Bibliographic record
Abstract
Abstract The maximum possible Carnot thermal efficiency of an Ocean Thermal Energy Conversion (OTEC) power system is about 7% as it exploits the water temperature gradient between the surface and the deep ocean, which is only slightly over 20 degree C in tropical waters. The overall efficiency of a multi-megawatt-sized OTEC electrical power plant, all inclusive from seawater to net electric power, is typically around 2%. Only about half of the water temperature gradient can be directly harnessed across the power cycle, as the rest is needed for heat transfer and mass flow to maintain economical heat exchanger and seawater pump sizing. About a third of the gross power generated is consumed pumping seawater, powering auxiliary systems, and in power transmission losses. This paper reviews efficiency as it relates to the OTEC power cycle. OTEC has enormous potential as a source of clean, renewable, and base-load electricity for many nations, territories and states near tropical waters. As OTEC technology matures, large floating OTEC plants are expected to produce energy carriers or synthetic fuels that can be shipped to energy consumers. This paper will provide a current review of the various components used by one OTEC power cycle, their respective efficiencies and their contribution to the overall OTEC power plant economics. A thorough understanding of the efficiency losses, power burden, and potential areas for optimization is critical for OTEC to become an economically viable resource. This paper provides recommendations for the design of the OTEC plant main components and gives guidelines to optimize efficiency such that high quality power can be sold to the utility at competitive rates. Finally, the results of a parametric study of OTEC size in terms of Megawatts produced shows the efficiency gains that can be achieved with large scale OTEC power generation plants. I. Introduction Global energy demand is increasing rapidly. Growing populations and higher standards of living worldwide are pushing the limits of existing energy resources. At the same time hydrocarbon reserves are becoming harder to find. There is growing global concern for energy security and environmental sustainability. Global development and commercialization of renewable technologies is required before existing resources become scarce. For instance: wind energy has been used through the centuries but was surpassed during the Industrial Revolution by coal and oil. However, over the past decade, wind power has seen exponential growth as an energy resource (2009 US DOE). Solar and wind energy have the economic advantage of free " fuel?? but come with the intermittent and variable nature of that " fuel?? source. Ocean thermal energy enjoys the benefit of the large thermal storage of ocean waters and can therefore provide a base-load source of electricity to coastal areas in tropical waters and energy carriers to energy consumers. Figure 1 is a map showing the vast worldwide OTEC resource.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.016 |
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".