223
Views
0
CrossRef citations to date
0
Altmetric
Original Articles

ElasticSpace: A computational framework for interactive form-finding of textile hybrid structures through evolving topology networks

&
Pages S4-S14 | Received 13 Oct 2016, Accepted 30 Jun 2017, Published online: 20 Nov 2017
 

Abstract

Textile Hybrid Structures are a novel type of structural system referring to the coupling of tensile form- and bending-active components into a stiffer construct. For form finding its static equilibrium shape, several computational frameworks built upon the Dynamic Relaxation method have been developed for the interactive exploration of material and geometric properties. However, efforts are still required when addressing dynamic alterations of topology without completely resetting the simulation. The main problem to face is the dynamic alteration of topological data without losing consistency of connectivity. In this paper, we present the development of a computational framework for form-finding textile hybrid structures which enables dynamic explorations of complex topological configurations during solver’s execution. A so-called evolving network formulation used to model mutable assemblies of interconnected particles is presented as well as the numerical scheme adopted to find the equilibrium state of such structures. The implementation of the framework is further described through the development of ElasticSpace, an interactive form finding tool for textile hybrid structures built with Java.

Topology-driven form-finding with ElasticSpace.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 763.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.