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Record W2531809649 · doi:10.1017/s0001924000028086

An investigation of the grid fins as control surface devices for missiles

2000· article· en· W2531809649 on OpenAlexaff
S. Chen, M. Khalid, Hui Xu, François Lesage

Bibliographic record

VenueThe Aeronautical Journal · 2000
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsBombardier Recreational Products (Canada)National Research Council Canada
Fundersnot available
KeywordsFinAerodynamicsGridComputational fluid dynamicsVortexMechanicsAerospace engineeringComputer scienceEngineeringSimulationStructural engineeringMechanical engineeringGeometryPhysicsMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.223
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2000
Admission routes1
Has abstractyes

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