Parametric analysis of a two-body floating-point absorber wave energy converter
Bibliographic record
Abstract
In the evolution of floating-point absorber wave energy conversion systems, multiple-body systems are gaining more attention than single-body systems. Meanwhile, the design and operation factors affecting the performance of multiple-body systems are much greater than those of single-body systems. However, no systematic study has yet been presented. In this article, a theoretical model is proposed by using a coupled oscillator system consisting of a damper-spring system to represent a two-body system (the floating body and the reacting body). Dimensionless expressions for the motion response and wave power absorption efficiency are derived. With the newly developed model, we prove that an appropriately tuned two-body system can obtain a limiting power absorption width of L/2π (L is the incident wavelength) as much as a single-body system. The generic case of a two-body system is presented with numerical simulations as an example. The results show that increasing the damping coefficient can reduce the wave frequency at which the peak of power absorption efficiency occurs. Increasing stiffness can make the wave frequencies for high power absorption efficiency move to a higher frequency region and can also make the spectrum bandwidth for high power absorption efficiency become narrower. Further, we show that the two-body system can absorb more wave energy at low wave frequencies than the single-body system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".