An investigation of the grid fins as control surface devices for missiles
Bibliographic record
Abstract
Abstract Recently, grid fins have been receiving increasing attention as a practical and efficient means of controlling missile trajectory. Preliminary studies at IAR have demonstrated that modern CFD methods can be used for computing flows past complex grid fin type configurations, and that these methods are more soundly physically based than the earlier vortex lattice and/or shock expansion methods. The current paper addresses the issue of the grid fin size, in terms of both the panel thickness and the frontal shape. The study covers three thicknesses for the grid fin panel, with front shapes having a simple blunt square face, as well as a sharp knife-edge shape. In addition, an important aspect of the present investigation is to quantify the aerodynamic effect of the ramp fairing installed immediately upstream of the blunt base upon which the grid fin assembly resides. A comparison of flow field characteristics and aerodynamic coefficients of the grid fin assembly, with and without the fairing ramp, would provide a direct means of evaluating the effect of the ramp. At this stage, the investigations are based on Euler calculations. The present study focuses on a standard grid fin configuration mounted on a generic cylindrical body.
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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".