CFDSustain

In the realm of sustainable engineering, accurately simulating fluid dynamics presents numerous challenges due to their complex and nonlinear nature. Overcoming these challenges is crucial for driving scientific progress and enabling innovative engineering solutions. Computational Fluid Dynamics (CFD) has long been a cornerstone in modeling real-world fluid dynamics, offering proven and commercially available solutions. In fact, Lattice Boltzmann Methods (LBM) are particularly optimized for parallel processing, making them well-suited for tackling large-scale fluid dynamics simulations efficiently. More recently, Machine Learning (ML) techniques have gained traction in CFD for their ability to tackle complex data, speed up simulations, and reveal hidden patterns that may not be obvious through traditional approaches. The statistical foundation of ML allows for deep CFD data analysis, complexity reduction, optimization studies, and improved simulation models. In this project, the Lattice Boltzmann Research Group (LBRG, KIT, Germany) will join with the Brazilian groups, namely Structural Bioinformatics and Computational Biology Lab (SBCB/INF/CBIOT, UFRGS, Brazil), Mechanical Engineering Department (Engineering School, UFRGS, Brazil) and Institute of Hydraulic Research (IPH, UFRGS, Brazil)), to continue their existing close interdisciplinary collaboration in the fields of mathematics, computer science, and engineering.