Octet Truss Memory, Time and Dollar Costs

Cost for 3D Octet Truss Arrays

	i	j	k	time (s)	stl (bytes)	WSF ($)
1	1	1	1	1.47	73384
2	2	2	2	28.74	435384	2.61
3	3	3	3	173.32	1342184	4.99
4	4	4	4	873.20	3052984	9.46
5	5	5	5	2952.67	5826984	16.69
6	6	6	6	6694.16	9923484	27.32

This is a follow-up to the previous post on using the octet truss for topology optimization. The memory cost of performing the union of all the truss members in BRLCAD to generate stl files for printing was too large for the ToPy dogleg example so I generated a set of stl files for a range of arrays of octet truss unit cells. Then I uploaded them to shapeways to see how much they would cost to print in the white-strong-flexible nylon material. The table shows how long the python script took to execute, the size of the resulting stl file, and the cost to print the part on Shapeways in the white-strong-flexible material.

Small Multiple Scatter Plots with Marginal Densities

An example data visualization in R inspired by Tufte's example on pp118-121 of Beautiful Evidence; source below the fold.

HIFiRE-5 Flight Test Preliminary Results and Video

Check out the preliminary results on AIAA's ARC site: HIFiRE-5 Flight Test Preliminary Results

Maxima Related Lisp Tutorials

In this previous post I mentioned that I learn a lot from the Maxima mailing list.

There was a recent question (somewhat frequently asked) about how to learn Lisp to be a better Maxima user / developer that is a great example of that. The thread is called Lisp tutorial. The original poster has a simple request:

Hi,
I have enjoyed using Maxima to solve differential equations. I would like
to learn lisp to complement Maxima. Can any of the maintainers of Maxima
(or others for that matter) recommend a lisp tutorial?
Many thanks

Ronald F. Modesitt

The list responded with a variety of resources.

• The Common Lisp wiki: Getting Started.
  
• Practical Common Lisp
  
• Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp (on Amazon), companion site by Norvig
  
• Common Lisp: A Gentle Introduction to Symbolic Computation by Turetsky has also been previously mentioned on the list
  

Norvig's PAIP (which I was quite happy to get as a present over the holidays) is especially relevant to Maxima users. Chapter 8, Symbolic Mathematics: A Simplification Program, has a simple symbolic algebra implementation, and Chapter 15, Symbolic Mathematics with Canonical Form, has a more capable solution. Both chapters are modeled on Maxima.

Nuclear Rockets Petition

As reported by NBC there is a petition on WhiteHouse.gov to rapidly develop and deploy a nuclear thermal rocket for both manned & un-manned space missions. I signed it; why not? Here's the short url: http://wh.gov/UVuD

Nuclear rocketry seems to be a popular topic of late. Gary Johnson has a recent post about nuclear rockets. The NBC article mentions NERVA. There is also the more recent Project Timberwind and Space Nuclear Thermal Propulsion program effort to develop nuclear upper-stages.

Scientific Computing Going Mobile/Embedded

I follow both the Octave and Maxima user mailing lists (I learn more from the Maxima list, but the Octave one still throws up a gem every now and then). I am also a founding member of the Dayton Diode hackerspace. So it was interesting to see some things of interest to makers/hackers pop-up on those two lists.

The first was a thread on the Octave list about installing Octave and the Octave Forge packages on a Raspberry Pi. In theory it should be as easy as any other Debian system using the package manager for the distribution. No word back from the OP yet on whether it works in practice or not.

The second thread to pop up was on the Maxima list about installing Maxima on Android systems. Maxima-on-Android is apparently a bundle on Google Play of Maxima 5.28.0, Gnuplot 4.6, Mathjax 2.1 and Qepcad 1.69. I have not played with it yet on any of my Android devices.

National Strategy for Advancing Climate Modeling

NAP has a new report out on a national strategy for advancing climate modeling. I've just done a quick skim so far. Some tidbits below that touch on V&V and uncertainty.

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