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Record W4402873991 · doi:10.33737/gpps24-tc-037

High-Fidelity Film-Cooled Rotor High Pressure Turbine Simulation using Metric-Based Anisotropic Mesh Adaptation

2024· article· en· W4402873991 on OpenAlex
Frédéric Alauzet, Enza Parente, Alberto Remigi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings · 2024
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsHigh fidelityRotor (electric)Computer scienceAdaptation (eye)FidelityAnisotropyMetric (unit)TurbineMaterials scienceMechanical engineeringAcousticsEngineeringPhysicsOpticsTelecommunications

Abstract

fetched live from OpenAlex

This paper shows the ability of metric-based anisotropic mesh adaptation to accurately predict the aerodynamic flow field on a particularly complex test case: a film-cooled rotor high pressure turbine. Film-cooled high pressure turbine blades are very complex turbomachinery components that in recent years results in very complex geometries. In fact increasing temperatures in the combustion chamber results in cooling needs on the blade surface. For this reason, the film cooled turbine are composed of a cooling network and many different-shaped film-cooling holes which makes these geometries extremely complex to mesh with structured standard meshing approaches. Consequently, metric-based anisotropic mesh adaptation seems quite appropriate for such applications thanks to the fact that it provides automation of the whole process and it allows significant gains in terms of simulation accuracy by capturing coherent flow features proper to the turbomachinery physics. The benefits in terms of accuracy and mesh-convergence are demonstrated on a realistic film-cooled rotor high pressure turbine.

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.

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 score0.241
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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