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Record W2623848603 · doi:10.4050/f-0071-2015-10146

Bird Impact Simulation of Polycarbonate Windshield Subject to Brittle Failures

2015· article· en· W2623848603 on OpenAlexaff
Lu Zi, Michael Seifert

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsWindshieldPolycarbonateSubject (documents)BrittlenessComputer scienceEngineeringMaterials scienceComposite materialAerospace engineeringWorld Wide Web

Abstract

fetched live from OpenAlex

Due to its low density, optical transparency, and ability to withstand large plastic deformations without failure, polycarbonate is being increasingly used as a structural material for light weight, impact resistant helicopter windshields. However, polycarbonate can exhibit brittle failure with degraded impact resistance when exposed to some types of high triaxial loads or high strain-rate loading conditions. This paper presents the state-of-the art simulation techniques developed at Bell Helicopter Textron Inc. (BHTI) to support the design of bird impact resistant windshields. Linear Elastic Fracture Mechanics (LEFM) methods were used to investigate mechanisms that can trigger the polycarbonate to exhibit brittle failure. Finite element simulations were conducted to explore the threshold conditions under which brittle failure can occur, and to correlate the material constitutive model against test data under a wide range of loading conditions and strain rates. The failure model of the polycarbonate material model was validated to reflect the specific loading conditions. The validated analytical tool provides the capability to design for equivalent or greater levels of impact protection using thinner and lighter polycarbonate and has been successfully used to guide the development of bird impact resistant windshields.

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 score0.434
Threshold uncertainty score0.308

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 head0.021
GPT teacher head0.281
Teacher spread0.259 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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