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Record W9196375 · doi:10.1136/adc.76.5.477f

Modélisation tridimensionnelle par éléments finis enrichis pour le calcul de singularités de délaminage et à la jonction de matériaux anisotropes

2008· dissertation· fr· W9196375 on OpenAlexaboutno aff
Vincent Magnier

Bibliographic record

VenueArchives of Disease in Childhood · 2008
Typedissertation
Languagefr
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhysicsPhilosophy

Abstract

fetched live from OpenAlex

Il est connu de longue date que les discontinuites materielles et/ou geometriques peuvent provoquer des concentrations des contraintes, dites surcontraintes, nocives pour la resistance du materiau qui doit les supporter. En effet, l'etat de contrainte dans ces zones peut etre singulier et ne peut etre modelise en pratique par elements finis reguliers tridimensionnels, a moins de mailler tres finement. Pour palier cette difficulte, nous proposons une methode numerique en deux etapes. La premiere permet de determiner l'ordre de la singularite et le mode de surcontraintes. La seconde etape consiste a inclure ces resultats dans une description des champs mecaniques regnant dans la structure pour obtenir les informations completes sur l'amorcage de fissure via une regle de raccordement. Ce raccordement a d'abord ete effectue dans une formulation d'elements finis hybrides Metis a double singularite puis dans une formulation en deplacement pur. Cette technique a ete etendue au cas general ou l'exposant de singularite est quelconque permettant de resoudre des problemes de bord libre ou d'entaille a la jonction de plusieurs materiaux anisotropes avec une grande precision. L'analyse duale d'un probleme grâce aux deux types de formulations fournit une estimation a posteriori de l'erreur. Des applications numeriques tant au niveau du calcul d'exposants de singularite que de celui des facteurs d'intensite des contraintes valident la methode en s'appuyant sur divers exemples issus de la litterature. Les resultats montrent une bonne coherence et une bonne precision. De plus, ces methodes ne requierent qu'un faible temps CPU.

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: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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
GenreOther

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
Published2008
Admission routes1
Has abstractyes

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