Nalu VS CFDPython

Hi /r/RPI - Greg Vernon, Aero-Mech '12 here. I wanted to let you know that our company, Coreform, co-develops the Cubit mesh-generation software with Sandia National Labs and we sell Cubit to non-government entities as "Coreform Cubit." Prior to joining Coreform I spent ~8 years as a finite element analyst contractor for the Dept. of Energy and I used Sandia's Cubit on a daily basis. Cubit is, in fact, used heavily within the DOE and DOD as it serves as the native mesh generator for many government FEA and CFD codes. For example, it is the preferred pre-processor for Idaho National Labs' open-source MOOSE finite element code, Sandia's open-source GOMA FEM code, Sandia's NALU CFD code, and many more internal codes.

Is 12 steps to Navier Stokes a good start? I have done all the modules, wrote all the code by myself (except for the plotting part which I had literally no experience in) and I am trying to solve some random problems in the J P Holman heat transfer book. Then I am thinking of going through the Application part of Anderson CFD.

Thank you for posting the most in-depth reply I have ever received on Reddit. This is some very good advice. I'm constantly trying to get a better understanding of solving differential equations; consequently, I'm currently solving the Gray-Scott diffusion model. My example above is based on what I've done for the Gray-Scott model. Since you seem to have experience in this area, do you have any books that you recommend for learning more about writing code to solve differential equations using finite differences, finite element, or finite volume methods? I have some books that talk about the theory but haven't found anything that gives good code examples related to the math. CFD Python has been a great resource but it doesn't provide the depth that a book would give.

updated link

CFDPython/14_Step_11.ipynb at master · barbagroup/CFDPython (github.com)

This course is freely available and gives a very good hands on introduction to incompressible CFD solvers: https://github.com/barbagroup/CFDPython

For CFD, I just starred looking into it recently and trying to avoid paying premium for the software and came across this recently. https://github.com/barbagroup/CFDPython

There are many different ways to do what you'd like. The easiest starting point would probably be this tutorial: https://github.com/barbagroup/CFDPython

But that won't handle turbulence. The real "turbulence problem" is that computing actual turbulent flows requires enormous computational resources. So instead of solving the Navier-Stokes equations, related equations with lower computational cost are solved. Because of how these equations are developed, they require modeling of "unclosed" terms, and this is a likely source of inaccuracy.

If you want something relatively simple, you could take the RANS approach and use the Spalart-Allmaras model:

https://www.cfd-online.com/Wiki/Introduction_to_turbulence/R...

https://www.cfd-online.com/Wiki/Spalart-Allmaras_model

12 Steps to Navier Stokes by Lorena Barba link

I sniff an air of condescension; what’s your goals therein? Are you posturing your ‘maths’ knowledge?

I thought I was explicit in my criticism of “notation only” explanations, but perhaps a positive example would be more explicit.

https://github.com/barbagroup/CFDPython

This repo explains computational fluid dynamics (an example of a complex system!) from “what is a python function” to “2d Navier stokes”.

It shows the work of how to discretize ‘latex beautified’ notation, shows the relationship between the computations and the notation, and even explains when their LaTex strays from “conventional use of notation” and why.

The authors even throw in traditional handwritten board lecture videos if that helps you learn better.

complexityexplained reads like it’s written by the Spider-Man points at Spider-Man meme.