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Record W4224997346 · doi:10.18280/mmep.090204

The Difference in the Wall Thickness of the Helicopter Structure Are Made of Composite Materials with Another Made of Steel

2022· article· en· W4224997346 on OpenAlexvenueno aff
Emad Toma Karash, Jamal Nayief Sultan, Majid Khaleel Najem

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAluminiumComposite numberMaterials scienceAerodynamicsLayer (electronics)Composite materialAlloyWeldingStructural engineeringEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Weight is of great importance in the aircraft industry. Aircraft are made of aluminum alloys that are susceptible to heat treatment, because they are light in weight and are metal strong enough for the dynamic designed loads they can face, but there are other reasons for obtaining alternative materials, and these materials are composite materials that it is lighter in weight than aircraft made of aluminum, firstly, and secondly, it can be formed into attractive shapes, eliminating welding and rivets, and thirdly, it can be formed into aerodynamic shapes. This work is based on designing a three-dimensional model consisting of aluminum alloy (AA-6061-T6) of the structure helicopter and then comparing it with five other models of different metal and composite materials to obtain a structure that has the least weight among these models. The results indicate that the best model with the lowest weight is the fourth model consisting of carbon fiber, proportions and weight of a square meter and a thickness of (28 mm) than the weight of the first model consisting of aluminum and weighing (81 kg), it was less than (22.7%). Then the fifth model, which consisted of an outer layer of aluminum with a thickness of five millimeters and another inner layer of aluminum of the same thickness, and between the inner and outer layer eighteen layers of carbon fiber, where the percentage of decrease in it compared to the first model by up to (19.2%), and worse a model in terms of weight is the second model was made of steel, which has a weight that is almost twice the weight of the first model.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.013
GPT teacher head0.182
Teacher spread0.170 · 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 designBench or experimental
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

Citations6
Published2022
Admission routes1
Has abstractyes

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