ARCHER UK Video Tutorials

ARCHER UK National Supercomputing Facility have introduced some video tutorials.

Many may give insight into the practical usage for scientific computing stuff.

Some topics that are covered in the tutorials are;

Using Make, PetSc, Version Control, PBS scripting, OpenMP + MPI coding, etc

ARCHER Weblink
YouTube Link

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Cylinder Meshing in ICEM tutorials

 https://www.youtube.com/watch?v=JfaSUcectHI
 https://www.youtube.com/watch?v=GRswo4agntM

--By Simulate Smartly

Please do comment, suggest, correct and add to our posts

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Its Python time !!!

Start here :
https://developers.google.com/edu/python/
http://www.learnpython.org/
http://www.tutorialspoint.com/python/
http://sthurlow.com/python/

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Automatic Differentiation - for CFD

Automatic Differentiation - for CFD

Automatic Differentiation (AD) deals with differentiating a single / multi variable function w.r.t a set of independent parameters. Simply, its a package which differentiates a function, and function is given by a piece of code. 

This is particularly helpful in CFD in the case of implicit algorithms, where the Flux Jacobian is required. Of course, there are other ways of computing the Jacobian, like Numerical Differentiation, but it is computationally very expensive. 

We think TAPENADE package (and even MATLAB - Symbolic toolbox) is a good place to start in AD. The package takes the function, its dependent and independent variables and gives the differential (a slope - in case of single variable and Jacobian - in case of multi-variable function). 

As an example, the process of deriving the analytical flux Jacobians for the Roe flux formulation is tedious and may result in incorrect evaluation of the Jacobians. An efficient alternative to avoid both hand-derived Jacobians and computationally expensive finite-difference evaluation or Numerical differentiation of the Jacobians, is to generate the flux Jacobians using automatic-differentiation. Auto-differentiation uses the chain-rule to calculate the derivative of a function, in this case the flux formulation. In this method the flux formulation is passed as a differentiable function to the auto-differentiation package (TAPENADE). This generates the analytical expressions of the flux Jacobians.

Alternative info, obviously on AD on Wiki
TAPENADE Documentation

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CFD Lectures (Comprehensive)

CFD Lectures (Comprehensive)

Lecture 1 - Introduction and first illustrations of CFD

Lecture 2 - Introduction to turbulence - Linear isotropic closures

Lecture 3 - Turbulence models - Towards non-linear anisotropic models

Lecture 4 - Hybrid LES turbulence closures

This last lecture concerning turbulence closures will be devoted to a brief description of LES and Hybrid LES turbulence closures. Illustrations will be provided on automotive flows.

Lecture 5 - Unstructured finite volume discretisation

This lecture is the first of a series of three lectures which will be devoted to the methodology used to build a generalized unstructured finite volume discretization.

Lecture 6 - Unstructured finite volume discretisation

Second part of the course on generalized unstructured finite volume discretization methods.

Lecture 7 - Pressure equation

To take into account the incompressibility of the flow and find the pressure, it is necessary to build a pressure equation. This lecture will be devoted to this topic and to the specific mass flux reconstruction schemes.

Lecture 8 - Fully coupled formulation and free surface capturing strategies

This lecture will present alternate strategies to solve the coupling between mass and momentum conservation equations. Free-surface capturing methodologies and specific compressive discretization schemes will be also presented

Lecture 9 - Various illustrations of up-to-date computations

This presentation shows some recent CFD applications devoted to viscous ship hydrodynamics

Lecture 10 - Verifcation and Validation - Part 1

Lecture 11 - Verifcation and Validation - Part 2

This lecture will describe the current recommended procedures used to verify and validate a numerical result obtained with the help of CFD. A new 2D procedure for estimating the local discretization error will be also presented.

Link to Source to find animations as well !

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1d burgers equation using DG and WENO

http://lsec.cc.ac.cn/lcfd/DEWENO/dg_code.htm


The above link is a Fortran77 code on the solution of burgers equation which uses DG and WENO for polynomial truncation of non linear terms to compute 1D conservation laws.....

Have a nice time!! enjoy coding!!! :)

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