Analytical Modeling of Regression Rates in Lattice-Protrusion Augmented Hybrid Rocket Fuels
Bibliographic record
Abstract
Fuel sloughing, the detachment of un-combusted fuel fragments, poses a significant challenge in paraffin wax-based hybrid rockets, potentially leading to motor failure. The current work aims to reduce sloughing by improving the mechanical properties of the fuel by embedding 3D-printed PLA lattices within the fuel grain. Protrusions made from thermal shock-resistant materials are also incorporated into the fuel grain to enhance regression rates by creating recirculation flows. A one-dimensional analytical model is developed to predict the regression rates of the lattice-augmented fuel grain with protrusion. The developed model is validated through experimental investigations using a slab burner. Regression rates of lattice-augmented fuel grains were measured with lattice volume fractions ranging from 5% to 25%. The results reveal that the model performs reasonably well at predicting the modest decrease in regression rate with increasing lattice volume fraction.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".