Exergetic Performance Assessment of Optimally Inclined BIPV Thermal System by Considering Cyclic Nature of Insolation
Bibliographic record
Abstract
Abstract The structural and architectural elements of building-integrated photovoltaic-thermal (BIPVT) systems are made up of photovoltaic (PV) modules and these are required to be fixed at an optimum inclination angle for generating maximum exergy. This work presents an attempt to determine the amount of exergy generated by an optimally inclined double-storied BIPV thermal system by considering the actual cyclic nature of insolation, surrounding air temperature, PV cell temperature, intermediate slab temperature, and the chamber temperature. The insolation value, which is computed by an anisotropic sky model along with these cyclic variables, is used for solving the set of governing differential equations for evaluating the exergy of the system. Other influencing parameters of the BIPV thermal systems such as air changes in both chambers, packing factor of PV module, the orientation of PV module, and thickness of the intermediate slab are considered for finding its effect on the total exergy of the system. Numerical results show that for packing factor more than 0.6, there is no significant change in total heat exergy with respect to the inclination angle. For packing factor more than 0.3, the generation of electrical exergy exceeds the heat exergy, and the overall exergy of BIPVT system decreases with rise in packing factor (βm) up to 0.3 and then rises nonlinearly.
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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 | 0.001 | 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".