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Record W2088226850 · doi:10.1115/gt2010-22104

Unsteady Tip Leakage Flow Characteristics and Heat Transfer on Turbine Blade Tip and Casing

2010· article· en· W2088226850 on OpenAlexaff
M. Hamidur Rahman, Sung In Kim, Ibrahim Hassan, Carole El Ayoubi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsMechanicsHeat transferTrailing edgeNusselt numberTip clearanceMaterials scienceLeading edgeCasingTurbineTurbine bladeLeakage (economics)StatorThermodynamicsMechanical engineeringEngineeringPhysicsVortexTurbulence

Abstract

fetched live from OpenAlex

An unsteady numerical investigation was performed to examine time dependent behaviors of the tip leakage flow structures and heat transfer on the rotor blade tip and casing in a single stage gas turbine engine. A transonic, high-pressure turbine stage was modeled and simulated using a stage pressure ratio of 3.2. The rotor’s tip clearance was 1.2 mm in height (3% of the rotor span) and its speed was set at 9500 rpm. Periodic flow is observed for each vane passing period. Tip leakage flow as well as heat transfer data showed highly time dependent behaviors. A stator trailing edge shock appears as the turbine stage is operating at transonic conditions. The shock alters the flow condition in the rotor section, namely, the tip leakage flow structures and heat transfer rate distributions. The instantaneous Nusselt number distributions are compared to the time averaged and steady-state results. The same patterns in tip leakage flow structures and heat transfer rate distributions were observed in both unsteady and steady simulations. However, the unsteady simulation captured the locally time-dependent high heat transfer phenomena caused by the unsteady interaction with the upstream vane trailing-edge shock and the passing wake.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.463

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 head0.180
Teacher spread0.176 · 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 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

Citations6
Published2010
Admission routes1
Has abstractyes

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