Ansys Discovery, ML in Fluid Mechanics, Free Fluid Mechanics Series & ΦFlow
📚 "The ideal engineer is a composite, using knowledge and techniques from various disciplines to solve engineering problems.” – N.W. Dougherty
💻 Ansys Discovery: Thermal Cooling Simulation for Designers - James Shaw | Deep Dive Session 4
💦 CoSimIO Solver and Script Coupling
This repository is based on the official CoSimIO tutorial provided by Kratos Multiphysics. Most of the files differ from the original examples to meet our specific needs, particularly in coupling CoSimIO with another solver. This repository is designed to walk you through the basics, step by step, until you reach the point of coupling two different solvers: Kratos Multiphysics with OpenFOAM. This advanced coupling scenario is not covered in the original repository. For a more detailed exploration of CoSimIO and its capabilities, we encourage you to visit the official documentation and tutorials.
💦 Machine Learning in Fluid Dynamics
A curated list of awesome Machine Learning (Deep Learning) projects in Fluid Dynamics. Topics consist of Computational Fluid Dynamics (CFD), turbulence modeling, non-Newtonian fluids, Hemodynamics, PIV measurement, Geophysical fluid dynamics, Aeroelasticity, multiphase flow, etc.
♨️ Fluid Mechanics Series
This collection of videos was created about half a century ago to explain fluid mechanics in an accessible way for undergraduate engineering and physics students. I find that no other series of videos has explained the basics of fluid mechanics better than this one by the National Committee for Fluid Mechanics (those national committees gotta be good for something...)
💻 Engineering Tool of the Week – ΦFlow - Open-Source Simulation Toolkit
ΦFlow is an open-source simulation toolkit built for optimization and machine learning applications. It is written mostly in Python and can be used with NumPy, PyTorch, Jax or TensorFlow.
📚Book of the Week
An Introduction to Statistical Learning: with Applications in R
The 2nd edition of An Introduction for Statistical Learning (with R examples) is out! The book includes code examples with #R.
The second edition includes the following topics:
- Sparse methods for classification and regression
- Decision trees
- Boosting
- Support vector machines
- Clustering
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Keep engineering your mind! 🧠
Jousef