Elastic kinetic coupling for hygroscopic amplification in climate responsive ventilation shingle
Bibliographic record
Abstract
Envelope systems that can passively and autonomously respond to climate conditions are a valuable, sustainable strategy to improve building performance and reduce energy consumption. Wood bilayer shape-change actuators are cost-effective to produce and can be precisely programmed to respond to target environmental conditions, but their response speed is limited by the speed of moisture diffusion. The thicker and mechanically stronger the sample, the longer it takes to respond. Previous research has shown that some improvement in response time can be achieved through the coupling of bilayers and by integrating moisture diffusion channels within the bilayer architecture, but the response speed remains below the level that most occupants desire. In this paper we present an elastic kinetic strategy that can improve the response time of a hygroscopic wood actuator by augmenting the amplitude of the resulting shape-change deformation. The first section examines local biological role models that use elastic systems to achieve kinematic amplification. The second section presents the development of a wood bilayer and its integration into an elastic kinetic mechanism. The third section tests the integration of the elastic amplification mechanism into a proof-of-concept climate responsive shingle system for building ventilation purposes. The presented coupling of elastic components with passive hygroscopic actuators demonstrates faster response times through the increased range of motion of the hygroscopic actuator. The shingle application offers a valuable perspective for system integration within adaptive architectural building components, which can greatly contribute to improved building performance in climate adaptive applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".