Fuel regression rate estimation model for swirling-flow hybrid rocket engines
Bibliographic record
Abstract
In the current study, an analytical model to estimate the fuel surface regression rate of hybrid rocket engines with head-end swirling flow oxidizer injection is established. The model is based on a convective heat feedback approach and, in conjunction with the corresponding boundary layer (or zone) concept which accounts for transpiration, effective hydraulic diameters, and wall friction. The effective tangential (swirl) velocity of the gas provides a positive augmentation effect to the fuel regression rate, above that due to the axial mass flux component of the core gas flow. 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 results provided in this report for comparative purposes. The predicted fuel regression rates for the most part compare quite well with the corresponding experimental data. Additionally, the validity of the underlying assumption of a slowly decaying effective axial and tangential velocity of the gas as one moves downstream along the central fuel port is to some degree verified using a computational approach, based on a simplified engine flow model. As a final element of the overall study, the fuel regression rate model is evaluated for parameter sensitivity. The settings for some propellant and gas properties are found to have a significant influence on the quantitative predictive results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".