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Record W2159577459 · doi:10.1177/0957650914531949

Influence of the axial turbine design parameters on the stator–rotor axial clearance losses

2014· article· en· W2159577459 on OpenAlexfundno aff
Aki Grönman, Teemu Turunen-Saaresti, Pekka Röyttä, Ahti Jaatinen‐Värri

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy · 2014
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsnot available
FundersLappeenranta University of TechnologyAcademy of FinlandUniversity of Ottawa
KeywordsStatorTurbineChord (peer-to-peer)Rotor (electric)AerospaceMach numberTip clearanceComputer scienceControl theory (sociology)Mechanical engineeringMechanicsEngineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

The drive towards lower emissions in aerospace engines promotes more efficient and physically smaller engines. One way to decrease the size of the axial turbine is to minimize the distance between successive stator and rotor rows. This can usually have either a positive or negative influence on the turbine performance. The reasons for this behaviour are not currently fully understood. In this study, a novel approach is taken to find new insights into this design question by analysing the influence of different design parameters on the turbine efficiency behaviour. Several different turbines are analysed using the literature. For the first time, the performed analysis reveals the design parameters, which correlate with the different efficiency curve shapes. The correlating parameters are the stator–rotor axial clearance, stator pitch to axial chord ratio, turning velocity Mach number and rotor aspect ratio. The mechanisms behind the found correlations are further analysed to connect the physical phenomena with the design parameters.

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 score0.646
Threshold uncertainty score0.281

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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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