Modeling the Regression Rates of Protrusion-and-Lattice Hybrid Rocket Fuel Grains
Bibliographic record
Abstract
Hybrid rockets based on neat paraffin fuel grains often suffer from poor combustion efficiency due to fuel sloughing and propellant stratification. Fuel sloughing occurs when unburned fuel fragments detach because of the weak mechanical properties of paraffin, reducing thrust and risking motor failure. Embedding lattice structures within the fuel grain enhances structural integrity and mitigates sloughing. Stratification refers to the layering of fuel-rich and oxidizer-rich zones, resulting from the diffusion flame formed at the interface between the solid fuel and gaseous oxidizer that leads to incomplete combustion. To address this, prior studies have introduced passive mixing devices, such as protrusions, to enhance turbulent mixing and combustion efficiency. The first objective of this study is to develop a one-dimensional analytical model that predicts the regression rates of protrusion-and-lattice-augmented fuel grains, accounting for enhanced convective heat transfer induced by protrusions. The second objective is to evaluate the effect of lattice volume fraction on regression rates for values ranging from 5% to 25% and compare predictions with experimental data. Results indicate that increasing lattice volume fraction reduces regression rates, and the model accurately captures this trend, matching experimental data within the error margin for lattice volume fractions up to 10%.
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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 | 0.000 |
| 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".