A Review on Ocean Energy Power Generation Technologies
Bibliographic record
Abstract
Ocean tidal currents, water waves and thermal gradients are a great source of renewable energy. Oceans cover almost 71 % surface of earth. Waves carry enormous amount of energy within them. Tides generated due to gravitational force between earth and moon and partly between earth and sun help in generating high heads of water to harness pollution free energy. Due to high cost of generation and lack of enough research, this sustainable source of energy has not been fully exploited yet. Although the technology used in harnessing marine energy is considered as a threat to aquatic life, but suitable methods and focused research in this area can lead to take advantage of this abundant source of renewable energy. Ocean tidal, osmotic, wave and thermal sources have annual potentials of 800, 2,000, 8000–80,000 and 10,000– 87,600 TWh, which are more than global 16,000 TWh/y electricity demand. Ocean wave generators produce relatively lower output, however, four to eleven meters tidal range stations have large power generation capacities. Abundant ocean heat energy potentially harvested using ocean thermal energy conversion (OTEC) devices and ocean thermo-electric generators (OTEG). Tidal stations may be tidal range or current types, but a wave energy converter (WEC) may be an oscillating water column (OWC), overtopping, heaving, pitching and surging devices. Ocean thermal energy can be harnessed by open, close Rankine cycles, thermo-electric generators and osmotic power plants. Large bays like Turnagain (USA), Annapolis/Minas Passage (Canada), Seven Barrages/Pentland Firth (UK), La Rance (France), Garorim (South Korea) and Mezen/Penzhin (Russia) have huge tidal current power generation capacities. Power Potential from tidal current stations is more than WEC devices which in turn is more than osmotic, OTEC and OTEG technologies. This paper reviews the current state-of-the-art of tidal, wave, OTEC and OTEG ocean energy technologies.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".