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Record W7161985802 · doi:10.82308/44678

Turbulence generated in a multi-fan wind tunnel

2022· dissertation· en· W7161985802 on OpenAlexaboutno aff
Austin L'Ecuyer

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsnot available
Fundersnot available
KeywordsWind tunnelFlow (mathematics)TurbulenceInletWind engineeringAerodynamicsWater tunnelWind shearSIGNAL (programming language)

Abstract

fetched live from OpenAlex

"With the rise in popularity of unmanned aerial vehicles (UAVs), there is a need to test these vehicles in a laboratory setting. A Multi-fan wind tunnel (MFWT) facility has been developed and built at McGill for this purpose, and comprises of 81 small fans arranged in a 9-by-9 grid. A MFWT allows the user to create tailored flows with minimal pressure losses, due to the absence of screens and grids. The small fans used in the MFWT also have a fast dynamic response which can be modulated to mimic wind gusts or other naturally occurring wind phenomenon. Before UAVs can be tested in the facility, the flow characteristics of the tunnel must be well documented, with the goal of creating well defined tailored flow fields. The scope of this study is to examine the baseline flow characteristics in the development region of the flow (the region near the wall of fans) and how the inlet conditions of the tunnel can be varied to create different conditions in the flow field. Although dynamic modulation of the wind tunnels fans is possible, this study focuses on the static case where individual fans do not change speed while in operation. In addition to the baseline case with all fans on, different shear ratios of fans configured in a checkerboard pattern have been tested. The fans were wired in an alternating pattern and sent one of two inputs; with signal one and signal two wired to the fans in this alternating pattern, the fan wall resembles a checkerboard. The shear ratio is defined as the ratio of the velocities produced by the two fan signals, Sr = Uhigh/Ulow, with the larger signal as the numerator (shear ratio is always greater to or equal to one). A two-component hot-wire anemometer probe was mounted to a 3D traversing sys- tem inside of the tunnel to obtain the streamwise and transverse velocity components. Shear ratios from 1.00 to 3.50 were investigated, as well as the on-off case; all cases had a bulk flow velocity of 5ms−1. It is found that shear ratios larger than 1.00 produced a noticeable increase in the turbulence intensity, turbulent kinetic energy and integral length scale. At the closest and farthest measured points to the fan wall, an increase of 25.09 % and 14.75 % in turbulence intensity, respectively, was noted as the shear ratio was increased from 1.00 to 3.50 at each location. Critically, turbulence in the vicinity of the fans was found to exhibit non-equilibrium turbulent properties, whereby the non-dimensional dissipation coefficient Cε was found to be approximately inversely proportional to the Taylor-scale based Reynolds number Reλ (as opposed to equilibrium turbulence where Cε = constant). Suggested scalings are proposed for the increase in turbulence intensity, and a similarity with traditional grid turbulence scaling is presented for the decay of the turbulent kinetic energy."@eng

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.112
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.017
GPT teacher head0.278
Teacher spread0.262 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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