Grain Geometry Modifications for Instability Symptom Suppression in Solid Rocket Motor
Bibliographic record
Abstract
*† Research towards predicting and quantifying undesirable transient axial combustion instability symptoms in solid rocket motors has led to the further development of a comprehensive numerical model for internal ballistic simulation under dynamic flow, combustion and structural vibration conditions. In the present paper, the modification of the internal propellant grain geometry (in this case, introducing an expansion or contraction in the port flow cross-sectional area at some point along the grain’s length), one of several traditional means for suppressing axial instability symptoms, is comprehensively examined using the internal ballistic simulation model. Individual transient simulation runs show the evolution of the axial pressure wave and associated dc shift for the given grain geometry of a reference motor, as initiated by a given pressure disturbance. Limit pressure wave magnitudes are collected for a number of simulation runs for different grain area transition positions and magnitudes, and mapped on an attenuation trend chart. When the effect of acceleration (through structural vibration of the propellant surface) on the combustion process is included in the numerical calculations, one observes substantial differences in burning and internal flow behavior in the presence of axial pressure wave activity, as reflected in individual firing simulations and the corresponding attenuation map.
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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 | 0.000 | 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".