The influence of plug nozzle and Laval nozzle on the flow field and performance of non-premixed rotating detonation combustor
Bibliographic record
Abstract
With the rotating detonation engine's (RDE) development to engineering applications, the selection and optimization of nozzle is garnering great concerns, with the aim to maximize the performance benefits of this pressure gain propulsion system. The present study represents the first effort to explore the distinct impacts of two commonly used nozzles in RDE, namely, the plug nozzle and the Laval nozzle, on the internal flow and performance within the combustion chamber. Three-dimensional numerical simulations are conducted on non-premixed annular RDEs with plug nozzles and Laval nozzles. It is found that the Laval nozzle induces a forward-leaning wavefront structure in the combustion chamber. Furthermore, the overall pressure gain of the RDE is divided into the injection pressure loss, the average pressure gain at the chamber bottom, and the flow losses downstream, by combining the wavefront coordinate averaged flow field, which is proposed and applied in this study, and laboratory coordinate averaged flow field. The results show that, for the performance of the combustion chamber, while Laval nozzles enhance pressure gains at the chamber bottom and reduce exit flow non-uniformity, they also increase downstream losses. By comparing the RDE performance with the ideal performance of deflagration-based combustors, it is found that the premixed control group exceeded the deflagration ideal performance by 30%. Despite lower combustion efficiency, non-premixed configurations nearly match the ideal deflagration performance, underscoring the inherent advantages of RDEs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".