Advanced Thermal Modeling, Machine Learning Methods for Physics, MegaFlow2D
📚 “The whole purpose of education is to turn mirrors into windows” – Sydney J. Harris
🔋🔋🔋 Advanced Thermal Modeling for Battery Systems - Xiangchun Zhang | Deep Dive
🌎 Deep Dive Repo: https://www.mathworks.com/matlabcentral/fileexchange/179904-battery-modeling-examples
🎙Podcast with Xiangchun
Xiangchun Zhang has experience working in the field of battery research and development. They began their career at Sakti3, Inc. as a Design Engineer from 2009 to 2015. During this time, they focused on developing MATLAB codes for battery simulation and design optimization, as well as implementing parameter estimation and improved Kalman filters for battery state of charge estimation.
💻 On Machine Learning Methods for Physics
Machine learning methods will fundamentally transform the landscape of physical simulation.
Physical modeling allows precise and simple descriptions of nature, yet large-scale simulations of these models can be computationally expensive. Traditional numerical methods have dominated these efforts for most of the last century. However, new paths to modeling macroscopic physics have opened with recent advancements in hardware, machine learning methods, and data collection strategies. This blog reviews some of the most promising new approaches and discusses the authors preferences in this broader context.
💦 MegaFlow2D: A Parametric Dataset for Machine Learning Super-resolution in Computational Fluid Dynamics Simulations
This paper introduces MegaFlow2D, a dataset of over 2 million snapshots of parameterized 2D fluid dynamics simulations of 3000 different external flow and internal flow configurations. It's worth noting that, simulation results on both low and high mesh resolutions are provided to facilitate the training of machine learning (ML) models for super-resolution purposes. This is the first large-scale multi-fidelity fluid dynamics dataset ever provided. We build the entire data generation and simulation workflow on open-source and efficient interfaces that can be utilized for a variety of data samples according to the user's specific needs. Finally, we provide a use case to demonstrate the potential value of the MegaFlow2D dataset in applications related to error correction.
💻 Sizing inflation layers using a y+ estimation tool - Aidan Wimshurst
💦 Advanced Heat Sink Design with ToffeeX - Webinar
With the ongoing trend towards miniaturizing electronic devices, the issue of components overheating has become increasingly critical. To address this challenge, advancements in heat dissipation performance through improved heatsink designs are more relevant than ever.
🎬 Video of the Week
💻 Engineering Tool of the Week – FeenoX
FeenoX can be seen either as
a syntactically-sweetened way of asking the computer to solve engineering-related mathematical problems, and/or
a finite-element(ish) tool with a particular design basis.
📚Book of the Week
Modern Fortran by Milan Curcic 📚
Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you'll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light.
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Keep engineering your mind! 🧠
Jousef