An inverse analysis of fluid flow through granular media using differentiable lattice Boltzmann method

Abstract

This study presents a method for the inverse analysis of fluid flow problems. The focus is put on accurately determining boundary conditions and characterizing the physical properties of granular media, such as permeability, and fluid components, like viscosity. The primary aim is to deduce either constant pressure head or pressure profiles, given the known velocity field at a steady-state flow through a conduit containing obstacles, including walls, spheres, and grains. The lattice Boltzmann method (LBM) combined with automatic differentiation (AD) (AD-LBM) is employed, with the help of the GPU-capable Taichi programming language. A lightweight tape is used to generate gradients for the entire LBM simulation, enabling end-to-end backpropagation. Our AD-LBM approach accurately estimates the boundary conditions for complex flow paths in porous media, leading to observed steady-state velocity fields and deriving macro-scale permeability and fluid viscosity. The method demonstrates significant advantages in terms of prediction accuracy and computational efficiency, making it a powerful tool for solving inverse fluid flow problems in various applications.