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Record W2016824772 · doi:10.1115/gt2009-60142

CFD Investigation of Temperature Rise in a Combustor Test Rig Exhaust System

2009· article· en· W2016824772 on OpenAlexafffundabout
Joan Boulanger, Yinghua Han, Lei‐Yong Jiang, Shaji Manipurath

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsComputational fluid dynamicsCombustorFluentNozzleExhaust gasMach numberCondensationWater coolingTurbineMaterials scienceMechanicsNuclear engineeringMechanical engineeringAerospace engineeringEngineeringCombustionThermodynamicsChemistryPhysicsWaste management

Abstract

fetched live from OpenAlex

This paper presents a study of temperature rise in the exhaust system of a combustor test rig, Test Cell #1, at the Gas Turbine Laboratory, Institute for Aerospace Research, the National Research Council of Canada. As the flow regime is supersonic with a mixture of hot air & water vapour, condensation of water vapour in the system is suggested to explain the temperature rise observed along the exhaust pipe. The method of Computational Flow Dynamics is used to carry out the first investigation on this hypothesis. The exhaust system is reproduced by CAD, meshed and modelled by the ANSYS-FLUENT CFD package. Simulations of a two-phase complex mixture are performed. The numerical results indicate that the pressure control devices in the exhaust flow towards the stack create phenomena similar to nozzles and yield condensed water into the system. The simulations of liquid phase content and temperature fields are qualitatively consistent with experimental observations and support the hypothesis that condensation is occurring and may therefore threaten the structural integrity of the system through thermal effects.

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 score0.456
Threshold uncertainty score0.360

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 head0.005
GPT teacher head0.179
Teacher spread0.174 · 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
Published2009
Admission routes3
Has abstractyes

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