Data-Driven Multi-Grid Solver for Accelerated Pressure Projection

Understand

Pressure projection is the single most computationally expensive step in an unsteady incompressible fluid simulation.

• This work demonstrates the ability of data-driven methods to accelerate the approximate solution of the Poisson equation at the heart of pressure projection.
• Geometric Multi-Grid methods are identified as linear convolutional encoder-decoder networks and a data-driven smoother is developed using automatic differentiation to optimize the velocity-divergence projection.
• The new method is found to accelerate classic Multi-Grid methods by a factor of two to three with no loss of accuracy on eleven 2D and 3D flow cases including cases with dynamic immersed solid boundaries.