Flexinol is a brand name for a specifically programmed shape memory alloy that contracts by 4.5% when heated to a specific temperature and then returns to its initial length. In our case, a flexinol wire connects two points of a planar frame, causing it to bend.

physical model

After physical testing of various frame materials, thicknesses, geometries and anchoring points of the flexinol, we decided to simulate the system’s behavior, using grasshopper and kangaroo.

The frame geometry we are simulating is the pentagon. The lower edge is anchored and the flexinol wire runs from 11 possible point positions on that edge, to 11 point positions on each of the two upper edges, as shown on the diagram.

Flexinol anchor points

The parameters we set in our grasshopper definition are:

- The pentagon’s radius and frame’s offset distance

- The flexinol’s lower anchor point

- The Flexinol’s upper anchor point

- The frame’s stiffness

The optimization run by galapagos will give us the configuration with the maximum ratio of displacement / flexinol length.

Galapagos run more than three generations, from which point on it kept crushing, so the results are not pointing towards a specific configuration. At this point we decided to work with octopus for the optimization procedure.

galapagos results

Working with octopus proved more stable, but still left us with lots of invalid configurations.

Octopus, nevertheless, let us organize the results on two axis, for the two main fitness parameters, the flexinol’s length and the frame’s displacement (in radians).

Octopus two-axis results visualization

By simplifying the parameters and reducing them to only control the anchor points, we get more specific and accurate results.

Octopus visualization of the simplyfied optimization

The optimum solutions can be seen on the “pareto front”, the configurations that give results closes to the optimum of the two axes. Each result can be selected and presented on grasshopper.

The “pareto front” with the optimum elite

The army of solutions given by octopus

One of the optimum solutions

Abstract:

It is nowdays possible to track your own everyday movements through GPS technologies. This can be used to improve the efficiency of your transportation, saving you precious time. It is possible to compare several means of transport and perhaps, choose the ones that are healthier such as walking, bicycles, skate board, roller skaters, etc. This mean it is possible to turn your transportation into your own everyday gym, saving you even more time!

Preparation

Tracking App used: My Tracks, Android. This App tracks your movement and saves it as a KMZ file, which we turned to XML file to use it in Grasshopper.

Self-Stalking:

For this assignment, we decided to track our movement form our apartment to IAAC using three means of transportation: walking, long board and bicycle. The tracking was done between the corner of Napols and Pujades, to IAAC entrance in Pujades 102, Barcelona-Spain.

The distance between the apartment and IAAC is about 1km.

Image from Google Maps Direction: A- Carrer de Nàpols, 9-11, Barcelona
B- Institute for Advanced Architecture of Catalonia, Carrer de Pujades, Barcelona

Visualize:

Other References

The map of reference was done using Open Street Maps data.  http://www.openstreetmap.com/

“Human” (formerly “HDT utilities”) plug-in was used to adjust the line weight of the map drawing.

http://www.food4rhino.com/project/human

The Akashi Kaikyō Bridge in Japan (1998)
http://en.wikipedia.org/wiki/File:Akashi_Bridge.JPG

Bridges have always been a challenge for engineers. Over the course of history, bridges are advancing technologically, both in terms of structural design and in terms of materials, allowing for bigger span lengths.

Suspension bridges hold the record of the longest span between ground columns. Although Suspension bridges had been previously built in China, England, and elsewhere in Europe, the first American suspension bridge was the Jacob’s Creek Bridge in Pennsylvania, built in 1801. It is claimed to have had a span of 21 meters.

Since then, suspension bridges have reached spans of up to 1,991 meters. This record length is attributed to the Akashi Kaikyō Bridge, constructed in 1998 in Japan.

[Sources:

http://en.wikipedia.org/wiki/Suspension_bridge
http://en.wikipedia.org/wiki/Jacob%27s_Creek_Bridge
http://en.wikipedia.org/wiki/Akashi_Kaiky%C5%8D_Bridge]

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Wikipedia’s list of longest suspension bridge spans
http://en.wikipedia.org/wiki/List_of_longest_suspension_bridge_spans

Wikipedia is one of the main sources of information and data online today. One of its features is the integration of data tables.

Data tables often contain information of ranking lists on various topics.

Bridges, of course, are not excluded, nevertheless suspension bridges in particular. Wikipedia’s list of longest suspension bridge spans contains 131 bridges [http://en.wikipedia.org/wiki/List_of_longest_suspension_bridge_spans].

By creating a csv file from this table, I imported the data into grasshopper.

I also modeled an abstract mesh geometry of a suspension bridge segment to use as a module for the visualization.

module model in created in Rhino

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The resulting visualization, involves the first 10 bridges of the list.

They are arranged in descending order of longest span.

Visual representation of the span length maintains relativity to the actual.

The bridges’ age is also roughly visualized by a gradient of blue, with the oldest bridges in lighter tone and the more recent in darker.

Capture of the baked result from Rhino’s perspective viewport