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Record W2034006642 · doi:10.1115/gt2009-59856

Pressure Loss Modeling of Non-Symmetric Gas Turbine Exhaust Ducts Using CFD

2009· article· en· W2034006642 on OpenAlexafffund
Steven Farber, Wahid Ghaly

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaPratt and Whitney Canada
KeywordsComputational fluid dynamicsAerodynamicsDuct (anatomy)TurbineInletMechanicsExhaust gasMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

In typical gas turbine applications, combustion gases that are discharged from the turbine are exhausted into the atmosphere in a direction that is sometimes different from that of the inlet. In such cases, the design of efficient exhaust ducts is a challenging task particularly when the exhaust gases are also swirling. A parametric Computational Fluid Dynamics (CFD) based study was carried out on non-symmetric gas turbine exhaust ducts where the effects of geometry and inlet aerodynamic conditions were examined. These exhaust ducts comprise an annular inlet, a flow splitter, an annular to rectangular transition region, and an exhaust stub. The duct geometry, which is a three-dimensional complex one, is approximated with a six-parameter model (four geometric and two aerodynamic), which was coupled with a design of experiment method to generate a relatively small number of exhaust ducts. The flow in these ducts was simulated using CFD for different values of inlet swirl and aerodynamic blockage and the numerical results were reviewed so as to assess the effects of the geometric and aerodynamic parameters on the total pressure loss in the exhaust duct. These flow simulations were used as a data base to generate a total pressure loss model that designers can use as a tool to build more efficient non-symmetric gas turbine exhaust ducts.

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 score0.277
Threshold uncertainty score0.481

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 head0.014
GPT teacher head0.225
Teacher spread0.211 · 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

Citations4
Published2009
Admission routes2
Has abstractyes

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