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 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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".