A Simplified Method for Calculating the Effective Solar Optical Properties of a Venetian Blind Layer for Building Energy Simulation
Bibliographic record
Abstract
The use of venetian blinds to control solar gain through windows is common in both residential and commercial buildings, and their potential for reduction in peak cooling load and energy consumption is recognized to be large. As such, there is a strong need for models that allow a venetian blind to be included in glazing system analysis. A simplified method of calculating the “effective” solar optical properties of a venetian blind is presented. The solar optical properties of the entire blind are determined based on slat geometry and the optical properties of the slat material, and on the direction and nature (beam or diffuse) of incident radiation. The slat material optical properties are assumed to be independent of the angle of incidence, and are assumed to transmit and reflect beam radiation diffusely. As a first approximation, the slats are assumed to be flat with negligible thickness. A correction is then developed and applied that accounts for the curvature of the slat. The results of the flat and curved slat models are compared with experimental data for commercially available venetian blinds. Both models demonstrate excellent agreement with experiments, except when the profile and slat angles are aligned. In that case, the flat slat model predicts blind transmissions that are too large. The models developed in this study provide useful input to multilayer glazing/shading models used for rating or for building energy simulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".