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Record W4386953075 · doi:10.1115/omae2023-103996

On Implementing Cummins Equation to Represent Accurate Wave Radiation Forces in Modelica

2023· article· en· W4386953075 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicModeling and Simulation Systems
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsConvolution (computer science)Benchmark (surveying)ModelicaComputer scienceApplied mathematicsSimulationMathematicsArtificial neural network

Abstract

fetched live from OpenAlex

Abstract This work explores the development of a Modelica™ compliant Ocean Engineering Toolbox (OET). Focusing on a symbolic solution to the Cummins equation for a floating, heaving body subject to polychromatic wave excitation forces, three formulations of the radiation convolution (symbolic, numerical, and state-space) are investigated and validated using the benchmark WEC-Sim. The presented formulation is tested using the model parameters of the Reference Model-3 (RM3) heaving point absorber Wave Energy Converter (WEC). For a preset excitation force, a 300-second simulation of the OET yields accurate results when compared to WEC-Sim. Computational speed measurements also indicate significant improvements in simulation efficiency compared to solving the radiation convolution integral numerically.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score0.259

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.141
GPT teacher head0.355
Teacher spread0.214 · 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

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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