Experimental and Theoretical Modelling of Concentrating Photovoltaic Thermal System with Ge-Based Multi-Junction Solar Cells
Bibliographic record
Abstract
Climate change is one of the biggest environmental, political, economic, technological, and social challenges of the 21st century. Due to ever-increasing fossil fuels costs. The world energy system should be transitioned to renewable energy sources to mitigate greenhouse gas emissions. Solar energy is one of the suitable alternatives to fossil fuel usage. Currently, the most widely available solar technologies are solar photovoltaic (PV) and solar thermal. The integration of these two techniques enables the exploitation of the most significant amount of solar radiation. This combination has led to a hybrid photovoltaic/thermal system (PV/T). Concentrated solar radiation on PV cells, known as concentrated photovoltaic (CPV), effectively decreases PV receivers’ area and harnesses the same quantity of solar radiation. However, the main problem with CPV is the elevated PV surface temperature, which often requires active cooling. This issue can be solved by introducing a Concentrating Photovoltaic Thermal (CPVT) system. In this article, a new CPVT hybrid system based on Point Focus Fresnel Lens (PFFL) and embedded Multi Junction Photovoltaic (MJPV) (GaInP/InGaAs/Ge) cells has been experimentally investigated and numerically modelled under indoor conditions. Experiments and simulations were carried out at different heat transfer fluid (HTF) flow rates and under constant irradiation emitted from a sun simulator. The results indicate that the thermal and electrical performance of the CPVT system improves under the testing conditions, where the total efficiency was 68.7% and 73.5% for the experimental and CFD models, respectively. At the same time, the highest thermal efficiency of the experimental and CFD models was 49.5% and 55.4%, respectively. In contrast, the highest electrical efficiency was 36.5% and 37.1%. Therefore, the CPVT system has an excellent possibility of being competitive with conventional power generation systems.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".