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Record W4415991143 · doi:10.1115/1.4070350

The Adverse Effect of Pre-Swirl on Ingestion in a Downstream Cavity

2025· article· en· W4415991143 on OpenAlexaff
James A. Harrington, Simon Vella, Hui Tang, Gary D. Lock, James A. Scobie, Fatoumata Bintou Santara, Clément Jarrossay, Damien Bonneau, Francesco Salvatori, Carl M. Sangan

Bibliographic record

VenueJournal of Turbomachinery · 2025
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsPurgeDownstream (manufacturing)ChangeoverFlow (mathematics)TurbineStatorInjector

Abstract

fetched live from OpenAlex

Abstract Gas turbine designers demand accurate predictions of metal temperature to ensure acceptable operating life of components experiencing high thermal stress. Rotor–stator cavities ingest hot mainstream gas through rim seals when inadequately purged with relatively cool air bled off the compressor. Superfluous use of purge, and any associated windage increase, creates a parasitic loss in overall efficiency. Shear interaction caused by the difference in swirl between the purge and mainstream flow is a principal driver for ingestion; preswirled purge flow has the potential to alter the swirl gradient. This article presents the first assessment of purge conditioning in a downstream cavity. An experimental campaign was conducted in a new aero-engine representative 1.5-stage test facility designed to facilitate expedient changeover of modular components in the downstream stator assembly. Purge flow in the downstream cavity was supplied through a series of angled injectors contained in a single component at mid-radius. Three coswirled injection angles were tested. Measurements of CO2 gas concentration, static pressure, and swirl were taken in the cavity to examine the relationship between purge-mainstream swirl gradient and ingress downstream of a rotor blade. The aero-engine designer must balance caution when employing preswirl to reduce disc windage; coswirled purge increased the purge-mainstream swirl gradient and subsequently increased shear-driven ingestion.

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)0.001
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 score0.471
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.002
GPT teacher head0.218
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
Published2025
Admission routes1
Has abstractyes

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