Analysis of a Mechanically Ventilated Multiple-skin Facade with Between-the-Panes Venetian Blinds
Bibliographic record
Abstract
A Building Integrated Photovoltaic/Thermal (BIPV/T) system that consists of a mechanically ventilated, multi-skin facade, a between-the-panes venetian blind layer, and a between-the-panes Photovoltaic (PV) panel is considered. Ambient air is drawn in and forced to flow upward through the system. As air moves through the system, it is heated by the blind layer, the glazing layers, and the PV panel. This BIPV/T system is especially attractive because it can produce electricity and thermal energy in the form of preheated fresh air and allow for adjustable daylighting. \n \nThere is a need to understand, design, and optimize BIPV/T systems. The velocity and temperature fields around the blind slats were experimentally and numerically studied. Experimental observations and numerical models are essential in understanding the complex fluid dynamical and thermal system and providing design and optimization guidelines. Solar-optical and Computational Fluid Dynamics (CFD) models were developed and validated at various blind slat angles and flow mean speeds. Particle Image Velocimetry (PIV) and temperature measurements were taken inside the ventilated facade. A simple empirical one-dimensional (1–D) model was developed, based on average surface temperatures and heat transfer coefficients, to quickly calculate average surface temperatures and heat flux rates. Between-the-panes convective heat transfer coefficients were obtained from CFD and used in the 1–D model. Despite high vertical temperature stratifications along the glazing, shading, and air layers, the 1–D model can predict the surface temperatures accurately and allow for future optimization and inclusion in building energy simulation software.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".