./episode_161.sh
Thank you for 2023! ❤️
🎙️ Computational Engineering - Josefine Lissner | Podcast #114
Josefine Lissner is an early pioneer in the field of Computational Engineering. Some of her work has been hailed as a historic milestone in physical design and 3D printing. Josefine uses computer code to build the world’s most astonishing and complex physical objects.
She is the founder of LEAP 71, a company working on designing and producing the next generation of computer-generated machines. Before starting her current venture in 2023, she was the Strategic Engineering Lead at Hyperganic, helping push and build the company’s technology and credibility through a number of industry-first projects.
🚀 Joukowsky Airfoil
In applied mathematics, the Joukowsky transform (sometimes transliterated Joukovsky, Joukowski or Zhukovsky) is a conformal map historically used to understand some principles of airfoil design. It is named after Nikolai Zhukovsky, who published it in 1910.
🧠 Verification and Validation for AI
To ensure the accuracy, reliability, and trustworthiness of AI-enabled systems in safety-critical industries, there has been significant progress in verifying AI through whitepapers, standards, and planning across sectors.
In the context of AI certification, V&V techniques will play a vital role in demonstrating that the AI model meets the necessary standards for safety and reliability. By applying V&V techniques, organisations can systematically verify the behaviour of the AI model, identify any potential errors or biases, and validate its performance against predefined criteria. V&V techniques for AI may include various approaches, such as testing the AI model against representative datasets, conducting simulations or experiments to assess its performance, analysing the model's decision-making process, and ensuring that it operates within acceptable bounds.
📊 Vinuesa Lab - DATABASES
Vinuesa Lab is sharing all their databases! This includes Wings, Ducts, Pressure Gradient TBLs as well as the Repositories of their Deep Learning Codes.
💡 Cable Routing Algorithms | Wire Harness Engineering - Rahman Akkoyun | Synera Podcast #7
💡 Machine Learning Applied to CFD by Andre Weiner
This repository contains examples of how to use machine learning (ML) algorithms in the field of computational fluid dynamics (CFD). ML algorithms may be applied in different steps during a CFD-based study:
pre-processing, e.g., for geometry or mesh generation
run-time, e.g., as a dynamic boundary condition or as a subgrid-scale model
post-processing, e.g., to create substitute models or to analyze results
💻 Engineering Tool of the Week - Flexcompute Flow360
A Scalable CFD Solver for Better Product Design
Flow360 Solver is a revolutionary CFD solver based on a breakthrough computing architecture that results in the scalability of accurate aerodynamic simulations at unprecedented speeds.
📚Book of the Week
An Introduction to Computational Fluid Dynamics
This book is a guide to numerical methods for solving fluid dynamics problems. The most widely used discretization and solution methods, which are also found in most commercial CFD-programs, are described in detail. Some advanced topics, like moving grids, simulation of turbulence, computation of free-surface flows, multigrid methods and parallel computing, are also covered. Since CFD is a very broad field, we provide fundamental methods and ideas, with some illustrative examples, upon which more advanced techniques are built.
🙃 Meme of the Week
❤️ Support the Blog & Newsletter
Let’s connect on Twitter or Instagram or LinkedIn!
For any business-related issues or collaborations, feel free to email me at support@jousefmurad.com!
Keep engineering your mind! 🧠
Jousef