Side Force Generation Mechanism for a Missile with Nose-Mounted Micro-Structures
Bibliographic record
Abstract
Using nose-mounted micro-structures, it was shown experimentally and computationally in earlier publications by the authors that signiflcant side force could be generated at moderate angles-of-attack and for supersonic ∞ow on a generic missile conflguration. Under these ∞ow conditions, a \clean slender body does not usually experience any lateral or side forces. In the current study, additional numerical simulations were performed in order to determine the mechanism by which the nose-mounted micro structures contribute to the generation of a side force at moderate angles-of-attack. The computational results show that the presence of the micro-structure on the nose contributes to the formation of a vortex that eventually merges with one of the two standard counter-rotating leeside vortices that form when a slender body is placed at angle-of-attack. The ∞ow efiector shed vortex being much stronger than the leeside ∞ow separation generated vortex, entrains the latter one to eventually merge into a single stronger vortex. The newly formed vortex, being stronger than the opposite side vortex, is forced to move upward above the missile’s leeside. In turn, this results in higher surface pressure on that side of the missile and hence the formation of a side force.
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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 | 0.000 |
| 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) | 0.002 | 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".