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Record W1978606758 · doi:10.1115/gt2013-94700

CFD Simulations of Full Surface Passive Effusion Mass Injections in a Rectangular S-Bend Diffuser

2013· article· en· W1978606758 on OpenAlexafffund
B. C. N. Ng, A. M. Birk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputational fluid dynamicsMechanicsMaterials scienceEffusionDiffuser (optics)Hydraulic jumpFlow (mathematics)PhysicsThermodynamicsOptics

Abstract

fetched live from OpenAlex

A coarse grid CFD methodology was employed to simulate internal flow passages with full coverage effusion cooling by imposing momentum sinks on effusion cooled surfaces based on a perforated plate pressure loss analogy. The methodology was implemented by specifying 1D Porous Jump boundary conditions (available in ANSYS FLUENT) on the effusion cooled surfaces. Numerical simulations were conducted based on the experimental data of an S-duct diffusing passage where ambient air was passively drawn into the sub-atmospheric passage along the different effusion surfaces with 1 mm diameter holes spaced 4 mm apart. The porous wall simulations were also compared to an alternative CFD approach with mass inlet boundary specified on the effusion surfaces. The proposed porous wall model is promising for practical design applications with the reasonable simulations of the S-duct flow fields with effusion injections. A reasonable accuracy in the results of injection mass flow rates was also obtained for the different effusion configurations. Discrepancies in the simulations of flow momentum components were mainly contributed to the diminishing effects of discrete injections at the aft-section of cooling surface due to the development of a shear layer across the free surfaces (porous jump boundaries) between the main flow and coolant flow.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score1.000

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)0.0010.000

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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.

Study designBench or experimental
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
Published2013
Admission routes2
Has abstractyes

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