Optimisation of Thermal Efficiency in Parabolic Trough Solar Collectors: A Numerical Simulation Study Across Seasonal Variations
Bibliographic record
Abstract
Thermal efficiency serves as a crucial indicator of performance, facilitating the prediction of the overarching functionality of large-scale systems through the analysis of parabolic trough behavior in relation to the working fluid's temperature.This study examines the influence of heat loss on the efficiency of collectors, employing a numerical simulation to distribute the tube's thermal efficiency across the four distinct seasons.A comprehensive heat transfer model for the thermal analysis of parabolic trough solar receivers has been developed, considering significant variables such as solar intensity, the flow rate of heat transfer, and heat losses.This research endeavors to evaluate the performance of a specifically designed 24-metre parabolic trough solar collector (PTSC) under Cairo's climatic conditions, focusing on surface temperatures and thermal efficiency.The system was numerically analysed during winter, from January 15 to January 21, highlighting efficiency metrics across the week at a mass flow rate of 1.41 kg/s, where efficiency did not surpass 35% on the concluding day.The proposed model incorporates precise heat transfer correlations and an in-depth examination of radiative heat transfer, aiming to underpin the foundation for the manufacture of larger units within Egypt.The results indicate that thermal efficiency is critically impacted by heat loss, underscoring the necessity for refined models that accurately represent heat transfer dynamics within parabolic trough solar collectors.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".