Artificial Intelligence in Fluid Mechanics
AI and fluid mechanics: for better planes and wind farms
Designing more efficient aircraft and wind farms, requires an ever deeper understanding of continuously more complex flows as we strive towards a carbon neutral society.
With the advent of new experimental techniques and high-fidelity flow simulations that provides big data on complex flows, AI tools can now be leveraged to provide this better understanding and prediction capability for those complex flows. In the AIFluids Lab, we focus on two major challenges of fluid mechanics: predicting and controlling complex, unstable and turbulent flows by using new AI techniques combined with past physical understanding of flows. We aim to combine human and machine insights to get to the essence of complex physical flows, be it in air, water or other media.
Using this combination of AI and physics-based approaches, our research will lead to models that can be used to design more efficient aircraft and wind farms. It will also allow us to train our AI algorithms so that they can autonomously control sensors and actuators to manage complex flows and improve aerodynamic performance.
The AIFluids Lab is part of the TU Delft AI Labs programme.
For MSc thesis projects at the AIFluids lab, see the “MSc AE Profile Aerodynamics” page on Brightpage or take contact with our lab by sending an email.
- Data-Driven URANS Closure with Sparse Regression (Nicolo Miori)
- Physical Reconstruction in Pool Fire with Deep Learning (Dirk de Boer)
- Generalizing Simulation Surrogates with Physical Information and Transfer Learning (Tadeusz Kaniewski – in collaboration with Neural Concept https://www.neuralconcept.com/ )
- Turbulent flows learning with deep learning (Rohan Kaushik)
- Physics-Informed Neural Networks for Airfoil Optimization (Dobbin Huang)
- Machine learning-based identification of precursors of extreme events in chaotic (Urszula Golyscka)