Regression Rate Estimation for Swirling-Flow Hybrid Rocket Engines
Bibliographic record
Abstract
In the present study, an analytical model based on convective heat feedback is developed for the estimation of the solid fuel surface regression rate of hybrid rocket engines with head-end swirling-flow oxidizer injection. The convective heat transfer between the axial core flow and the burning fuel surface, coupled with the convective heat feedback between the effective tangential flow and the burning fuel surface, is the means by which the fuel regression rate is presumed to be increased by swirl, above that due to the axial mass flux. The representation of the effective boundary layers used in this study includes the influence of transpiration, effective hydraulic diameters (for flows in the axial and tangential direction), and fuel surface roughness. From the literature, a variety of propellant combinations, engine sizes, and flow swirl numbers are evaluated for engines having circular-port fuel grains, with sample model results provided. The predicted fuel regression rates for the most part compare quite well with the corresponding experimental data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".