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%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".