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Gas Turbine Blade Stress Analysis and Mode Shape Determination Using Thermoelastic Methods

2008· article· en· W2015698424 on OpenAlexaff
David Bäckman, Richard Greene

Bibliographic record

VenueApplied Mechanics and Materials · 2008
Typearticle
Languageen
FieldEngineering
TopicMechanical and Thermal Properties Analysis
Canadian institutionsNational Research Council Canada
FundersUniversity of Sheffield
KeywordsThermoelastic dampingVibrationStructural engineeringTurbine bladeAcousticsStress (linguistics)Stack (abstract data type)ActuatorDisplacement (psychology)TurbomachineryNatural frequencyEngineeringMaterials scienceTurbineMechanical engineeringPhysicsElectrical engineeringThermalComputer science

Abstract

fetched live from OpenAlex

The efficacy of thermoelastic stress analysis for use in the study of moderately curved gas turbine blades is considered over a frequency range of 68 Hz to 3.4 kHz. A selection of blades, both industrial examples and simplified planar laboratory specimens, are excited at their natural vibration frequencies using both electromagnetic shakers and piezoelectric stack actuators, in order to develop a cyclic displacement of the blade surface and hence a cyclic variation in surface stress condition. Results are shown using both snapshot array and rolling array infrared detector systems, and the data then used to generate maps of normalized principal surface stress sum, and hence the mode shapes of vibration, including the first four excitation modes.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.251
Teacher spread0.228 · 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

Citations6
Published2008
Admission routes1
Has abstractyes

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