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Record W4213307060 · doi:10.3390/atmos13020360

Influence Mechanism of a Bridge Wind Barrier on the Stability of a Van-Body Truck under Crosswind

2022· article· en· W4213307060 on OpenAlexaff
Zhiqun Yuan, Dandan Xia, Xiaobo Lin, Lin Li, Yufeng Liu, Yuehan Li

Bibliographic record

VenueAtmosphere · 2022
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsUniversity of Regina
FundersFujian Provincial Department of Science and TechnologyNatural Science Foundation of Fujian ProvinceNational Natural Science Foundation of ChinaU.S. Department of Transportation
KeywordsCrosswindWind speedStructural engineeringTruckEnvironmental scienceWind gradientBridge (graph theory)Wind tunnelMarine engineeringAerodynamicsWind directionDeckEngineeringMeteorologyAutomotive engineeringAerospace engineering

Abstract

fetched live from OpenAlex

Understanding the crosswind stability of cars under strong wind loads and research on wind resistance methods is important for improving the safety performance of wind-induced driving on bridges. Taking van-body trucks as the research object, numerical calculation methods and wind tunnel test methods are used to conduct the wind-induced driving safety analyses of van trucks on a cross-sea bridge. The influence of the structural parameters of the barrier-type wind barrier on the aerodynamic characteristics and straight-line driving stability of the trucks on the bridge is studied and analyzed quantitatively. The results show that the decrease in the porosity of the wind barrier can effectively reduce the average wind speed of the bridge deck, and increasing the height of the wind barrier can effectively reduce the wind speed and increase the occlusion height of the bridge deck. The lateral acceleration, yaw rate, and lateral displacement of trucks decrease with the decrease in the porosity of the wind barrier and decrease with the increase in the height of the wind barrier. The research conclusions can not only provide data support for wind-induced driving safety analysis and the wind-resistant design of bridges but also provide a new method to balance the requirements of bridge wind-induced driving safety and bridge wind-induced structure safety.

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 categoriesInsufficient 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 score0.122
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 integrity0.0000.000
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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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