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Record W2024963537 · doi:10.1115/gt2013-95059

Efficient Computation of Large Pitch Ratio Transonic Flow in a Fan With Inlet Distortion

2013· article· en· W2024963537 on OpenAlexaff
Gaurav Sharma, Laith Zori, Stuart Connell, Philippe Godin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsAnsys (Canada)
Fundersnot available
KeywordsTransonicInletMechanicsAnnulus (botany)Distortion (music)Computational fluid dynamicsAerodynamicsFlow (mathematics)Mach numberStatic pressureAcousticsSimulationComputer scienceEngineeringPhysicsMaterials scienceMechanical engineering

Abstract

fetched live from OpenAlex

Modeling the unsteady flow of a fan subject to an inlet distortion is computationally expensive due to the need to model the full-annulus. Using the Fourier Transformation (FT) method in ANSYS CFX, which recognizes phase-shifted periodic boundary conditions, the fan inlet distortion simulation can be achieved efficiently by solving just two passages. The FT method can handle very large inlet distortion to blade passage pitch ratios such as the case of the problem simulated in this work. The analysis considers transonic flow through a fan with high bypass ratio subjected to an inlet total pressure distortion. The inlet disturbance traverses the inlet once per revolution and is intended to simulate the inlet flow distortion seen by an aircraft engine fan during take-off conditions. The pressure ratio across the fan is chosen so that the fan moves from a started to un-started condition as the disturbance moves past the inlet. This condition will provide a rigorous test of the FT method. The FT method is validated by comparing to the equivalent full-annulus unsteady solution. The FT unsteady solution compares remarkably well with the reference solution and is able to reproduce the detailed dynamics of the shock movement. Moreover, the solution from the FT method is also able to reproduce the efficiency, viscous effects and blade loading from the full-annulus case. The FT solution is obtained with a 5X reduction in CPU time and a 10X reduction in memory requirement.

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 score0.415
Threshold uncertainty score0.206

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 head0.003
GPT teacher head0.181
Teacher spread0.178 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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