Hypersonics Basic and Applied Lectures

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?

Hamiltonian Monte Carlo with Stan

Stan is a library for doing Bayesian statistical inference. One of the really cool capabilities it has is the Hamiltonian Monte Carlo (HMC) method rather than the more common Markov Chain approaches. There are interfaces for using the library from Python, R or the command line:

Stan is based on a probabilistic programming language for specifying models in terms of probability distributions. Stan’s modeling language is is portable across all interfaces (PyStan, RStan, CmdStan).

I found this video from the documentation page a very understandable description of the Hamiltonian Monte Carlo approach used by Stan. It's neat to see how using a deterministic dynamics can improve on random walks. I'm reminded of Jaynes: "It appears to be a quite general principle that, whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought."

A One-Equation Local Correlation-Based Transition Model

This article is available for free download until 15 Oct 2015, h/t ANSYS blog.

Here's the Abstract:

A model for the prediction of laminar-turbulent transition processes was formulated. It is based on the LCTM (‘Local Correlation-based Transition Modelling’) concept, where experimental correlations are being integrated into standard convection-diffusion transport equations using local variables. The starting point for the model was the γ-Re θ model already widely used in aerodynamics and turbomachinery CFD applications. Some of the deficiencies of the γ-Re θ model, like the lack of Galilean invariance were removed. Furthermore, the Re θ equation was avoided and the correlations for transition onset prediction have been significantly simplified. The model has been calibrated against a wide range of Falkner-Skan flows and has been applied to a variety of test cases.

Keywords: Laminar-turbulent transition, Correlation, Local variables
Authors: Florian R. Menter, Pavel E. Smirnov , Tao Liu, Ravikanth Avancha

Transition location, and subsequent turbulence modeling remain the largest source of uncertainty for most engineering flows. Even for chemically reacting flows the source of uncertainty is often less the parameters and reactions for the chemistry, and more the uncertainty in the fluid state driven by shortcomings in turbulence and transition modeling.

Reliability Growth: Enhancing Defense System Reliability

This report (pdf) from the National academies on reliability growth is interesting. There's a lot of good stuff on design for reliability, physics of failure, highly accelerated life testing, accelerated life testing and reliability growth modeling. Especially useful is the discussion about the suitability of assumptions underlying some of the different reliability growth models.

The authors provide a thorough critique of MIL-HDBK-217, Reliability Prediction of Electronic Equipment, in Appendix D, which is probably worth the price of admission by itself. If you're concerned with product reliability you should read this report (lots of good pointers to the lit).

Guidelines for Planning and Evidence for Assessing a Well-Designed Experiment

This paper is full of great guidance for planning a campaign of experimentation, or assessing the sufficiency of a plan that already exists. The authors break up the effort into four phases:

1. Plan a Series of Experiments to Accelerate Discovery
2. 1. Design Alternatives to Span the Factor Space
   2. Decide on a Design Strategy to Control the Risk of Wrong Conclusions
3. Execute the Test
4. Analyze the Experimental Design

They give a handy checklist for each phase (reproduced below). The checklists are comprehensive (significantly more than my little list of questions) and I think they stand-alone, but the whole paper is well worth a read. Design of experiments is more than just math, as this paper stresses it is a strategy for discovery.

Starscraper Sounding Rocket Kickstarter

The Boston University Rocket Propulsion Group (BURPG) is developing a sounding rocket designed to top 150km and funding it partially through a kickstarter campaign. They plan on launching from Blackrock next year like Qu8k, but this is a more ambitious and complex effort.

The rocket will be controlled using fluid injection thrust vectoring. The thrust levels of their hybrid motor are comparable to Qu8k, but it is a significantly larger (30 vs 14ft long, 12 vs 8in diameter) and heavier (1100 vs 320 lbs) and aims higher (150km vs 120kft). It's hard to tell, but it also seems to be an order of magnitude or so more expensive.

The advantage the BURPG folks claim for their concept over traditional solid fuel sounding rockets is a gentler ride for payloads on the longer, smoother burning hybrid.

Gaussian Processes for Machine Learning

What a great resource for learning about Gaussian Processes: The Gaussian Processes Web Site.

• The Gaussian Processes for Machine Learning book.
  
• Software around the web, and to go with the book
  
• Data Sets
• Tutorials
  

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.