Bibliographic record
Abstract
Thermal energy extraction from solar pond is theoretically studied with the use of Rankine cycle heat engine.The Rankine power cycle configuration consists of evaporator, turbine, condenser, feed pump and R-134a which is used as the working fluid.A solar pond is considered as the heat source for the evaporator.Fresh water circulates through an internal heat exchanger, located in the lower convective zone of the pond, and transfers its thermal energy to evaporator.The heat that is absorbed increases the temperature of a working fluid and causes the working fluid to vaporize.When the working fluid has taken enough potential energy, the fluid vaporizes and begins to rise, thereby converting some of the potential energy to kinetic energy.The vapor flows under high pressure to the turbine and thereby expanding through the turbine from a higher pressure to a lower pressure.Useful work can be extracted from this expansion process.The turbine is placed in between the evaporator and condenser section of the cycle.The water is acting as heat sink for the condenser.An attempt is made to analyze the thermodynamic aspects of the cycle and the net works generated by the system are elaborated.The report presents the analysis of an alternative method of thermal energy extraction from solar energy thermal resource with the use of solar pond.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.312 | 0.135 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".