Star-CCM+ Heatsink Tutorial, ML for Aerodynamics, AI for CAD
📚 Free education is abundant, all over the Internet. It's the desire to learn that's scarce.
🔥 Heatsink - Conjugate Heat Transfer | Simcenter STAR-CCM+ Deep Dive #2
💦 Machine Learning for Aerodynamics - Deep Learning & Neural Networks applied to CFD simulations
senseFly is a Swiss drone company that wanted to improve the flight time of their fixed-wing drones. senseFly, Neural Concept, EPFL (the technical University of Lausanne - École polytechnique fédérale de Lausanne) and AirShaper teamed up to apply Deep Learning to drone design to improve the aerodynamics, as improvements to the lift/drag ratio directly extend the range / increase flight time.
🧠 Autoencoders & Physics Informed Machine Learning
🧠 AI Helps Predict and Sketch Computer-Aided Design Models
Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design. Distinguished by relational geometry, parametric CAD models begin as two-dimensional sketches consisting of geometric primitives (e.g., line segments, arcs) and explicit constraints between them (e.g., coincidence, perpendicularity) that form the basis for three-dimensional construction operations. Training machine learning models to reason about and synthesize parametric CAD designs has the potential to reduce design time and enable new design workflows.
💻 Engineering Tool of the Week – CALCULIX
CalculiX is a package designed to solve field problems. The method used is the finite element method.
With CalculiX Finite Element Models can be built, calculated and post-processed. The pre- and post-processor is an interactive 3D-tool using the openGL API. The solver is able to do linear and non-linear calculations. Static, dynamic and thermal solutions are available.
📚Book of the Week
An Analysis of the Finite Element Method
A complete classic by Gilbert Strang and George Fix, first published in 1973. The original book demonstrates the solid mathematical foundation of the finite element idea, and the reasons for its success. The second part is a new textbook by Strang. It provides examples, codes, and exercises to connect the theory of the Finite Element Method directly to the applications.
The reader will learn how to assemble the stiffness matrix K and solve the finite element equations KU=F. Discontinuous Galerkin methods with a numerical flux function are now included. Strang's approach is direct and focuses on learning finite elements by using them.
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Keep engineering your mind! 🧠
Jousef