3-D FSI Simulation and Practical Experiments of Thermal Performance Enhancement on PVT in Tehran, Abadan, Baghdad, and Basra
Bibliographic record
Abstract
The pursuit of clean energy through advancements in thermal engineering and renewable energy research has led to the development of solar collectors and photovoltaic (PV) solar panels, with an emphasis on maximizing both thermal and electrical efficiencies.This study explores a thermal system designed to harness electrical energy from solar panels, provide cooling, and utilize waste heat to enhance the performance of the working fluid for various applications.The solution's accuracy was verified using ANSYS software for fluid simulations and Simulink for efficiency calculations.Results indicate that the average incoming solar flux throughout the summer quarter was highest in Basra, followed by Abadan, Baghdad, and Tehran.Peak output temperatures were observed at midday, with the system exhibiting maximum thermal efficiency during this period.A high degree of temperature convergence was noted, implying optimal system performance.Under identical operating conditions, including solar radiation from 8:00 AM to 5:00 PM and ambient temperature, the electrical performance of the system was assessed across the four cities.A positive correlation between solar radiation and electrical efficiency was observed, with Basra exhibiting the highest thermal and electrical efficiencies.The flow rate through the system's pipes was determined to be 0.0014 kg/s.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".