MétaCan
Menu
Back to cohort
Record W4235901649 · doi:10.22215/etd/2019-13823

Computations and Measurements of the Effects of Trailing-Edge Geometry on the Wake of Bluff Bodies

2019· dissertation· en· W4235901649 on OpenAlexaff
Utku Caylan

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsWakeBluffTurbulenceInstabilityTrailing edgeMechanicsPhysicsScale (ratio)GeometryMathematics

Abstract

fetched live from OpenAlex

This study has aimed to shed further light on the wake dynamics of a bluff body with a streamlined fore body and a rectangular blunt base through experiments and numerical analysis. Specifically, the study was designed to establish the role of instability modes and small-scale turbulence in shaping the wake dynamics, and the effect of boat tails including lobed geometries on the wake structure. In the absence of small-scale turbulence, the near wake of the body is dominated by the effects of two instability modes that promote larger-scale transient motions. Small-scale turbulence is found to have a significant suppressing effect on these motions. Presence of a straight tail is observed not to alter the fundamental structure of the wake, but the relative roles of the instability modes affecting the wake and the resultant streamwise mixing rates are modified. The straight tail is noted to reduce the initial size of the wake significantly to yield substantial reduction in form drag. Shaping the tail into a lobed trailing edge is found to modify the initial cross-section of the wake to an extent to negate the drag-reduction benefits produced by the tail in the straight configuration. Furthermore, the lobed configuration introduces three-dimensional coherent vortical structures at the tail trailing edge that dominate the near-wake development as well as promoting cross-stream mixing farther downstream.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.277

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicFluid Dynamics and Vibration AnalysisFrench-language works237,207