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Record W2794473997

Bird strike analysis on jet engine fan blade

2018· article· en· W2794473997 on OpenAlexaboutno aff
M. Mohankumar, Krishna Gupta K, T Tamizharasan, N Venkatesh

Bibliographic record

VenueInternational Journal for Advance Research and Development · 2018
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
Fundersnot available
KeywordsBlade (archaeology)Jet (fluid)EngineeringWingMechanical fanJet engineMarine engineeringStructural engineeringAerospace engineeringMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

Bird strike otherwise called as avian ingestion is the collision between the bird and the aircraft. It is a very dangerous situation in the field of aviation. Bird strike usually occurs at low altitude during take-off and landing of the aircraft. The bird strike in the jet engine is very dangerous because when a bird hits the fan blade the fan blade is displaced into other fan blade causing the cascading failure. The main objective of this project is to analyze bird strike on the aircraft jet engine Fan blade. Hemispherical Model is used to design the Bird Model. The Canadian goose and GE-NX 2B engine Fan Blade is taken to perform the analysis. The Calculation of the dimensions of the bird model is done. The fan Blade and Bird Model is designed and then assembled using CATIA V5R20. The material Properties is defined for both Bird Model and Fan Blade. The analysis is done and an attempt is made to discuss Deformation, Stress, and Strain on the Fan Blade depending on the Relative Velocity. ANSYS- Explicit Dynamics was used to analyze the 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
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 head0.062
GPT teacher head0.406
Teacher spread0.343 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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