Ocean Wave Energy Converter Mid-Fidelity Numerical Simulation Tools: A Review
Bibliographic record
Abstract
Ocean wave energy holds immense potential as a renewable energy source, offering a vast, untapped resource. Despite this, wave energy converters (WECs) are still in their technological infancy, and their levelized cost of energy (LCoE) is not yet competitive compared to other generation sources. Most devices currently reside at technology readiness levels (TRL) 3 through 5, relying heavily on mid-fidelity numerical modelling to proceed toward commercial viability. WEC developers often develop these tools in-house and for bespoke needs, meaning few are publicly available and flexible enough to meet the diverse needs of end-users. The past decade has marked the emergence of dedicated mid-fidelity WEC numerical modelling and simulation tools. In driving WEC designs towards commercial viability, a comparative evaluation of these tools is critical; it builds confidence in their reliability by helping understand the nuances between differing fundamental assumptions and modelling approaches, leading designers to make informed choices. Although several reviews were published shortly following the release of these tools in the mid-to-late 2010s, it has been some time since an updated thorough analysis has been performed. Over this period, some tools have undergone significant developments, whereas others have since been discontinued, creating a gap in the literature. Furthermore, existing analyses have focused on reviewing specific components through code-to-code comparisons, rather than a holistic discussion of the underlying fundamental theory and available features, leaving users without a clear understanding of subtle distinctions between tools. This study seeks to provide a qualitative analysis of publicly available ocean wave energy simulation tools, helping users navigate the market and select the technology that best suits their needs. Key features being examined include formulations of the hydrodynamics, wave, mooring, PTO and control problems, and the multibody dynamic solver. Within these domains, minute variations in assumptions and modelling approaches define the distinctions between tools. Namely, it is the distinction of formulating the hydrodynamic problem using Morison’s and/or Cummins equation. Additionally, some tools are designed for general ocean engineering applications and thus have limited capacity to model and simulate features specific to wave energy such as a PTO and associated control. This review offers valuable insights into the distinctions between general mid-fidelity wave energy simulation tools and aims to assist designers in selecting the tool best suited to meet their unique requirements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".