Lattice Boltzmann methods for solids

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

The Lattice Boltzmann methods for solids (LBMS) are a set of methods for solving partial differential equations (PDE) in solid mechanics. The methods use a discretization of the Boltzmann equation(BM), and their use is known as the lattice Boltzmann methods for solids.

LBMS methods are categorized by their reliance on:

  • Vectorial distributions[1]
  • Wave solvers[2]
  • Force tuning[3]

The LBMS subset remains highly challenging from a computational aspect as much as from a theoretical point of view. Solving solid equations within the LBM framework is still a very active area of research. If solids are solved, this shows that the Boltzmann equation is capable of describing solid motions as well as fluids and gases: thus unlocking complex physics to be solved such as fluid-structure interaction (FSI) in biomechanics.

Proposed insights

[edit | edit source]

Vectorial distributions

[edit | edit source]

The first attempt[1] of LBMS tried to use a Boltzmann-like equation for force (vectorial) distributions. The approach requires more computational memory but results are obtained in fracture and solid cracking.

Wave solvers

[edit | edit source]

Another approach consists in using LBM as acoustic solvers to capture waves propagation in solids.[2][4][5][6]

Force tuning

[edit | edit source]

Introduction

[edit | edit source]

This idea consists of introducing a modified version of the forcing term:[7] (or equilibrium distribution[8]) into the LBM as a stress divergence force. This force is considered space-time dependent and contains solid properties[Note 1]

g=1ρxσ,

where σ denotes the Cauchy stress tensor. g and ρ are respectively the gravity vector and solid matter density. The stress tensor is usually computed across the lattice aiming finite difference schemes.

Some results

[edit | edit source]
2D displacement magnitude on a solid system using force tuning. Obtained field is in accordance with finite element methods results.

Force tuning[3] has recently proven its efficiency with a maximum error of 5% in comparison with standard finite element solvers in mechanics. Accurate validation of results can also be a tedious task since these methods are very different, common issues are:

  • Meshes or lattice discretization
  • Location of computed fields at elements or nodes
  • Hidden information in software used for finite element analysis comparison
  • Non-linear materials
  • Steady state convergence for LBMS

Notes

[edit | edit source]
  1. ^ Matter properties such as Young's modulus and Poisson's ratio.

See also

[edit | edit source]

References

[edit | edit source]
  1. ^ a b Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  2. ^ a b Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  3. ^ a b Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  4. ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  5. ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  6. ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  7. ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  8. ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).