OpenLSTO plus InverseCSG

I was recently excited to learn about the OpenLSTO and InverseCSG projects, and that got me thinking: can we automate topology optimization interpretation for a 3D part with open source tools?

Topology optimization results are usually a discrete set of density voxels (as from ToPy) or a triangulated mesh (as from OpenLSTO). There is an interpretation step often required to take this result and turn it into something that you can fabricate or incorporate into further design activities. In the case of OpenLSTO you are getting what your manufacturing chain needs (an stl file) if you are 3D printing.

Interpreting the results of a topology optimization can be a time consuming manual process for a designer. While the steps to interpret a 2D topology optimization result can already be automated with a complete open source tool-chain, 3D is harder. I demonstrated in this post how the 2D bitmap output of ToPy can be traced to generate dxf files that you can import and manipulate in a CAD program. On the other hand, here’s an example I did that demonstrates the more manual process for a 3D part.

InverseCSG recovers CAD from model

The MIT Computational Fabrication Group has a page up with the abstract and links to the paper and video. The InverseCSG folks took a program synthesis approach to enable them to generate CAD boolean operation "programs" from the 3D model "specification."

OpenLSTO: New Open Source Topology Optimization Code

Optimized 3D Cantilever from OpenLSTO Tutorial

I was excited to see this short mention of a new open source topology optimization code in the Aerospace America Year in Review.

In July, University of California, San Diego published open-source level set topology optimization software. This new software routinely runs 10 million element models by adapting and tailoring the level set method, making design for additive manufacturing immediately accessible.
New computing tools, international collaboration spell design progress

The software site for UC San Diego's Multiscale, Multiphysics optimization lab has the basic license information, and links to documentation and downloads. The source code is up on github as well.

Monoprice Mini Delta 3D Printer

I recently bought my first personal 3D printer. I have been involved in DIY and hobbyist 3D printing for many years through the Dayton Diode hackerspace I co-founded. This Monoprice Mini Delta is the first printer of my very own. The price point is amazing (less than $160!), and things just work right out of the box. What a hugely different experience than building that first printrbot kit (RIP printrbot). The Printrbot story is actually a piece of the Innovator's Dilemma playing out in this market niche. Printrbot disrupted a higher-cost competitor (Makerbot) who retreated up-market towards higher-end machines, and was then in-turn disrupted by foreign suppliers like Monoprice. This caused Printrbot to reatreat unsuccessfully up-market themselves towards $1000 machines. Who will disrupt Monoprice? I can't wait for my voice controlled, artificially intelligent, $20 printer... In the meantime, this post is about my experience with this little desktop FDM machine you can buy today.

Some interesting aerodynamics & control details on the re-design required for the Falcon Heavy at 15:20 or so. Great launch!

Topology Optimization with ToPy: Pure Bending

From The Design of Michell Optimal Structures

Here is an interesting paper from 1962 on the design of optimal structures: The Design of Michell Optimal Structures. One of the examples is for pure bending as shown in the figure above. I thought this would be a neat load-case to try in ToPy.

Topology Optimization for Coupled Thermo-Fluidic Problems

Interesting video of a talk by Ole Sigmund on optimizing topology for fluid mixing or heat transfer.

Installing ToPy in Fedora 26

This post summarizes the steps to install ToPy in Fedora 26.

Machine Learning for CFD Turbulence Closures

I wrote a couple previous posts on some interesting work using deep learning to accelerate topology optimization, and a couple neural network methods for accelerating computational fluid dynamics (with source). This post is about a use of machine learning in computational fluid dynamics (CFD) with a slightly different goal: to improve the quality of solutions. Rather than a focus on getting to solutions more quickly, this post covers work focused on getting better solutions. A better solution is one that has more predictive capability. There is usually a trade-off between predictive capability, and how long it takes to get a solution. The most well-known area for improvement in predictive capability of state-of-the-practice, industrial CFD is in our turbulence and transition modeling. There are a proliferation of approaches to tackling that problem, but the overall strategy that seems to be paying off is for CFD'ers to follow the enormous investment being made by the large tech companies in techniques, open source libraries, and services for machine learning. How can those free / low-cost tools and techniques be applied to our problems?

The authors of Machine Learning Models of Errors in Large Eddy Simulation Predictions of Surface Pressure Fluctuations used machine learning techniques to model the error in their LES solutions. See an illustration of the instantaneous density gradient magnitude of the developing boundary layer from that paper shown to the right. Here's the abstract,

We investigate a novel application of deep neural networks to modeling of errors in prediction of surface pressure fluctuations beneath a compressible, turbulent flow. In this context, the truth solution is given by Direct Numerical Simulation (DNS) data, while the predictive model is a wall-modeled Large Eddy Simulation (LES
). The neural network provides a means to map relevant statistical flow-features within the LES solution to errors in prediction of wall pressure spectra. We simulate a number of flat plate turbulent boundary layers using both DNS and wall-modeled LES to build up a database with which to train the neural network. We then apply machine learning techniques to develop an optimized neural network model for the error in terms of relevant flow features