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Record W4300891416 · doi:10.5957/icetech-2006-126

Mathematical Modeling of Ice-Hull Interaction for Ship Maneuvering in Ice Simulations

2006· article· en· W4300891416 on OpenAlexaff
Jiancheng Liu, Michael Lau, F. Mary Williams

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsNational Research Council CanadaMemorial University of Newfoundland
Fundersnot available
KeywordsHullBuoyancyMarine engineeringComputationParametric statisticsSea iceSimulationParametric modelComputer scienceMeteorologyEngineeringMechanicsMathematicsPhysicsAlgorithm

Abstract

fetched live from OpenAlex

A new ice-hull interaction model using the analytical approach with numerical implementation for simulating various ship maneuvers in level ice is presented. Its simple physical detail and short computation time make it very suitable for real-time simulations. In the model, the icebreaking process was numerically simulated in the time domain. Three independent ice force components, the breaking, buoyancy and clearing forces, representing individual processes identified during a typical ice-hull interaction, are calculated separately and summed as the total ice force. The model is benchmarked against measurements from PMM model tests. A preliminary parametric analysis on the channel width and yaw moment due to the ship motions is carried out using the model. The simulation results demonstrate the capability of the model for the real-time simulations.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.253
Teacher spread0.225 · 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

Citations13
Published2006
Admission routes1
Has abstractyes

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