Thermofluids analysis of four novel anchor-shaped turbulator and eco-friendly nanofluid (GAGNPs /H2O) in a parabolic trough solar collector: A CFD modeling approach
Bibliographic record
Abstract
• The hydrothermal properties of a green nanofluid (GAGNPs/ H 2 O) and four novel anchor-shaped turbulator are studied in PTSC. • Changing the PTSC from simple tube to FEA-f increases the Nusselt number by approximately ∼ 16.67 %. • Changing the PTSC from FEA-f (H: 34 mm) to FEA-f (H: 58 mm) increases the Nusselt number by approximately ∼ 18.26 %. • Changing the inlet temperature from 300 K to 345 K decreases the efficiency value by approximately ∼ 3.04 %. In this paper, four new turbulator models are implemented inside the absorber tube of the Parabolic Trough Solar Collector (PTSC) in a linear arrangement (anchored shape) to regulate and standardize its surface temperature. The study analyzes the impact of parameters such as heat transfer coefficient (h), friction factor (f), Nusselt number (Nu), and outlet temperature (T out ). Moreover, a new type of nanofluid (GAGNPs/H 2 O) has been utilized, consisting of gallic acid combined with graphene nanoplatelets (GNPs), known for its environmental friendliness. The solar heat flux (SHF) in the environment is calculated using the Monte Carlo Radiation Transfer Method (MCRT) with C++ code. The key findings indicate that at Reynolds number 25,000, replacing the simple absorber tube with the DEA, DEA-f, FEA, and FEA-f models increases the Nusselt number by ∼ 3.99 %, ∼ 5.40 %, ∼ 14.08 %, and ∼ 16.20 %, respectively. Additionally, increasing fin height from 34 mm to 58 mm at this Reynolds number results in ∼ 18.26 % increase in the Nusselt number, while increasing the outlet temperature by ∼ 0.08 %. Increasing the top height from 34 mm to 58 mm can increase efficiency by up to 8.20 %. The efficiency of the PTSC decreased by approximately ∼ 3.04 % when the inlet temperature was increased from 300 K to 345 K in FEA-f turbulator (H: 58 mm). Furthermore, increasing the concentration of GAGNPs/H 2 O nanofluid from 0.025 % to 0.1 % in the same FEA-f turbulator (H: 58 mm) resulted in ∼ 4.50 % increase in efficiency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".