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Record W2335154227 · doi:10.1115/gt2007-27489

Instability Analysis for Thermal Barrier Coating by Fracture Mechanical Modelings

2007· article· en· W2335154227 on OpenAlexaff
Amar Kumar, Amiya Nayak, Alok R. Patnaik, Xijia Wu, Prakash Patnaik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceFracture mechanicsThermal barrier coatingComposite materialFracture (geology)Stress (linguistics)Strain energy release rateStructural engineeringInstabilityCrack growth resistance curveCoatingUltimate tensile strengthCrack closureMechanicsEngineering

Abstract

fetched live from OpenAlex

Two simplistic models based on fracture mechanics considerations are used to advance the understanding of instability conditions in TBC systems. First model assumes isostrain behavior at and prior to the onset of crack initiation and is based on elastic energy balance approach. The other model is used for layer buckling and crack propagation behavior. The analysis for crack initiation suggests that the crack tip driving force, KI can reach a high value that is comparable to the fracture resistance of the coating material at and near the TBC/TGO interface even for a small nominal applied stress. The stresses required for the crack driving force (KI or GI) exceeding the fracture resistance of TBC materials are found to be in the range of 0.05 to 0.5 GPa. This appears to be an order of magnitude lower than the reported tangential tensile stress values of 1 to 2 GPa, but matches closely with the simulated transverse stress. High crack driving force resulting from low stress and small size defects (around 2 microns) facilitates early crack initiation in TBC system.

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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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
Published2007
Admission routes1
Has abstractyes

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