Numerical Investigation of the Overall Performance of a Square Receiver for a High-Temperature Concentrating Photovoltaic Thermal System
Bibliographic record
Abstract
Abstract To sustainably meet the growing global demand for modern energy services, clean, renewable, and sustainable energy systems must be developed and deployed. Concentrating photovoltaic thermal (CPV/T) systems effectively utilize the solar spectrum by simultaneously generating heat and electricity, achieving high overall efficiencies. While significant research has focused on the design and optimization of low-temperature CPV/T systems, relatively few studies have explored the performance of high-temperature CPV/T systems, particularly regarding their potential for efficient electrical energy production using high-efficiency, high-temperature solar cells on smaller areas. This study aims to investigate the performance of a square high-temperature concentrating photovoltaic thermal receiver for a parabolic trough solar collector (PTSC) with a geometric concentration ratio of 121. A validated Monte Carlo ray-tracing method implemented in SolTrace was employed for the optical analysis. The thermal performance of the receiver was evaluated using a finite volume-based computational fluid dynamics approach. Pressurized water at 6.9 MPa, with a flow rate of 7.64 m3/hr and an inlet temperature of 400 K, was used as the heat transfer fluid. Under these conditions, the system achieved maximum thermal efficiency and solar electrical efficiency of 52% and 35%, respectively.
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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 | 0.000 |
| 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) | 0.003 | 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".