Uniform Particle Regression and Solid Rocket Combustion Instability Suppression
Bibliographic record
Abstract
Research towards predicting and quantifying undesirable transient axial combustion instability symptoms in solid-propellant rocket motors necessitates a comprehensive numerical model for internal ballistic simulation under dynamic flow and combustion conditions. In the present investigation, important elements of the framework for numerically evaluating the usage of reactive aluminum particles for the suppression of axial shock wave development are brought forward. A primary focus is placed on evaluating the qualitative trends associated with the time-dependent reduction in size of the aluminum particles as they move downstream in the central internal flow. In this study, the reactive particle size regression is stipulated to occur at a nonuniform rate, through an evaporation law that is governed by the particle’s current diameter. Individual transient internal ballistic simulation runs for a reference composite-propellant cylindrical-grain motor show the evolution of the axial pressure wave for a given initiating pressure disturbance, and particle loading, initial particle size, and evaporation law parameter setting. The limit pressure wave magnitudes at a later reference time in a given firing simulation run are collected for a series of runs, in order to assist in the evaluation of identifiable trends. The numerical results demonstrate that the ability of the particles to suppress axial wave development can be effective, but in general, not nearly as effective when comparing to the constant-diameter inert particle case, for the same particle loading. There may be some advantage in using a larger starting reactive particle size relative to the reference inert case, for improved overall symptom suppression. However, increasing the reactive particle loading may be the only means for reaching a desired symptom suppression level.
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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".