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Record W2075401311 · doi:10.2514/1.9751

Influence of Jet Inlet Conditions on Time-Average Behavior of Transverse Jets

2005· article· en· W2075401311 on OpenAlexaff
Marina Campolo, Gian Maria Degano, Alfredo Soldati, Luca Cortelezzi

Bibliographic record

VenueAIAA Journal · 2005
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsNozzleMechanicsJet (fluid)Transverse planePlenum spacePenetration (warfare)PhysicsAerodynamicsComputational fluid dynamicsEngineeringThermodynamicsStructural engineering

Abstract

fetched live from OpenAlex

Dynamics and dispersion mechanisms in transverse jets are partially controlled by jet-exit conditions that are intimately linked to the coupling between issuing jet and crossflow. Accurate knowledge of this coupling is crucial to plan repeatable and effective experiments, perform accurate computations, and design dispersion control devices. A simplified geometry is focused on, representing a plenum/nozzle and a wind/water tunnel, to characterize the time-averaged extent of the coupling between the jet and crossflow and its effect on jet penetration, with specific emphasis on the following: 1) The relative importance of simulating/neglecting the coupling between jet and crossflow within the nozzle/plenum (pipe) is established to reproduce the jet penetration observed experimentally. 2) The distance down the pipe is characterized to determine how far down the presence of the crossflow modifies the flow with respect to the case of a jet issuing in a quiescent fluid. 3) Variations calculated in jet penetration are quantified when different boundary conditions are used to simulate the jet. 4) The effect of different crossflow velocities at jet exit on simulated jet penetration is evaluated. Results discussed may provide a guideline for future computational investigations on transverse jets and a useful reference to understand the discrepancies observed between experimental and numerical results.

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 score0.253
Threshold uncertainty score0.372

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 head0.005
GPT teacher head0.221
Teacher spread0.216 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

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