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Record W3187021090 · doi:10.2514/6.2021-2605

Pressure fluctuations under a turbulent boundary layer with transpiration cooling

2021· article· en· W3187021090 on OpenAlexaff
Sophie Hillcoat, Jean-Pierre Hickey

Bibliographic record

VenueAIAA AVIATION 2021 FORUM · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTurbulenceBoundary layerMechanicsTranspirationFlow (mathematics)CoolantPressure gradientMaterials scienceThermalWork (physics)PhysicsThermodynamicsChemistry

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2021-2605.vid Transpiration cooling is an active thermal protective system (TPS) increasingly used in space applications under high thermal loads. A coolant gas is effused through a porous wall into a turbulent boundary layer (TBL) via a pressure gradient. The pressure fluctuations at the wall above the transpiration region result in temporal and spatial modulation of the flow within the porous flow network. This work quantifies the pressure fluctuations at the wall in transpiration cooling through the analysis of a direct numerical simulation (DNS) database. The pressure distributions compare favorably to existing analytical near-wall pressure fluctuation models. It is found that the while the addition of blowing does somewhat impact the shape and magnitude of the resultant spectrum, the Liepmann analytical model still yields a sufficient approximation within the resolved range.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2021
Admission routes1
Has abstractyes

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Same venueAIAA AVIATION 2021 FORUMSame topicFluid Dynamics and Turbulent FlowsFrench-language works237,207