Responsive envelope tessellation and stochastic rotation of 4-fold penttiles
Bibliographic record
Abstract
This paper reports on the design and control of a responsive envelope based on the rotation of tessellated components. The study investigates responsive and dynamic approaches for building facades and envelopes to regulate solar shading, light control, views and thermal gain within the building. It is well known that near real-time visual output from computational simulation can significantly impact the prediction of dynamic building-environment interactions and lead to the development of smart, adaptable, net zero energy buildings. To address these motivations, this paper reports the development of an experimental simulation of a responsive envelope based on using a 4-fold penttile scheme. The simulation is developed using a novel pentagonal approach involving component (tile) design, tessellation and control methods. The paper further elaborates on the geometry and control aspects of the facade subdivision and presents the results of applying this tessellation geometry to a building envelope shading study based on facade components with rotation. Finally, it tests the “responsiveness” to multiple performance metrics by applying a non-deterministic exploration method for the stochastic rotation of individual panels. The sophisticated evaluation of alternative envelope configurations for a set of performance criteria require a tighter computational coupling between modelling and control of dynamic geometry.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".