Steps towards fully nonlinear simulations of arrays of OWSC
Bibliographic record
Abstract
OWSCs in waves reflect, refract and radiate waves in different directions depending on geometrical,structural and dynamic properties of the flap. Array interaction describes the changes induced on theexcitation of flaps in an array compared to the excitation of a single flap.Renzi and Dias, 2013, Renzi et al., 2014, investigated array effects of OWSCs using a linearised semi-analytical and a linearised FEM method. However, research suggests that the applicability of linear meth-ods for the simulation of OWSCs is limited to very small flap angles (Crooks et al., 2014, Crooks et al., 2016).Linear inviscid assumptions seem to break down in typical operating conditions (Folley et al., 2004,Asmuth et al., 2014). \n \nIt can therefore be assumed that the accuracy of linear methods in predicting the characteristic wave pat-tern around a flap and the interaction between multiple such devices is limited when applied to realisticoperating conditions with typical pitch motion amplitudes.Although RANS CFD tools have been shown to reproduce the motion of single flaps in waves within thelevels of experimental accuracy and can provide detailed data of all field variables like surface elevation,pressure or velocity (Schmitt and Els ¨asser, 2015a), the simulation of arrays of WECs remains an openchallenge. Due to numerical dissipation water waves simulated using volume of fluid methods tend todiminish in height and require careful spatial and temporal discretisation.The simulation of multiple moving bodies requires adaptation of the mesh and constitutes a considerablecomputational effort. As with physical test facilities numerical wave tanks require non-reflecting bound-ary conditions and wave makers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".