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Record W2803642013 · doi:10.1177/0021998318775472

Damage to an A-N720 ceramic matrix composite under simulated gas turbine static component conditions using laser heating

2018· article· en· W2803642013 on OpenAlexafffund
Larry Lebel, Rachid Boukhili, S. Turenne

Bibliographic record

VenueJournal of Composite Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsPolytechnique Montréal
FundersNational Research Council CanadaPratt and Whitney Canada
KeywordsMaterials scienceComposite materialDelamination (geology)CeramicComposite numberCeramic matrix compositeCrackingDeflection (physics)BrittlenessStress (linguistics)

Abstract

fetched live from OpenAlex

A ceramic matrix composite material constructed from a porous alumina matrix and Nextel™ 720 fibers was subjected to a novel type of experiment designed to reproduce the operating conditions of a gas turbine static component. Material specimens were exposed to cyclic laser heating on one side and active air cooling on the other side while being constrained in their bending deflection. For most specimens, accumulation of damage under the constant-amplitude heat load resulted in a temperature increase above the material limit of 1200℃ at the heated surface. Stress relaxation was observed due to inflicted sudden damage, like surface fiber buckling or ply delamination, and due to gradual permanent deformation of the material. The amount of damage was quantified by the variation of a stiffness parameter based on the measured reaction force and through-thickness temperature difference. While the level of damage was significant at the hot face, with the depth of cracking and delamination reaching up to half of the specimen thickness, the specimen back face remained intact. Stabilization of the damage was observed due to stress relaxation and decoupling of the damaged plies from the non-affected ones.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.331
Teacher spread0.306 · 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.

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
Published2018
Admission routes2
Has abstractyes

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