Thermal Performance Enhancement of Asphalt Solar Collector by Using Extended Surfaces
Bibliographic record
Abstract
The Urban Heat Island (UHI) effect occurs when the temperature of the asphalt pavement surface exceeds 70°C during the summer. Rutting is a significant temperature-related problem that occurs when the temperature rises too high on asphalt surfaces. Additionally, this phenomenon increases the amount of energy required to cool buildings adjacent to pavements and degrades air quality. The Asphalt Solar Collector (ASC) was examined in this work by inserting tubes into the pavement's construction and circulating working fluid within it to capture thermal energy generated by asphalt pavement. A low-carbon steel-alloy cheap waste materials have been investigated as an extended surface with HMA. The effect of various extended surfaces attached to the embedded tubes on the thermal performance of ASC has been studied to determine whether it satisfies specified aforementioned demands. The performance of several ASC models with bare, continuous finned, and mesh grid serpentine embedded tubes was investigated with same Conductive Hot Mixture Asphalt (C-HMA) by using a numerical 3-D model developed by COMSOL Multiphysics Software. when the Reynolds Number is increased, it is found that ASC efficiency increases from 66.74% for bare serpentine tubes to approximately 75.488% and 69.4% for continuous finned and mesh grid serpentine embedded tubes, respectively. A maximum value of about 398.53 W can be gained (from a total of 850 W/m2 incident solar radiation) by utilizing an extended surface. Additionally, the surface temperature of HMA decreases significantly from 52.67 to 46.07℃. For all models under investigation, it is clear that the optimum average Reynolds Number is about 600. It is found that the continuous fins model can capture more solar radiation than the mesh grid model by about 8.77%.
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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) | 0.000 | 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".