AI Adoption in Engineering, ML in Fluid Dynamics Resources & Open Source Unsteady Aerodynamics and Aeroacoustics Tool
🧠 “Strive for perfection in everything you do. Take the best that exists and make it better. When it does not exist, design it.” - Sir Henry Royce
💻 AI Adoption in Engineering - Roland Jones
💦 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...)
💻 Higher crash safety of composites through simulation with LS-DYNA
Crashworthiness is one of the most critical factors that every car manufacturer must take into account during the development phase of a vehicle. The safety of occupants and pedestrians depends largely on the vehicle's crashworthiness. An optimal vehicle body design for crash performance ensures that occupants and pedestrians are maximally protected in traffic accidents.
At the same time, regulations for vehicle crashworthiness are becoming increasingly stringent. To reliably and efficiently predict and demonstrate the crashworthiness of lightweight composite components, particular expertise is required to simulate highly dynamic processes and understand the composite materials used.
🎬 Video of the Week
💻 Engineering Tool of the Week – FLOWUnsteady
FLOWUnsteady is an open-source variable-fidelity framework for unsteady aerodynamics and aeroacoustics based on the reformulated vortex particle method (rVPM). This suite brings together various tools developed by the FLOW Lab at Brigham Young University: Vortex lattice method, strip theory, blade elements, 3D panel method, and rVPM. The suite also integrates an FW-H solver and a BPM code for tonal and broadband prediction of aeroacoustic noise. In the low end of fidelity, simulations are similar to a free-wake method, while in the high end simulations become meshless large eddy simulations.
📚Book of the Week : Data-Driven Science and Engineering - Machine Learning, Dynamical Systems, and Control
Data driven discovery is revolutionizing how we model, predict. control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data driven methods, machine learning, applied optimization.
Topics range from introductory to research level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics informed machine learning, significant new sections throughout. chapter exercises.
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Keep engineering your mind! 🧠
Jousef