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Record W3116695875 · doi:10.1115/omae2020-19068

Numerical Study of Large Pendulum Ice Impact Loads

2020· article· en· W3116695875 on OpenAlexaffabout
Sthéfano Lande Andrade, Bruce Quinton, Claude Daley, Robert Gagnon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsNational Research Council CanadaMemorial University of Newfoundland
Fundersnot available
KeywordsPendulumWork (physics)Impact pressureComputer simulationMarine engineeringGeologySimulationComputer scienceEnvironmental scienceMechanicsEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract The large pendulum ice impact experiments performed at the Memorial University of Newfoundland recorded pressure distributions using a novel high-fidelity measurement device, the Impact Module, which is capable of fine spatial and temporal resolutions — effectively 2 cm2 at 500 Hz. These experiments achieved impact energies approaching 29 kJ, velocities of 4.7 m/s, and loads reaching 620 kN. The data obtained by the device are unique, as the Impact Module is capable of recording ice pressure data with both high spatial and high temporal resolution over a large contact area. Until recently, there was no ice load measurement technique capable of excelling in all these aspects. This work aims to study the simulation of a numerical test panel model under the action of the loads measured during the ice impact experiments. This is done by using a non-linear numerical model with explicit time integration capable of simulating the dynamic transient ice loads and comparing their effects to a quasi-static approach.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.999

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)0.0020.000

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 head0.015
GPT teacher head0.269
Teacher spread0.255 · 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 designObservational
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

Citations2
Published2020
Admission routes2
Has abstractyes

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