Regression Rates of Protrusion and Lattice Augmented Fuel Grains in Hybrid Rockets
Bibliographic record
Abstract
One of the challenges in paraffin-based hybrid rockets is the poor mechanical properties of wax, which can lead to sloughing and potential motor failure. Strategies to mitigate sloughing include embedding polymer lattices as structural reinforcement within the wax fuel grain. However, this approach reduces regression rates at low oxidizer mass flux. To counteract this, mixing devices such as protrusions can be incorporated to create recirculation zones downstream, enhancing species mixing and heat transfer to the fuel. However, the combined effect of protrusion and lattice reinforcement has not been reported previously. This study presents the first results on the regression behavior of fuel grains augmented with protrusions and lattice reinforcement. Regression rates are investigated for different configurations, including variations in lattice unit cells, reinforcement length, and protrusion material (mullite and silicon carbide [SiC]). The results show that at a lattice volume fraction of 7%, fuel grains with mullite and SiC protrusions achieved regression rates comparable to pure paraffin. This suggests that paraffin wax fuel grains can be designed to resist sloughing while maintaining the regression rate by integrating both structural reinforcement and mixing devices.
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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 | 0.000 | 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".