Reducing the Ignition Delay of Hypergolic Hybrid Rocket Fuels
Bibliographic record
Abstract
Paraffin-based fuels incorporating solid amine boranes are investigated to identify formulations suitable for use as hypergolic hybrid rocket propellants. Their ignition delays are measured following contact with 90% white fuming nitric acid droplets. The mechanical properties of the paraffin binder and of the melt layer it produces are modified using an alpha-olefin polymer. The tests are carried out at atmospheric pressure, with visible flame light emission as well as chemiluminescence recorded. The ignition delays measured from both signals are nearly identical, confirming that emission begins at the same time as the visible light emission from the boron-containing additive mixed with the fuel. Identification of the different steps of the combustion process is done with a high-speed schlieren imaging technique. Consistently shorter ignition delays are obtained by increasing the proportion of polymerized alpha olefin. The effect of this addition on the viscosity of the melt layer produced in the fuel blend upon contact with the acid is inferred from rheological measurements realized on unburned samples. The effect of alpha-olefin addition on the theoretical thermochemical performance of the fuel is also computed. The results obtained confirm that polymer-based additives can be used to control mechanical and rheological properties in a way that lowers the ignition delays of hypergolic fuel systems based on paraffin, as required for use in space propulsion applications.
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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 | 0.000 |
| 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) | 0.001 | 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".