Baffled Tube Ram Accelerator Projectile Geometry Effects on Thrust
Bibliographic record
Abstract
The ram accelerator is a chemical mass driver system that uses a ramjet-like propulsive cycle to generate thrust. In a baffled-tube ram accelerator (BTRA), washer-like baffles are spaced throughout the tube to guide axisymmetric projectiles and prevent unstarts from forward surging pressure waves generated by the use of energetic propellants. Past BTRA work has focused on the geometry of the tube itself, leading to a better understanding of the impact of individual baffle designs on operation. In this investigation, projectile geometries were systematically varied and tested in an 8-m-long BTRA test section to determine which features increase thrust and those that may increase projectile thrust-to-mass ratio. A projectile shoulder-to-baffle port diameter ratio of ∼92% was found to generate the most thrust, thus some diametric clearance was useful. The thrust increased with length of projectile shoulder, however, the corresponding thrust-to-mass ratio did not with solid projectiles. A concurrent CFD effort used Ansys Fluent to estimate the BTRA drag experienced by these projectile configurations in inert propellant. These simulations found that relatively long nosecones and tail frusta resulted in less drag and that there may be a shoulder length optimum for minimum drag. This research effort has produced a technical data base that can be drawn upon to aid designing ram accelerator projectiles for a wide range of 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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".