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Record W2898481519 · doi:10.1115/fedsm2018-83443

Wind Flow Characteristics of a Model Downburst

2018· article· en· W2898481519 on OpenAlexaffabout
Junayed Chowdhury, Jubayer Chowdhury, Dan Parvu, Mohammad Amin Karami, Horia Hangan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsWind Energy Institute of Canada
Fundersnot available
KeywordsMeteorologyParticle image velocimetryWind speedGeologyEnvironmental scienceWind tunnelTornadoFlow (mathematics)Atmospheric sciencesTurbulenceMechanicsPhysics

Abstract

fetched live from OpenAlex

Downburst is an anti-tornado system with a slow rotating column of air slowly descending towards the ground which occurs due to the sudden downfall of air and precipitation generated from the cumulonimbus cloud. This natural event produces a strong downdraft which induces an outburst of damaging winds on or near the ground. This radially divergent wind with high velocity transpires when descending air strikes the ground which can cause immense damage to the ground mounted objects and structures. This paper discusses the wind flow characteristics of downbursts produced in the Wind Engineering, Energy and Environment (WindEEE) Dome at Western University, Canada. Downdraft diameter and speed were varied to produce several downbursts like flow. Point measurements using Cobra probes and surface measurements using Particle Image Velocimetry (PIV) were performed to analyze the wind flow field in detail. Instantaneous downburst wind speeds were decomposed into slowly varying mean and residual fluctuations for different averaging time. Velocity profile with height from WindEEE was compared with previous experiments and full scale data.

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 score0.040
Threshold uncertainty score0.080

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 head0.038
GPT teacher head0.236
Teacher spread0.197 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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