignition with affordable computational costs. Based on the promising results obtained in the current work, it would be relevant to extend this simulation approach to spray combustion that is required for aircraft engines because of storage volume constraints. From this standpoint, the simulation approach formulated in the present work is useful to engineers interested in optimizing the engines ignition at the design stage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".