Long-Term Performances and Technoeconomic and Environmental Assessment of Al2O3/Water and MWCNT/Oil Nanofluids in Three Solar Collector Technologies
Bibliographic record
Abstract
Bringing together nanofluids and solar collectors has been widely discussed without any major advance or long-term study being carried out. In this context, this paper provides a useful feasibility study to help future decisions in using nanofluids in Solar Water Heating Systems (SWHSs) in different locations. The performances of SWHSs using the nanofluid-based flat plate solar collector (FPSC), evacuated tube collector (ETC), and compound parabolic collector (CPC) under the Mediterranean, arctic, and desert climate conditions are presented and discussed. The analysis is carried out using a transient-based numerical approach, solving energy balance equations for different systems. Various performance factors such as energy saving, solar fractions, and environmental impacts of auxiliary energy supplies are evaluated to feasibly assess the use of nanofluids in such devices. Simulation results demonstrate that the use of nanofluids increases the solar heater performance which reduces considerably the payback period ( P P ) of the investment in solar heating systems up to 3.34 years in Tunisian climate. Under Quebec’s climate region, the annualized solar return of the ETC system increases from 4874.65 US$ to 9785.93 US$ by adding 0.06 v% Al2O3 in water. Also, the use of nanofluids in solar collectors with electric auxiliary heaters reduces harmful CO2 emissions up to 0.49 tons/year.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".