A Primer for University-Level Solid Rocket Motor Research and Development
Bibliographic record
Abstract
The recent addition of multiple intercollegiate rocket competitions has prompted the increased use of custom rocket propulsion systems by numerous universities. Collegiate rocket and research teams starting the development of solid propellant rocket motors often encounter problems throughout the entirety of design and testing phases. Specifically, there exists a discrepancy between standardized university textbooks on the subject and current hobbyist literature. Daedalus Astronautics at Arizona State University began research on solid rocket motors in 2006 and has since developed numerous successful motors. These motors have progressed from simple propellant formulations into high regression rate propellants utilizing multiple burn rate catalysts. The evolution of Daedalus’ motor mixing methodology and other key information pertinent to solid rocket motor design and manufacture is included in this paper. A number of significant steps are outlined including safety and regulatory concerns, basic formula compositions, propellant characterization methodology, manufacturing processes and motor testing. The intended use of this paper is to act as a primer for the quick start-up and development of reliable solid rocket motor designs suitable for use in high powered sounding rockets.
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 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.037 |
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".