A three-day ONLINE course on 'Large-Eddy Simulation, Detached-Eddy Simulations and Machine Learning using Python is given using Zoom.

Most engineers and many researchers have limited knowledge of what a LES/DES CFD code is doing. Furthermore they don't know how to use Machine Learning for model development.

The object of this course is to close that knowledge gap. During the course, the participants will learn and work with an in-house LES/DES code called pyCALC-LES, written by the lecturer. It is a finite volume code written in Python. It includes two zero-equation SGS models (Smagorinsky and WALE) and two two-equation DES models (the PANS model and the k-omega DES model).

The Machine Learning model that will be used are Neural Network (NN), binary search trees (KDTree) and Physical Informed Neural Network (PINN). They are all available as Python modules. NN will be used for improving wall functions and turbulence models, KDTree will be used for improving wall functions and PINN will be used for improving turbulence models.

All discretized equations in pyCALC-LES may be solved on the GPU if the computer has an Nvidia compatible graphics card. pyCALC-LES can also run fully on the GPU. On an eight million mesh, pyCALC-LES runs up to 70 times faster on the GPU than on the CPU.

The course includes lectures (12 hours) and workshops (12 hours) learning and working on Machine Learning and turbulence modeling.

The number of participants is limited to 16.

The course fee is 16 300 SEK (approx 1450 Euro).