algorithmic modeling for Rhino
During form-finding of funicular networks, dynamic weights that update per time-step relative to a local measure are interesting... not least because such systems often converge when intuition says they should explode.
For the case of a tensile spring network in 3 dimensions, the equilibrium result is funicular so long as the network is at most valency-3 (incident edges per node) and therefore statically determinate. This determinancy means that tensile springs (e.g. zero natural length) can be replaced with rigid bars to form a funicular thrust network (Block, 2007) with identical forces.
This component provides a method for exploring these _trivalent_ networks within Grasshopper, in a similar way to using Kangaroo (note: you will get a warning if the system is not statically determinate).
You can explore networks with constant nodal forces (0D), dynamic based on line length (1D) or dynamic based on local area triangles (2D). The examples available in the link below give examples of each.
The component is open source, available on github and released under the MIT licence. It will appear under Extra>Rosebud following installation of the gha file in the libraries folder.
Download latest release (gha and example files) here:
A preprint describing the method in more detail is attached below. This paper was presented at the 2013 International Association for Shell and Spatial Structures 2013 Symposium:
Please send any queries to email@example.com or post comments below.
Block, P. & Ochsendorf, J. (2007). Thrust Network Analysis: A new methodology for three-dimensional equilibrium.
International Journal of Shell and Spatial Structures, 155.