Application of machine learning to viscoplastic flow modeling
This work addresses the need for faster simulations of viscoplastic flows, which is important for engineering applications, but the method is incremental as it combines existing techniques.
The authors developed a reduced-order model for duct flows of Bingham media using POD and neural networks, achieving several orders of magnitude speedup with reasonable accuracy.
We present a method to construct reduced-order models for duct flows of Bingham media. Our method is based on proper orthogonal decomposition (POD) to find a low-dimensional approximation to the velocity and artificial neural network to approximate the coefficients of a given solution in the constructed POD basis. We use well-established augmented Lagrangian method and finite-element discretization in the "offline" stage. We show that the resulting approximation has a reasonable accuracy, but the evaluation of the approximate solution several orders of magnitude times faster.