Applications of nanofluids in geothermal: A review
Bibliographic record
Abstract
Geothermal energy is one of the most suitable sources of energy since it is possible to use it continuously for generating power and providing heat. Firstly, recent trends in geothermal energy are discussed and compared with other types of renewable energies. According to the results of this section, geothermal energy is an attractive choice for future power generation due to its low carbon dioxide emission and levelzied cost of electricity in comparison with other renewable energy sources. Afterwards, applications of nanofluid in geothermal-based energy systems are reviewed and their important results are represented. On the basis of literature review, using nanofluids can result in augment in geothermal systems. The enhancement is dependent on several factors including the type of nanofluid, concentration and system specification. According to the results of a study, the effect of using nanofluid on heat transfer rate became more significant at higher flow rates. In addition, using nanofluids can reduce the size of heat exchangers used in geothermal-based system. The main effects of employing nanofluids is increase in convective heat transfer and pressure loss.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".