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Record W4234613262 · doi:10.1115/1.4035159

Material Selection Issues for a Nozzle Guide Vane Against Service-Induced Failure

2016· article· en· W4234613262 on OpenAlexaff
Xijia Wu, Zhong Zhang, Lei‐Yong Jiang, Prakash Patnaik

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2016
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsNozzleMechanism (biology)ClimbMaterials scienceCreepStructural engineeringFailure mechanismDislocationMaterial selectionDeformation (meteorology)Deformation mechanismMechanical engineeringSelection (genetic algorithm)Component (thermodynamics)PlasticityForensic engineeringEngineeringComputer scienceComposite materialAerospace engineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Nozzle guide vanes (NGV) of gas turbine engines are the first components to withstand the impingement of hot combustion gas and therefore often suffer thermal fatigue failures in service. A lifting analysis is performed for the NGV of a gas turbine engine using the integrated creep–fatigue theory (ICFT). With the constitutive formulation of inelastic strain in terms of mechanism-strain components such as rate-independent plasticity, dislocation glide-plus-climb, and grain boundary sliding (GBS), the dominant deformation mechanisms at the critical locations are thus identified quantitatively with the corresponding mechanism-strain component. The material selection scenarios are discussed with regards to damage accumulated during take-off and cruise. The interplay of those deformation mechanisms in the failure process is elucidated such that an “optimum” material selection solution may be achieved.

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)0.000
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 score0.845
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.213
Teacher spread0.208 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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