Octet Truss for Topology Optimization

Random Octet Truss Array (on shapeways)

This post demonstrate a work-flow for topology optimization using open source tools (with mixed success). The approach uses an unpenalized method (see wiki) that maps the material density output from the optimizer to unit-cells based on the octet truss (inspired by this white paper, see pp10). As some further motivation for this approach, the students working on the record-setting human powered helicopter demonstrated that multi-scale trusses (trusses with elements made of smaller trusses) were a very efficient structural concept (see the comments for further references on multi-scale structures). One of the benefits of not penalizing (using variable density solutions rather than trying to achieve predominantly solid-void solutions) is that we don't need to spend time doing parameter continuation on the penalization exponent.

Interesting Developments in the Numerical Python World

From Travis Oliphant by way of the Planet Scipy feed:

Hello all,

There is a lot happening in my life right now and I am spread quite thin among the various projects that I take an interest in. In particular, I am thrilled to publicly announce on this list that Continuum Analytics has received DARPA funding (to the tune of at least $3 million) for Blaze, Numba, and Bokeh which we are writing to take NumPy, SciPy, and visualization into the domain of very large data sets. This is part of the XDATA program, and I will be taking an active role in it. You can read more about Blaze here: http://blaze.pydata.org. You can read more about XDATA here: http://www.darpa.mil/Our_Work/I2O/Programs/XDATA.aspx

I personally think Blaze is the future of array-oriented computing in Python...

Passing the torch of NumPy and moving on to Blaze

Open Source Topology Optimization for 3D Printing

Rough Hex Output & Smooth Surface Reconstruction for Dogleg

This post describes a set of open source tools for simple topology optimization for parts destined for 3D printing. The two Matlab codes (both are plain vanilla Matlab, so they work successfully in Octave too) are good introductions to topology optimization. The papers that go along with the codes provide great documentation and examples of tweaking and changing the scripts to treat various problems. The python implementation is more capable (and a few more lines of code).