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Record W7061638999

Solid particle erosion resistant hard coatings for gas turbine engine applications

2017· other· en· W7061638999 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2017
Typeother
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
Fundersnot available
KeywordsErosionDowntimeImpellerGas compressorTurbineAirfoilGas turbinesService life
DOInot available

Abstract

fetched live from OpenAlex

Aircraft engine components, such as compressor blades, vanes, and impeller blisks/wheels, when operating in a sandy environment, can experience severe erosion damage due to ingestion of sand particles. As erosion progresses, the substantial amount of material removal not only leads to further aerodynamic losses but results in the structural weakening of blades as well. Replacement of the parts, whose erosion damage limits have been reached, significantly increases the maintenance and downtime costs. Applying erosion resistant coatings on airfoil surface has been proven as an effective approach to extending the serviceable life of gas turbine engine components. This chapter provides a comprehensive review of the factors affecting erosion damage to engine components, the basic requirements that erosion resistant coatings need to satisfy, the various coating systems that have been developed with excellent erosion resistance, as well as the erosion durability testing techniques applied to evaluate and qualify erosion-resistant coatings. Several technical challenges related to the development of erosion resistant coatings are also briefly 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 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.004

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 head0.020
GPT teacher head0.276
Teacher spread0.256 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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