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Record W6996890104

Study of Fluid-structure Interactions of Communication Antennas

2011· dissertation· en· W6996890104 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2011
Typedissertation
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsAerodynamicsAntenna (radio)VibrationFinite element methodAerodynamic forceWind engineeringCurrent (fluid)Directional antenna
DOInot available

Abstract

fetched live from OpenAlex

Large structures exposed to the environment such as the collinear omni and large panel communication antennas in this research suffer damage from cyclic wind, rain, hail, ice load and impacts from birds and stones. Stresses from self-weight, ice loading and wind gusts will produce deformations of the structure that will lead to performance deterioration of the antenna. In order to avoid such a case, it is important to understand the static, dynamic and aerodynamic behavior of these structures and thus optimization can be achieved. In this research the current fluid-structure interaction methods are used to model, simulate and analyze these communication antennas in order to assess whether failure would occur under service loads. The FEA models developed are verified against analytical models and/or experiments. Different antenna configurations are compared based on their capacity to minimize vibration effects, stress-induced deformations and aerodynamic loading effects.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.154
Teacher spread0.150 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2011
Admission routes1
Has abstractyes

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