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Thermal Barrier Coatings in burner rig experiment analyzed through LAser Shock for DAmage Monitoring (LASDAM) method

2024· article· en· W4396663459 on OpenAlexaff
Lara Mahfouz, Vincent Maurel, Vincent Guipont, Basile Marchand, Rami El Hourany, Florent Coudon, Daniel Emil Mack, Robert Vaßen

Bibliographic record

VenueJournal of the European Ceramic Society · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsMaterials scienceThermal barrier coatingOverheating (electricity)Composite materialThermal shockDelamination (geology)Temperature gradientAluminideTemperature cyclingSuperalloyCoatingTurbine bladeThermalIntermetallicTurbineAlloy

Abstract

fetched live from OpenAlex

This study investigates failure mechanisms in a typical thermal barrier coating (TBC) system comprising an EB-PVD columnar top coat, an aluminide bond coat, and a Ni-based single crystal superalloy substrate, simulating gas turbine operating conditions using a burner rig. TBC degradation, initiated by interfacial defects from the LASAT method , was studied during thermal gradient cycling under fast and slow cooling. In-situ optical and infrared imaging, along with ex-situ SEM cross-sectional analysis, monitored failure mechanisms. The Laser Shock for Damage Monitoring (LASDAM) technique provided insights into gradient and cooling rate impacts on columnar TBC damage. Results showed significant effects of cooling rate on delamination and localized failure at blister sites, with LASDAM revealing significant overheating at damage sites. Analysis included full-field temperature and damage assessment, emphasizing blister-driven delamination under severe thermal gradients. Discussion focused on elastic stored energy effects, noting that fast cooling induced transient conditions where reversed temperature gradients increased damage, limiting TBC lifespan.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.293
Teacher spread0.274 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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Same venueJournal of the European Ceramic SocietySame topicHigh-Temperature Coating BehaviorsFrench-language works237,207