Deep Learning Based Speed Up of Fluid Dynamics Solvers

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2022-9-8

Author

Acar, Deniz Alper

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In this thesis, two distinct deep learning-based methods for the speed-up of fluid dynamics solvers are proposed. The first method called Parametric Encoded Physics informed neural network (PEPINN), is utilized to solve transient fluid dynamics problems. PEPINN is an alternative to the Physics informed neural networks (PINN) and is based on the parametric encoding of the problem domain. In PEPINN the automatic differentiation for calculation of the problem residual is replaced with finite difference kernels which improve PEPINN's time and memory complexity. This model can achieve up to 40 times speed up in wall time for the solution of the Taylor-Green Vortex problem compared to the best alternative vanilla PINN model with no loss in solutions mean squared error. It is also shown that PEPINN can be trained on up to 183 times larger data compared to the alternative vanilla PINN methods in a GTX 1080 Ti GPU. The second proposed method in this thesis is based on the hypothesis that providing the predicted solution of the steady-state Navier-Stokes equations as their initial condition might speed up the solution process. In this method, an Unet-based architecture is trained on a discretized representation of the whole problem domain given its initial and boundary conditions. The trained model is used to predict the converged solution of similar cases and the obtained results are transferred to the computational mesh of that problem. This method is tested on the steady, incompressible, subsonic flow around arbitrary airfoils.

Subject Keywords

Partial Differential Equations, Physics informed neural networks, Deep learning, Computational Fluid Dynamics, Parametric Encoding

URI

https://hdl.handle.net/11511/99620

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

D. A. Acar, “Deep Learning Based Speed Up of Fluid Dynamics Solvers,” M.S. - Master of Science, Middle East Technical University, 2022.