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Record W4414219654 · doi:10.36688/ewtec-2025-730

Augmenting the Modelica<sup>TM</sup> Ocean Engineering Toolbox to Support Multibody Dynamic Simulations

2025· article· en· W4414219654 on OpenAlexaff
Thomas H. Hogan, Kush Bubbar

Bibliographic record

VenueProceedings of the ... European Wave and Tidal Energy Conference · 2025
Typearticle
Languageen
FieldComputer Science
TopicModeling and Simulation Systems
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsToolboxRenewable energyMarine energyWind powerSubmarine pipelineOffshore wind powerTidal power

Abstract

fetched live from OpenAlex

Ocean wave energy shows enormous promise as a renewable energy source, offering an extensive and energy-dense resource. Despite this potential, WECs have not yet seen the same widespread commercial adoption as other renewable sources such as wind and solar. This is largely due to their technological immaturity, with most WEC devices residing at technology readiness levels (TRL) 3 through 5. At these medium TRLs, developers rely on mid-fidelity time-domain numerical modelling simulation tools, as they offer an accurate low-cost alternative to experimental testing. There are only a handful of publicly available dedicated WEC simulation tools, especially ones capable of representing wave-to-wire (W2W) models. Furthermore, many of these tools are only available through commercial licences, WEC-Sim™ being the only widely adopted open-source alternative which even requires a commercial MATLAB™/SIMULINK™ licence. There is a lack of accurate and computationally efficient W2W WEC numerical simulation tools, particularly one that is open-source and does not require additional commercial software. In this work, we present the Ocean Engineering Toolbox (OET) – an open-source toolbox developed specifically to address this need. The OET is an open-source modelling and simulation toolbox developed by Sys-MoDEL™ and written in Modelica™ that is designed to simulate offshore structures including marine renewable energy (MRE) technologies. However, at present, only WEC technologies can be modelled, with a road map to incorporate tidal and offshore wind in future iterations. Previous works have established capabilities to represent the radiation state-space and excitation force spectral decomposition in regular and irregular wave conditions but were limited to a single body acting in one degree of freedom (DoF). This work addresses these limitations with an extensive overhaul of the existing codebase and integrating several key new features. Namely, the latest release introduces a comprehensive multibody dynamic architecture as well as additional components such as a: a) linear mooring, b) linear power take-off (PTO) and controller, c) viscous damping and drag, and d) equal energy method for frequency selection. Following the ethos of Modelica, this work integrates the principles of Object-Oriented Programming (OOP) to enhance code readability and reusability. Validation is performed by modelling the US Department of Energy’s (US DoE) Reference Model 3 (RM3) and using WEC-Sim as the benchmark. The requisite hydrodynamic coefficients are obtained from the WEC-Sim source code using the Boundary Element Method (BEM) solver WAMIT™. Preliminary simulations demonstrate good data coherence between the two tools. Although still early in development, the OET shows enormous potential to meet the demand for a fully open-source tool capable of efficiently conducting W2W simulations of various WEC archetypes.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.040
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0400.020

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 head0.017
GPT teacher head0.223
Teacher spread0.206 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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