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Record W4412767268 · doi:10.1007/978-3-031-98893-6_1

CLIMATHUNDERR: A Combined Impinging Jet and Gravity Current Technique to Investigate Thermal Effects on Downburst Winds

2025· book-chapter· en· W4412767268 on OpenAlexaff
Federico Canepa, A. Guibert, Andi Xhelaj, Josip Žužul, Djordje Romanić, Alessio Ricci, Horia Hangan, Jean-Paul Bouchet, Philippe Delpech, Olivier Flamand, Massimiliano Burlando

Bibliographic record

VenueLecture notes in civil engineering · 2025
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsOntario Tech UniversityMcGill University
FundersHORIZON EUROPE Framework ProgrammeEuropean Commission
KeywordsJet (fluid)Current (fluid)MechanicsMeteorologyEnvironmental scienceThermalAtmospheric sciencesAerospace engineeringPhysicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Abstract A comprehensive experimental campaign was conducted as part of the ERIES-CLIMATHUNDERR project (CLIMAtic Investigation of THUNDERstorm Winds) at the Jules Verne Climatic Wind Tunnel, CSTB, Nantes, France. The study aimed to investigate the thermal effects driving downdraft winds from thunderstorm clouds and their influence on downburst outflow dynamics near the ground. Downbursts are typically simulated using two methods: (i) the gravity current (GC), which models jet formation through density instability between two fluids, and (ii) the impinging jet (IJ), which generates the downdraft mechanically using wind tunnel fans. While the IJ method is preferred in wind engineering for its scalability, it lacks the thermodynamic contributions intrinsic to GC-based simulations. For the first time, the CLIMATHUNDERR project combines these two techniques at large scale, leveraging varied temperature differentials between the jet and its surroundings to analyze the dynamic and geometric evolution of downburst outflows and associated vortex structures, particularly the leading primary vortex (PV). The experiments also include testing these reproduced flows over a scaled topographic model of the Polcevera Valley in Genoa, Italy, to assess real-world applications. Evolving flow and temperature fields are captured using state-of-the-art measurement techniques, including Large-Scale Particle Image Velocimetry (LS-PIV) and high-response thermocouples.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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
Published2025
Admission routes1
Has abstractyes

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