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Record W2748830329 · doi:10.1115/gt2017-63083

Blade Tip Leakage Loss Reduction by Means of Passive Tip Injection: Linear Cascade Wind Tunnel Results

2017· article· en· W2748830329 on OpenAlexfundno aff
Jonas Rejek, Stefan aus der Wiesche, Reinhard Willinger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsnot available
FundersBundesministerium für Wirtschaft und EnergieRyerson University
KeywordsCascadeLeakage (economics)Wind tunnelVortexTip clearanceMechanicsMaterials scienceTurbineComputational fluid dynamicsAcousticsEnvironmental scienceAerospace engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

In the open literature, an innovative concept for turbine blade tip leakage loss reduction by means of passive tip injection was recently proposed. The present paper presents experimental results obtained for an unshrouded turbine blade corresponding to a 50 % reaction stage. The experiments were performed in a low-speed linear cascade wind tunnel facility with air as working fluid. The effect of passive tip injection on the resulting loss was investigated by detailed five-hole-probe measurements. Cascades with three different tip gap heights and blades with and without passive injection were considered. Special attention was spent to the actual upstream conditions. The detailed flow field measurements showed that at the blade tip exit the leakage flow merged with the main flow and rolled up to a tip leakage vortex. The linear cascade wind tunnel results indicated a slight reduction of the resulting total pressure loss coefficient due to the passive tip injection. The observed tip leakage loss reduction was well comparable with the predictions of simplified analytical model.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.566

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 communication0.0000.001
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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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