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Record W2306178234 · doi:10.1115/gt2015-42520

Study of Effects of Rotor Tip Tailoring in Axial Flow Compressors

2015· article· en· W2306178234 on OpenAlexfundno aff
Chaitanya Halbe, Yashovardhan S. Chati, Jubin Tom George, A. M. Pradeep, Bhaskar Roy, Hong Yu, Peter H. Townsend

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
FundersIndian Institute of Technology BombayPratt and Whitney Canada
KeywordsTip clearanceGas compressorAxial compressorRotor (electric)Computational fluid dynamicsMaterials scienceLeading edgeTrailing edgeMechanicsPressure dropStructural engineeringMechanical engineeringEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

The performance of an axial compressor rotor is known to be affected by the variations in tip clearance during its operation. This effect is pronounced for the rear stages of a multistage compressor. This paper describes a novel design that is shown to aerodynamically desensitize the rotor tip to the tip clearance variations. The effect of tip clearance variations on the performance of a baseline low speed, high hub-to-tip ratio axial compressor rotor is studied using CFD. Based on the understanding developed from this flow analysis, the baseline rotor is redesigned by tailoring the tip and redistributing the blade loading over the span. The tip tailoring results in a blade with split dihedral, i.e. of applied dihedral variable from the leading edge to the trailing edge. CFD analysis of the tip tailored configuration shows lower pressure drop with increasing tip clearance as compared to the baseline design. The simulation results are validated through testing in a low speed axial compressor rig, thereby giving experimental support to the desensitization of the rotor to the studied tip clearance variations by tip tailoring.

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 score0.712
Threshold uncertainty score0.186

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 head0.022
GPT teacher head0.238
Teacher spread0.215 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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