Design by Large-Eddy Simulations of a Rich Burn – Quick Mix – Lean Burn Lab-Scale Combustion Chamber Operating in High-Pressure Conditions
Bibliographic record
Abstract
Abstract The Rich burn-Quick mix-Lean burn (RQL) concept is a promising staged combustion technology that ensures flame stability at all operating conditions and significantly reduces the pollutants concentration at the outlet of a combustion chamber. The current paper aims to detail the design of an optimized RQL lab-scale combustion module equipped with large optical accesses and a new-generation Injection System from the French engine manufacturer Safran Helicopter Engines. It is intended to study soot oxidation and NOx reduction processes under realistic high-pressure conditions up to 14 bar by simultaneously investigating the Rich, Quick-mix and Lean regions by laser-based diagnostics. The design of this module is first ensured by performing Large-Eddy Simulations of the reactive flow produced by a kerosene-vapor/air mixture with the AVBP solver. Various geometric configurations of this module were tested at the nominal regime to gradually improve its performance while seeking a suitable solution optimizing flow, mechanical and thermal constraints. The main versions are first presented to show the progress made to well separate the three regions of the RQL combustor while preserving an ability to perform a detailed optical investigation. A reactive two-phase flow LES with liquid fuel kerosene is finally performed to validate the adopted design of the RQL module.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".