Energy and exergy analysis of flat plate solar collector for three working fluids, under the same conditions
Bibliographic record
Abstract
The growth and expansion of the population, has caused increased the use of energy in the last few years. One of the cleanest and renewable sources of the energy is the solar energy. The solar energy can be collected by solar collectors. One of the solar collectors is the flat plate solar collector (FPC), that it is used in domestic utilization. Use of various Nano-fluids to improve the thermal properties of solar collectors, considered as one of the most effective method to optimize the flat plate collectors. In this study, a FPC in terms of energy and exergy, for three fluids (water, air and TiO2 Nano-fluid) have been investigated. According to the results obtained and under the same conditions, destruction exergy of water is more than other two fluids and TiO2 Nano-fluid has the least amount of destruction exergy. Also, by increasing in the total radiation on tilted surface (Gt) TiO2 Nano-fluid’s exergy efficiency is more than the other fluids in this study. By increasing ambient temperature, the exergy efficiency decreases, that water has the most variation. Due to the temperature range of the inlet working fluid to the collector’s tubes, observed that outlet temperature of the TiO2 Nano-fluid is about 50°C higher than when water enters it. Therefore, the initial statement about Nano-fluids is confirmed. In appropriate conditions, the collector’s efficiency is between 45% - 50%, thus FPC is one of the best devices for domestic utilization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".