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Record W3162959517 · doi:10.1364/ao.427116

Experimental study on the effect of defect curvature on the impactfracture behavior of structures using the real-virtual causticsmethod

2021· article· en· W3162959517 on OpenAlexaff
Chenxi Ding, Renshu Yang, Cheng Chen, Desheng Wang, Min Gong

Bibliographic record

VenueApplied Optics · 2021
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsGeomechanica (Canada)
FundersFundamental Research Funds for the Central UniversitiesChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMaterials scienceHammerCurvatureFracture mechanicsDrop (telecommunication)Crack closureStructural engineeringDrop impactFracture (geology)Impact energyMechanicsComposite materialEngineeringMechanical engineeringMetallurgyGeometryPhysics

Abstract

fetched live from OpenAlex

Defects have significant influence on the impact fracture behavior of structures. In this paper, the real-virtual caustics method is used to study the impact fracture behavior of structures with elliptical arc defects under impact loading of the drop hammer, and the impact loading process is simultaneously analyzed. The research results show that impact loading of the drop hammer in this experiment is a multi-period dynamic loading process, and the fracture of specimens under impact loading of the drop hammer is an energy-controlled process. The running crack initiates under impact loading and propagates toward the elliptical arc defect. After reaching the end of the elliptical arc defect, the running crack arrests and accumulates energy, and then it initiates again and propagates toward the loading position. The greater the end curvature of the elliptical arc defect, the shorter the time for the running crack to stagnate and accumulate energy at the defect end, and the earlier the time for the running crack to initiate again at the defect end, the smaller the impact loading stress of the drop hammer and the dynamic stress intensity factor of running crack tip when the running crack initiates again at the defect end.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.433

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 head0.024
GPT teacher head0.298
Teacher spread0.274 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

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