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Record W6892958876 · doi:10.5281/zenodo.1345000

The Role Of Thermally Grown Oxide In The Failure Thermal Barrier Coatings For Gas Turbine Engine Applications

2018· article· en· W6892958876 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsThermal barrier coatingOxideCeramicLayer (electronics)Temperature cyclingCatastrophic failureSuperalloyFailure mechanismGas turbines

Abstract

fetched live from OpenAlex

Thermal barrier coatings (TBCs) are widely used in gas turbine engines for propulsion and power generation to maximize engine operating temperature and fuel efficiency. TBCs comprise primarily three major components: the ceramic top coat, the intermetallic bond coat and a thin layer of a thermally grown oxide (TGO) formed at the bond coat/top coat interface. It was demonstrated that the TGO layer plays a critical role in determining the TBC life time with many TBC failure events occurring with its direct involvement. However, the actual mechanisms that govern TBC degradation and failure are still not fully understood in terms of the TGO role and the effects of its properties on the failure process. Stress analysis and TBC life time modelling were used in this study to demonstrate that the TGO morphology, its specific mechanical and thermal properties significantly affect the degradation modes and failure behaviour of the entire TBC system while the particular failure mechanisms differ depending on the ceramic top coat fabrication process. In this paper, the effect of changes in the TGO layer during thermal cycling has been theoretically investigated for TBC with top coats fabricated by atmospheric plasma spray and electron be am physical vapour deposition and the differences in their degradation and failure have been discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.211
Teacher spread0.202 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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