Unknown

Dataset Information

0

Modeling and design of thin bending wooden bilayers.


ABSTRACT: In recent architectural research, thin wooden bilayer laminates capable of self-actuation in response to humidity changes have been proposed as sustainable, programmed, and fully autonomous elements for facades or roofs for shading and climate regulation. Switches, humidistats, or motor elements represent further promising applications. Proper wood-adapted prediction models for actuation, however, are still missing. Here, a simple model that can predict bending deformation as a function of moisture content change, wood material parameters, and geometry is presented. We consider material anisotropy and moisture-dependency of elastic mechanical parameters. The model is validated using experimental data collected on bilayers made out of European beech wood. Furthermore, we present essential design aspects in view of facilitated industrial applications. Layer thickness, thickness-ratio, and growth ring angle of the wood in single layers are assessed by their effect on curvature, stored elastic energy, and generated axial stress. A sensitivity analysis is conducted to identify primary curvature-impacting model input parameters.

SUBMITTER: Gronquist P 

PROVIDER: S-EPMC6191116 | biostudies-other | 2018

REPOSITORIES: biostudies-other

altmetric image

Publications

Modeling and design of thin bending wooden bilayers.

Grönquist Philippe P   Wittel Falk K FK   Rüggeberg Markus M  

PloS one 20181016 10


In recent architectural research, thin wooden bilayer laminates capable of self-actuation in response to humidity changes have been proposed as sustainable, programmed, and fully autonomous elements for facades or roofs for shading and climate regulation. Switches, humidistats, or motor elements represent further promising applications. Proper wood-adapted prediction models for actuation, however, are still missing. Here, a simple model that can predict bending deformation as a function of moist  ...[more]

Similar Datasets

| S-EPMC3245222 | biostudies-literature
| S-EPMC4027886 | biostudies-literature
| S-EPMC4199938 | biostudies-literature
| S-EPMC4939595 | biostudies-other
| S-EPMC8972245 | biostudies-literature
| S-EPMC3140867 | biostudies-literature
| S-EPMC4044725 | biostudies-literature
| S-EPMC4621613 | biostudies-literature
| S-EPMC8626450 | biostudies-literature
| PRJNA717160 | ENA