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Record W4414964998 · doi:10.1038/s41597-025-06046-w

Aerodynamic dataset for selected doubly curved membrane canopy structures

2025· article· en· W4414964998 on OpenAlexaffabout
Anoop Kodakkal, Ann‐Kathrin Goldbach, Tibebu Birhane, Rodrigo Castedo-Hernandez, Guillermo Martínez-López, Máté Péntek, Kai‐Uwe Bletzinger, Roland Wüchner, Girma Bitsuamlak

Bibliographic record

VenueScientific Data · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsWestern University
FundersHORIZON EUROPE Framework ProgrammeHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsAerodynamicsWind tunnelRidgeFlow (mathematics)Scale (ratio)ArchBoundary (topology)CanopyConical surface

Abstract

fetched live from OpenAlex

A dataset of aerodynamic measurements is collected for doubly curved membrane structures as part of a comprehensive experimental testing campaign on wind effects on structural membranes conducted at the WindEEE Dome at Western University, Canada. Common doubly curved membrane geometries - the hypar, ridge valley, arch supported, cone, and umbrella - were tested in isolated instances. The cone geometry was also tested in both a 1 × 3 row and a 3 × 3 group arrangement. All models were tested at a 1:25 scale under atmospheric boundary layer (ABL) flow at angles of attack ranging from 0° to 180° in 10° increments (and 45°, and 135° -depending on the line of symmetry). In addition to ABL, the hypar geometry was subjected to two other distinct flow scenarios: tornado, and downburst. Pressure time series at various tap locations are included in the data. In total, approximately 425 tests were conducted, providing a comprehensive dataset on the aerodynamic behavior of doubly curved structures under wind loads. This experimental data set offers valuable insights for the design and analysis of such structures in architectural and engineering applications and future design guideline developments. Data is available on an open-source dataset Zenodo as part of the ERIES-WENSS project.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.002
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 head0.024
GPT teacher head0.284
Teacher spread0.260 · 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 designNot applicable
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 routes2
Has abstractyes

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