Multi-altitude performance evaluation of axisymmetric propulsive nozzles with high divergence angles
Bibliographic record
Abstract
Abstract Conventional De Laval nozzles reliably produce thrust, but their divergent section poses the major design challenge. The contour of a bell nozzle can be traced with several philosophies such as thrust optimization parabolas (TOP) or truncated ideal compressed (TIC) nozzles. The major difference between the two enunciated contours lies on transient effects in overexpanding operation. In this study, the performance of the two contours and exhaust plume structures are compared for several pressure ratios simulating the operating conditions of an ascending vehicles. A high divergent angle at the exit is imposed at design and the results are compared to those of a cone nozzle, isentropic and Quasi-1D cases, showing that contoured bell nozzles suffer specific impulse losses due to a thicker boundary layer. Nonetheless, a lower than expected average angle allows that, near design conditions, the contoured nozzles have a higher specific impulse then if an isentropic expansion occurred, with the entire flow exiting with the design exit angle. The exhaust plumes show close resemblance to literature description, with the Mach disk concavity relating to oblique shocks and their interaction within the nozzle. The two contours show similar performances, with the TIC nozzle overperforming the TOP nozzle in overexpanding conditions near the design point, but with neither ever surpassing the cone nozzle performance.
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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) | 0.001 | 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".