Modeling of Ocean Thermal Energy Conversion (OTEC) Plant in Reunion Island
Bibliographic record
Abstract
Renewable Energy has a crucial interest for a remote area like Reunion Island. The supply of electricity based on renewable energy has many advantages but the major drawback is the production of electricity which varies highly according to the availability of the resource (wind, solar, wave, etc...). This causes a real problem for non interconnected electrical grid where intermittent renewable energies should be limited to a maximum of 30%. The ocean Thermal Energy Conversion (OTEC) provides an alternative of electricity production from the available energy of the oceans present all the time. By using surface hot water and deep cold water from the ocean, it is possible to operate a thermodynamics cycle, which will then generate electricity. In this article, in the first part a literary and technological review is carried out in two areas: electricity production and cooling of buildings with deep water. This study establishes a knowledge base on thermodynamic cycles consistent with the OTEC and on dimensional and functional parameters associated with this technology. Steady state simulations are presented to understand the operation of the system. Steady state models will evaluate the potential of the OTEC in distributing base electricity. These simulations will help evaluating the potential for new thermodynamic cycles such as the Kalina cycle. With these tools, a sensitivity study will evaluate the influence of different parameters on the cycle.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".