Detonation Physics-Based Modelling & Design of a Rotating Detonation Engine
Bibliographic record
Abstract
A rotating detonation engine (RDE) is a new, more thermodynamically efficient, propulsion concept that replaces the traditional constant pressure combustion mechanism found in all currently used rockets and power generation devices. The constant pressure combustion is replaced by a detonation wave: a coupled shock-flame complex propagating at speeds of up to 2-3 km/s and generating combustion products at pressures 5-10 times the initial reactant pressure. This pressure gain through the combustion process leads to more compact, simpler devices that no longer require (or depend less upon) initial reactant precompression. Detonation-based cycles also have the added advantage of being theoretically more thermodynamically efficient than their constant pressure combustion counterparts. As such, RDEs have become increasingly popular in the propulsion research community, although there is still a lack of understanding in the underlying physics which govern their operability, though the existence of a minimum mass flow rate limit for stable operation has been observed. To help engineers and researchers design an RDE, a model was developed which combines geometric properties, 1D isentropic flow, and detonation physics to predict the stable operating bounds of an RDE. An engine testing facility was also constructed in collaboration with McGill University to test RDEs and confirm the performance of the prediction model developed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.005 | 0.001 |
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".