MétaCan
Menu
Back to cohort
Record W4386069626 · doi:10.1017/jfm.2023.580

Impact force of roll waves against obstacles

2023· article· en· W4386069626 on OpenAlexafffund
Boyuan Yu, Vincent H. Chu

Bibliographic record

VenueJournal of Fluid Mechanics · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFroude numberObstacleMechanicsPhysicsFront (military)Perturbation (astronomy)InstabilityShock waveBow waveFlow (mathematics)GeologyClassical mechanicsMeteorology

Abstract

fetched live from OpenAlex

The roll waves in open-channel flow on steep slopes can strike an obstacle with great force. We conducted two-dimensional shallow-water simulations to study the impact force of the waves against structures of various shapes and orientations. The focus is on the front runner of a wave packet developed from spatial instability. The numerical results include the stand-off distance of the bow shock wave, the front face's run-up height and the wave force on the obstacle. The strength of the impact depends on the Froude number of the undisturbed flow and the obstacle's distance from the local disturbance but not much on the form of the perturbation that initiates the instability. The wave force could reach a peak of more than an order of magnitude greater than the force on the structure without the roll waves. However, an obstacle with a sharp and pointy front can deflect the incident waves, significantly reducing the impact force.

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)0.001
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.257
Teacher spread0.245 · 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

Citations12
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Fluid MechanicsSame topicFluid Dynamics Simulations and InteractionsFrench-language works237,207