Evaluation of MWCNT/ethylene glycol nanofluid flow in a parabolic trough collector with glass-glass absorber tube
Bibliographic record
Abstract
Purpose Because of its increased absorptance in fluid and reduced heat loss, direct absorption nanofluid (DANF) is receiving intense interest as an efficient way to harvest solar energy. This work aims to investigate, for the first time, the application of DANF in parabolic trough collectors (PTC), a promising collector for solar thermal systems. Design/methodology/approach A representative flow and heat transfer study of different fluids in a straight tube is conducted, and the basic energy equation and radiative transfer equations are numerically solved to obtain the fluid temperature distribution and energy conversion efficiency. Ethylene glycol (EG) and different concentrations of (i.e., 0.1-0.6 per cent) multi-wall carbon nanotubes (MWCNT) in EG are used as sample fluids. Four cases are studied for a traditional PTC (i.e., using metal tube) and a direct absorption PTC (i.e., using transparent tube) including a bare tube, a tube with an air-filled glass envelope and a tube with vacuumed glass envelop. The numerical results are verified by an experimental study using a copper-glass absorber tube, which reveals the good potential of DANFs. Findings Compared with a conventional PTC, using DANF shows an increase of 8.6 per cent and 6.5 K, respectively, in thermal efficiency and outlet temperature difference at a volume fraction (0.5 per cent) of nanoparticles. The results also show that the improvement in solar efficiency increases with increasing particle concentrations, and the vacuum insulated case has the highest efficiency. Originality/value In all previous studies, an important section was missing as the effect of photons on the direct solar absorption trough collector, which is considered in this study. This paper proposes a new concept of using direct solar absorption nanofluids for concentrated solar collectors and analyzes the performance of both absorptance and transmittance efficiency considerations. To reveal the potential of the new concept, an analytical model based on energy balance is developed, and two case studies are performed.
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 | 0.000 | 0.000 |
| 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".