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Record W2978755806 · doi:10.1115/1.4044981

Dynamic Mode Decomposition Analysis of Flow Separation in a Diffuser to Inform Flow Control Strategies

2019· article· en· W2978755806 on OpenAlexafffund
Jinchun Wang, Guoping Huang, Weiyu Lu, Pierre E. Sullivan

Bibliographic record

VenueJournal of Fluids Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaChina Scholarship CouncilMitacsCompute CanadaNational Natural Science Foundation of ChinaGovernment of OntarioUniversity of TorontoOntario Centres of Excellence
KeywordsDynamic mode decompositionWakeDiffuser (optics)Flow (mathematics)Mode (computer interface)Large eddy simulationFlow control (data)MechanicsMean flowBluffPhysicsLagrangian coherent structuresUpstream (networking)TurbulenceComputer scienceOpticsTelecommunications

Abstract

fetched live from OpenAlex

Abstract In this work, a large eddy simulation (LES) of a typical subsonic diffuser provides data used to analyze coherent structure in a separated flow with dynamic mode decomposition (DMD). From this, a low–dimensional approximation, which retains the main dynamic characteristics of the original flow fields, is obtained. In particular, specific dynamic structures associated with a unique frequency are isolated. The spatial structures of the real and imaginary parts of the DMD mode are similar but with a phase difference. The contribution of the conjugate modes to the evolution of the DMD modes over time is discussed. The dominant frequency is found to be related to the wake mode. The scale of wake will saturate, and the shear layer will become weaker and merges into the wake structure as it develops downstream. This allows direction for effective flow control strategies using this information.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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)0.0010.000

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 head0.003
GPT teacher head0.241
Teacher spread0.238 · 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 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

Citations9
Published2019
Admission routes2
Has abstractyes

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