NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

This is really cool. The capture for the specular reflections is great. I'm excited that something like this could be really useful for better photogrammetry. For instance, see this old GBU I did a long time ago. The reflections off the shiny metal cause artifacts in the point cloud reconstruction.

There's a paper, a project summary page with more views, and a github project page for the code as well.

3D Shape Segmentation With Projective Convolutional Networks

This is an interesting summary of an approach for shape segmentation. I think it's pretty cool how often VGG-16 gets used for transfer learning with good results. It's amazing that these models can represent enough knowledge to generate 3-D surfaces from single images. (I also like how many folks use airplanes as examples : - )



There's a website for the ShapeNet data set that they used as a benchmark in the video, and this paper describes the initial methods folks developed during the challenge right after the data set was released. That's a pretty neat approach. It reminds me a bit of the AIAA drag prediction workshops.

Engineering Sketch Pad

I haven't heard of Engineering Sketch Pad (source code as part of OpenMDAO, and here) before, but this is yet another NASA sponsored open source tool that could be useful to you for aircraft conceptual design. I read about it in a post on Another Fine Mesh about some interesting research the folks at Pointwise are doing. It reminds me of, but is different from, Open Vehicle Sketch Pad.

There's a seminar on the software given by one of the developers up on a NASA site: The Engineering Sketch Pad (ESP): Supporting Design Through Analysis. (yea, DARPA!)

It has some neat features that make it useful to support high-fidelity analysis. It creates watertight geometry, it can carry attributes with the geometry that could guide mesh resolution, it does "conservative" data transfer for discipline coupling (match a solver's numerical scheme), and most of its parts are differentiable which is useful for optimization.

I added this to my list of Open Source Aeronautical Engineering Tools.

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.

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.

Installing ToPy in Fedora 26

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

Deep Learning to Accelerate Computational Fluid Dynamics

Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks

I posted about a surprising application of deep learning to accelerate topology optimization. The thing I like about that approach is it's a strategy that could be applied to accelerate many different solvers that we use to simulate all sorts of continuum mechanics based on partial differential equations (i.e. computational fluid dynamics, structural mechanics, electrodynamics, etc.). With a bit of help from Google I found a neat paper and project on github doing exactly that for a Lattice-Boltzmann fluid solver.

Exciting 3D Print Service Developments

It has never been easier to go from a design in your head to parts in your hand. The barriers to entry are low on the front end. There are all sorts of free and open source drawing, mesh editing, modeling or CAD applications. On the fabrication end of things, services like shapeways, and imaterialise continue to improve their delivery times, material options and prices.

One of the recent developments that deserves some attention is in metal 3D printing. imaterialise has offered parts in DMLS titanium for a while, but they've been pretty pricey. They have now significantly reduced the prices on Ti parts, and are now offering a trial with aluminum. Not to be left out, Shapeways has graduated SLM aluminum from its pilot program.

It's great to see such thriving competition in this space. I'm working on some models specifically for this metal powder-bed fusion technology. What will you print?

Fully Scripted Open Source Topology Optimization

Helical Extruder Gear for Printrbot with Optimized Topology

I've used a couple different methods for stringing together open source tools to do topology optimization, but they have all required some interactive user input. Here are some previous posts demonstrating those manual methods:

• Open Source Topology Optimization for 3D Printing
• Octet Truss for Topology Optimization
• Prusa Mendel RepRap Frame Vertex Topology Optimization Using ToPy
• 3-in Tack Strip Bracket TopOpt

Those approaches are fine if you've got time to fiddle with interactive software, but I wanted to do some parametric studies, so I need an automated approach that would be scalable to lots and lots of optimizations.

119 Open Source Aeronautical Engineering Tools

* permanent page with updates: Open Source Aeronautical Engineering Tools*

I posted a list of 33 open source aeronautical engineering tools on LinkedIn a couple days ago. One of the comments was a question about how open they all really were so I added a column to the list for the license and any non-free dependencies (i.e. Matlab). I went ahead and made an entry for each of the pieces of software from Ralph Carmichael's PDAS collection, which added 84 public domain pieces of software. In addition, there are 23 with various flavors of GNU, 4 BSD-style, and 3 NASA open source agreement (NOSA) codes. See the whole list below the fold. Please suggest adds/changes/deletes in the comments.

HiFiLES v0.1 Release

The folks at the Stanford Aerospace Computing Lab have recently released version 0.1 of HiFiLES. "HiFiLES is a high-order Flux Reconstruction solver for the Euler and Navier Stokes equations, capable of simulating high Reynolds number turbulent flows and transonic/supersonic regimes on unstructured grids."

From the release notes:

High-order numerical methods for flow simulations capture complex phenomena like vortices and separation regions using fewer degrees of freedom than their low-order counterparts. The High Fidelity (HiFi) provided by the schemes, combined with turbulence models for small scales and wall interactions, gives rise to a powerful Large Eddy Simulation (LES) software package. HiFiLES is an open-source, high-order, compressible flow solver for unstructured grids built from the ground up to take full advantage of parallel computing architectures. It is specially well-suited for Graphical Processing Unit (GPU) architectures. HiFiLES is written in C++. The code uses the MPI protocol to run on multiple processors, and CUDA to harness GPU performance.

The main reference for the code right now is this V&V paper.[1] The code uses an Energy Stable Flux Reconstruction (ESFR) scheme. Here are a couple papers on that approach.[2, 3].

References

[1]   López-Morales, M. R., Bull, J., Crabill, J., Economon, T. D., Manosalvas, D., Romero, J., Sheshadri, A., Watkins II, J. E., Williams, D., Palacios, F., et al., “Verification and Validation of HiFiLES: a High-Order LES unstructured solver on multi-GPU platforms,” .

[2]   Vincent, P. E., Castonguay, P., and Jameson, A., “A new class of high-order energy stable flux reconstruction schemes,” Journal of Scientific Computing, Vol. 47, No. 1, 2011, pp. 50–72.

[3]   Castonguay, P., Vincent, P. E., and Jameson, A., “A new class of high-order energy stable flux reconstruction schemes for triangular elements,” Journal of Scientific Computing, Vol. 51, No. 1, 2012, pp. 224–256.

SU2 Community Verification Studies

I think there is quite a bit of excitement and community involvement building around the SU2 code. Other than all of the updates and improvements in the recently released version 3 and SU2_EDU release, I am excited to see the wider community start to do some serious verification studies. The advecting vortex case linked in that discussion thread would be a good one to add to the Test Case collection.

The core SU2 devs have a recent AIAA paper on verification/validation cases that they have successfully run with SU2 and compared favorably to other codes. One thing that is conspicuously absent is grid convergence studies to verify order of accuracy. This is an ideal place for the community to contribute because you don't have to have hugely in-depth knowledge of the source code base to run a grid convergence study or contribute a tutorial or test case (though you do have to be a fairly competent user). Much to their credit, the SU2 team is soliciting just this kind of contribution (my emphasis):

Expanded tutorials: we would like additional tutorials that complement the existing set found in the web-based documentation. The tutorials can either detail and explain the functionality of SU2 (shape optimization, parallel computing, mesh deformation, etc.) or demonstrate general CFD knowledge (highlighting good meshes vs. bad meshes, the importance of CFL number, etc.). Tutorials are intended to be tools for teaching and learning, and they should follow the same style as the existing tutorials. They must provide any mesh or config files that are necessary for their completion. New or unique verification and validation cases would be of particular interest here.

Exciting times in open source CFD!

SU2 v3 Released

The folks at Stanford Aerospace Design Lab have released a new major version of Stanford University Unstructured (SU2). Here's the announcement:

Dear Colleague,

Since its introduction in January 2012, SU2, The Open-Source CFD Code, has been downloaded thousands of times by users and developers in academia, government, and industry, including many leading companies and universities. As an open-source project, the growth of active user and developer communities is a crucial goal for SU2. Given the incredibly positive response, we are pleased to announce a new version of the code with major improvements and a entirely new package for educational purposes.

This release marks the third major version of the SU2 open-source code (su2.stanford.edu). SU2 is a collection of C++ software tools for performing Partial Differential Equation (PDE) analysis and for solving PDE-constrained optimization problems, with special emphasis on Computational Fluid Dynamics (CFD) and aerodynamic shape design.

We'd like to ask you to please distribute this announcement with the attached flyer to any colleagues and students in your department that might be interested.

Version 3.0 has a number of major additional capabilities:

• Adjoint-based RANS shape optimization.
• New unsteady analysis and design optimization capability.
• Upgrades to the underlying parallelization and file I/O.
• Significant improvements to the accuracy, performance, and robustness of the software suite.

Alongside Version 3.0 of SU2, we are introducing SU2 Educational (SU2_EDU): a new, educational version of the Euler/Navier-Stokes/RANS solver from the SU2 suite. The simplified structure of SU2_EDU makes it suitable for students and beginners in CFD. By focusing on a handful of key numerical methods and capabilities, SU2_EDU is ideal for use in CFD courses, for independent studies, or just to learn about a new field!

SU2_EDU is also geared toward anyone interested in high-fidelity airfoil analysis. The initial version of SU2_EDU is an intuitive, easy to use tool for computing the performance of airfoils in inviscid, laminar, or turbulent flow including non-linear effects in the transonic regime, that only requires the airfoil coordinates.

Finally, we would like to thank the open-source community for their interest, help, and support.

The SU2 team

One of the most interesting parts to me is the new SU2_EDU version. I've downloaded the code, but haven't had a chance to browse it or run any examples yet. I think this is a neat idea that will hopefully lower the barriers to entry that George pointed out previously.

Open Source Aeronautical Engineering Tools

* permanent page with updates: Open Source Aeronautical Engineering Tools*

I was reading through some of the papers and cites related to OpenMDAO and stumbled across a couple interesting papers[1, 2] on open source software in aeronautics.

Abstract: Open source software has become an alternative to commercial software for industrial users. Industrial users adopting to OSS and the underlying concepts need to consider changing their software development practices and organization in order to benefit from the OSS model. These changes may involve both technical and non-technical aspects. Openness and collaboration with a community are two non-technical concepts that may require such changes, while evaluating OSS products or evaluating different strategies for integrating OSS products are two technical aspects that may require adoption.

The objective of this paper is to create an awareness for the adoption of OSS in an industrial context. OSS can be used in different ways. Four categories of using OSS are presented by discussing motivation, implication and experience for each category. The main conclusion from this work is that organizations should have a realistic expectation of both the designated benefits and extension of organizational changes necessary to adopt to OSS. This conclusion is based on observations from industrial organizations using OSS, including OSS in the aeronautics industry, as well as from observations reported in the literature on open source.

Fedora OpenMDAO Install

OpenMDAO is "an open-source MDAO framework written in Python." It is an easy install (though not packaged for Fedora yet as far as I can tell). The only odd thing in the system requirements is the Chrome web browser for the GUI. They say things should work with Firefox (not Internet Explorer), but who cares? GUIs are for quiche-eaters anyway ; - ) The other requirements (Python, Scipy, Numpy, Matplotlib) are commonly packaged for a wide range of operating systems.

OpenMDAO features,

• Library of Built-in Solvers and Optimizers
  
• Tools for Meta-Modeling
  
• Data Recording Capabilities
  
• Support for Analytic Derivatives
  
• Support for High-Performance Compute Clusters and Distributed Computing
  
• Extensible Plugin Library
  

SU2 and OpenMDAO Joint Workshop

Here's an announcement for an exciting workshop combining the open source computational fluid dynamics code SU2 with the open source optimization framework OpenMDAO.

Dear Colleagues,

This is a friendly reminder about the OpenMDAO and SU2 joint workshop on Sept.30 - Oct.1 in Durand 450 (Stanford University). So far, we have had great interest for both in-person, and virtual attendance to the event and we are looking forward to a great workshop. There is still room for more participants and we encourage you to join us for as much workshop as you can. As a reminder, no experience with the software is necessary.

If you plan on attending in-person or virtually, all participants should register at the SU2 homepage:
http://su2.stanford.edu

For virtual attendance, you must additionally register for the online webinar at the following locations:
Day 1: https://attendee.gotowebinar.com/register/7114168239445654785
Day 2: https://attendee.gotowebinar.com/register/208563415407826177

OpenMDAO is an open-source framework for Multidisciplinary Design, Analysis and Optimization (MDAO). Written in Python, it functions to integrate analysis tools into a single design environment allowing for the investigation of engineering designs and optimization of designs subject to user requirements.

SU2 is an open-source numerical PDE solver that can be used to solve the equations of fluid motion. SU2 has been used to simulate a range of fluid including wind turbines, supersonic business jets, and hypersonic re-entry vehicles.

These powerful codes are suitable for general purpose applications. They have been downloaded more than ten-thousand times and have a continually growing footprint in a growing field of research. Because these codes are freely available and can be modified to suit specific needs, they can be used as part of countless research problems.

If you’re interested in multi-physics simulations applied to design, this is the workshop for you!

Each day will have a morning session to introduce the software packages and an afternoon session to give hands-on experience working with the codes. These sessions will focus on linking OpenMDAO and SU2 for specific multi-disciplinary problems. The best utilization of the two packages will be awarded an iPad!. The deadline for the competition (only on-site workshop attendees) will be on Thursday afternoon (October 3rd) and the winner will be announced on October 4th in the SU2 website.

The agenda for the workshop is attached. Food will be provided both days.

We hope you are able to join us for this exciting and engaging event!

Cheers,
The SU2 and OpenMDAO development teams

I wish I could spare the two days to participate virtually; looks like a really good event.

Update: Workshop Materials

• OpenMDAO Introduction, Installation, and Quick Start
  
• SU2 Introduction and Installation
  
• SU2 Advanced Analysis Topics (presentation)
  
• SU2 Advanced Analysis Topics (mesh and configuration example files)
  
• Shape optimization using SU2 (presentation)
  
• Shape optimization using SU2 (ONERA M6 files)
  

Update: videos and more from the workshop.

SU2 Now on Git: Build and Install Latest on Fedora

As I wrote previously the Stanford University Unstructured (SU2) code is now available on git hub. Here's the steps to get the latest code, build and install it.

• Use the git client to clone the repo:
  
git clone https://github.com/su2code/SU2

  
• Follow the 'from source' install instructions for your system. It is should look something like:
  
./configure
make
make install

  This will give you (after a little help from the kindly devs) a plain-Jane version of SU2 for serial computations, without CGNS or Metis (graph partitioning) support.
  

Happy number crunching!

SU2 Now on Git

Good news for Open Source Computational Fluid Dynamics from the SU2 team:

The Stanford University Unstructured (SU2) development team is proud to announce that the open-source SU2 suite has moved to GitHub!

This change makes it much easier for individuals and groups to use and modify the code for their own purposes. It also means that now SU2 is more open than ever. Moving to GitHub is an important and exciting step for SU2, and we are looking forward new contributions from the community in the true spirit of open-source software.

Users who wish to work with the code will now find that all of the capabilities they require to track modifications are already in place. Copying the code and submitting revisions is easier than ever with GitHub's "fork" and "pull request" features.

In the year and a half since our initial release, SU2 has been downloaded over 5,700 times, and the main SU2 website has received over 45,000 visits from around the world. Our forum hosted by CFD online sees an abundance of activity from many of our users.

SU2 is under active development in the Aerospace Design Lab (ADL) in the Department of Aeronautics and Astronautics at Stanford University and is released under an open-source license.

More information can be found at:

• Webpage
  
• Wikipedia
  
• GitHub
  
• CFD-online
  
• Twitter
  
• Facebook
  

Thank you for your interest and support! The SU2 team

I think this is a really good thing (see previous discussion about SU2 being more open). You may say, why do we need another open source CFD code, don't we already have OpenFOAM? Yes we do, but OpenFOAM is focused on industrial CFD applications that tend to have incompressible formulations and methods. SU2 is a compressible flow finite-volume code (with lots of neat design optimization capabilities too). If you come from the compressible flow world, then you'll find the schemes and methods (upwind/flux splitting, TVD limiters, etc) in SU2 are right in your comfort zone. The beauty of open source is that we can have all these complimentary options to fill different niches.