Investigation of Vegetable Oil as Thermal Fluid in Parabolic Solar Collector
Bibliographic record
Abstract
Because of limited sources of fossil fuel, renewable sources of energy are now getting concentration to meet up the huge demand of energy of modern technology. Among the renewable sources, solar energy source is considered to be the most important source because of its unlimited supply and numerous ecologically benign features. However, due to its diluted nature, it is required to concentrate this energy for fruitful utilization. Parabolic solar trough is one of the solar concentrators, for which a thermal fluid is used to absorb the solar radiation. However, most of the thermal fluids that are commonly used are synthetic and toxic in nature. In the frame of the sustainable development principles, it is necessary to establish a set of safe and non-toxic thermal oil for using in a solar collector. In this paper, both clean Canola oil and waste vegetable oil have been used as thermal oil to find out their performance. Experimental studies show a solar absorption efficiency of 77.8% and 37.67% with clean Canola oil and waste vegetable oil, respectively, can be achieved even at low solar irradiation conditions. All design criteria are investigated and reported in this paper.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".