Analysis on Propulsive Performance of Hollow Rotating Detonation Engine with Laval Nozzle
Bibliographic record
Abstract
In the present study, an experimental performance analysis of hollow rotating detonation engines (RDEs) with Laval nozzles is carried out for the first time. Experiments of a hollow rotating detonation engine with a Laval nozzle were performed with a modular RDE at a backpressure condition of 1 atm. Two configurations with area ratios of the outlet throat to the inlet of [Formula: see text] and 2.7 have been tested with gaseous methane/oxygen as propellants. Three normalized metrics, usually used for evaluating the performance of conventional rocket engines, are introduced to analyze the performance deficit between the measured value of an RDE and the ideal value of an isobaric-combustion-based engine. These metrics allow for assessing the entire engine and each component separately. The metric analysis suggests a small outlet-to-inlet area ratio ([Formula: see text]) is detrimental to the propulsive performance. To explain the mechanism, a gas-stratification flowfield model is further proposed. It is found that the unchoked region in the combustible gas layer, which is caused by unchoked injection on the injecting plate, is responsible for the performance deficit of the combustion chamber. This model is then validated by one-dimensional numerical simulations and experimental data. In addition, we also focus on the global performance, including the gross thrust, the specific impulse, and the utilization of the supplied stagnation pressure. The result implies a tradeoff space when choosing an appropriate [Formula: see text].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".