MétaCan
Menu
Back to cohort
Record W2904527024 · doi:10.1088/2399-6528/aaf602

A physics-based model of temperature-exposure-dependent interfacial fracture toughness of thermal barrier coatings

2018· article· en· W2904527024 on OpenAlexaff
Kuiying Chen, Dongyi Seo, Sung Hun Lee, Eungsun Byon

Bibliographic record

VenueJournal of Physics Communications · 2018
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceComposite materialFracture toughnessToughnessThermal barrier coatingOxideDelamination (geology)MetallurgyLayer (electronics)

Abstract

fetched live from OpenAlex

A physics-based model of temperature-exposure-dependent interfacial fracture toughness of thermal barrier coatings was developed using Arrhenius-type formulae and experimentally measured interfacial toughness at ambient temperature. The crack delamination occurring at the thermally grown oxide (TGO)/bond coat (BC) interface was assumed in the interfacial fracture toughness evaluation. To evaluate the interfacial toughness at elevated temperatures, the interfacial plastic zone, the interface crack tip opening displacement (ICTOD) and the interface crack density at the thermally grown oxide (TGO)/bond coat interface were specified. The temperature-exposure-dependent Young's modulus of the topcoat was formulated using the experimentally measured data, and its effect on the interfacial toughness at elevated temperature was investigated. As an application, the proposed interfacial toughness model was then used to study toughness variation versus exposure temperature and time. The trend of interfacial toughness versus temperature and exposure was explained in terms of microstructural changes of topcoat and TGO.

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: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.272
Teacher spread0.250 · 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
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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics CommunicationsSame topicHigh-Temperature Coating BehaviorsFrench-language works237,207