# 12. Lattice Boltzmann¶

For an implicit treatment of a solvent, ESPResSo allows to couple the molecular dynamics simulation to a lattice Boltzmann fluid. The Lattice Boltzmann Method (LBM) is a fast, lattice-based method that, in its “pure” form, allows to calculate fluid flow in different boundary conditions of arbitrarily complex geometries. Coupled to molecular dynamics, it allows for the computationally efficient inclusion of hydrodynamic interactions into the simulation. The focus of the ESPResSo implementation of the LBM is, of course, the coupling to MD and therefore available geometries and boundary conditions are somewhat limited in comparison to “pure” LB codes.

Here we restrict the documentation to the interface. For a more detailed description of the method, please refer to the literature.

Note

Please cite [ALK+ed] (Bibtex key espresso2 in `doc/sphinx/zref.bib`

) if you use the LB fluid and [RA12] (Bibtex key lbgpu in `doc/sphinx/zref.bib`

) if you use the GPU implementation.

## 12.1. Setting up a LB fluid¶

The following minimal example illustrates how to use the LBM in ESPResSo:

```
import espressomd
sys = espressomd.System()
sys.box_l = [10, 20, 30]
sys.time_step = 0.01
sys.cell_system.skin = 0.4
lb = espressomd.lb.LBFluid(agrid=1.0, dens=1.0, visc=1.0, tau=0.01)
sys.actors.add(lb)
sys.integrator.run(100)
```

To use the GPU accelerated variant, replace line 5 in the example above by:

```
lb = espressomd.lb.LBFluidGPU(agrid=1.0, dens=1.0, visc=1.0, tau=0.01)
```

Note

Feature `LB`

or `LB_GPU`

required

To use the (much faster) GPU implementation of the LBM, use
`espressomd.lb.LBFluidGPU`

in place of `espressomd.lb.LBFluid`

.
Please note that the GPU implementation uses single precision floating point operations. This decreases the accuracy of calculations compared to the CPU implementation. In particular, due to rounding errors, the fluid density decreases over time, when external forces, coupling to particles, or thermalization is used. The loss of density is on the order of \(10^{-12}\) per time step.

The command initializes the fluid with a given set of parameters. It is
also possible to change parameters on the fly, but this will only rarely
be done in practice. Before being able to use the LBM, it is necessary
to set up a box of a desired size. The parameter is used to set the
lattice constant of the fluid, so the size of the box in every direction
must be a multiple of `agrid`

.

In the following, we discuss the parameters that can be supplied to the LBM in ESPResSo. The detailed interface definition is available at `espressomd.lb.LBFluid`

.

The LB scheme and the MD scheme are not synchronized: In one LB time
step typically several MD steps are performed. This allows to speed up
the simulations and is adjusted with the parameter `tau`

, the LB time step.
The parameters `dens`

and `visc`

set up the density and (kinematic) viscosity of the
LB fluid in (usual) MD units. Internally the LB implementation works
with a different set of units: all lengths are expressed in `agrid`

, all times
in `tau`

and so on.
LB nodes are located at 0.5, 1.5, 2.5, etc.
(in terms of `agrid`

). This has important implications for the location of
hydrodynamic boundaries which are generally considered to be halfway
between two nodes for flat, axis-aligned walls. For more complex boundary geometries, the hydrodynamic boundary location deviates from this midpoint and the deviation decays to first order in `agrid`

.
The LBM should
*not be used as a black box*, but only after a careful check of all
parameters that were applied.

In the following, we describe a number of optional parameters.
Thermalization of the fluid (and particle coupling later on) can be activated by
providing a non-zero value for the parameter `kT`

. Then, a seed has to be provided for
the fluid thermalization:

```
lbfluid = espressomd.lb.LBFluid(kT=1.0, seed=134, ...)
```

The parameter `ext_force_density`

takes a three dimensional vector as an
array_like, representing a homogeneous external body force density in MD
units to be applied to the fluid. The parameter `bulk_visc`

allows one to
tune the bulk viscosity of the fluid and is given in MD units. In the limit of
low Mach number, the flow does not compress the fluid and the resulting flow
field is therefore independent of the bulk viscosity. It is however known that
the value of the viscosity does affect the quality of the implemented
link-bounce-back method. `gamma_even`

and `gamma_odd`

are the relaxation
parameters for the kinetic modes. These fluid parameters do not correspond to
any macroscopic fluid properties, but do influence numerical properties of the
algorithm, such as the magnitude of the error at boundaries. Unless you are an
expert, leave their defaults unchanged. If you do change them, note that they
are to be given in LB units.

Before running a simulation at least the following parameters must be
set up: `agrid`

, `tau`

, `visc`

, `dens`

. For the other parameters, the following are taken: `bulk_visc=0`

, `gamma_odd=0`

, `gamma_even=0`

, `ext_force_density=[0,0,0]`

.

## 12.2. Checkpointing LB¶

```
lb.save_checkpoint(path, binary)
lb.load_checkpoint(path, binary)
```

The first command saves all of the LB fluid nodes’ populations to an ascii
(`binary=0`

) or binary (`binary=1`

) format respectively. The load command
loads the populations from a checkpoint file written with
`lb.save_checkpoint`

. In both cases `path`

specifies the location of the
checkpoint file. This is useful for restarting a simulation either on the same
machine or a different machine. Some care should be taken when using the binary
format as the format of doubles can depend on both the computer being used as
well as the compiler. One thing that one needs to be aware of is that loading
the checkpoint also requires the user to reuse the old forces. This is
necessary since the coupling force between the particles and the fluid has
already been applied to the fluid. Failing to reuse the old forces breaks
momentum conservation, which is in general a problem. It is particularly
problematic for bulk simulations as the system as a whole acquires a drift of
the center of mass, causing errors in the calculation of velocities and
diffusion coefficients. The correct way to restart an LB simulation is to first
load in the particles with the correct forces, and use:

```
sys.integrator.run(steps=number_of_steps, reuse_forces=True)
```

upon the first call to run. This causes the old forces to be reused and thus conserves momentum.

## 12.3. LB as a thermostat¶

The LB fluid can be used to thermalize particles, while also including their hydrodynamic interactions.
The LB thermostat expects an instance of either `espressomd.lb.LBFluid`

or `espressomd.lb.LBFluidGPU`

.
Temperature is set via the `kT`

argument of the LB fluid. Furthermore a seed has to be given for the
thermalization of the particle coupling. The magnitude of the fricitional coupling can be adjusted by
the parameter `gamma`

.
To enable the LB thermostat, use:

```
sys.thermostat.set_lb(LB_fluid=lbf, seed=123, gamma=1.5)
```

The LBM implementation in ESPResSo uses Ahlrichs and Dünweg’s point coupling method to couple MD particles the LB fluid. This coupling consists of a frictional and a random force, similar to the Langevin thermostat:

The momentum acquired by the particles is then transferred back to the fluid using a linear interpolation scheme, to preserve total momentum. In the GPU implementation the force can alternatively be interpolated using a three point scheme which couples the particles to the nearest 27 LB nodes. This can be called using “lbfluid 3pt” and is described in Dünweg and Ladd by equation 301 [DunwegL08].

The frictional force tends to decrease the relative velocity between the fluid and the particle whereas the random forces are chosen so large that the average kinetic energy per particle corresponds to the given temperature, according to a fluctuation dissipation theorem. No other thermostatting mechanism is necessary then. Please switch off any other thermostat before starting the LB thermostatting mechanism.

The LBM implementation provides a fully thermalized LB fluid, all nonconserved modes, including the pressure tensor, fluctuate correctly according to the given temperature and the relaxation parameters. All fluctuations can be switched off by setting the temperature to 0.

Note

Coupling between LB and MD only happens if the LB thermostat is set with a \(\gamma \ge 0.0\).

Regarding the unit of the temperature, please refer to Section On units.

## 12.4. Reading and setting properties of single lattice nodes¶

Appending three indices to the `lb`

object returns an object that represents the selected LB grid node and allows one to access all of its properties:

```
lb[x, y, z].density # fluid density (one scalar for LB and LB_GPU)
lb[x, y, z].velocity # fluid velocity (a numpy array of three floats)
lb[x, y, z].pi # fluid pressure tensor (a symmetric 3x3 numpy array of floats)
lb[x, y, z].pi_neq # nonequilbrium part of the pressure tensor (as above)
lb[x, y, z].boundary # flag indicating whether the node is fluid or boundary (fluid: boundary=0, boundary: boundary != 0)
lb[x, y, z].population # 19 LB populations (a numpy array of 19 floats, check order from the source code)
```

All of these properties can be read and used in further calculations. Only the property `population`

can be modified. The indices `x,y,z`

are integers and enumerate the LB nodes in the three directions, starts with 0. To modify `boundary`

, refer to Setting up boundary conditions.

Examples:

```
print(lb[0, 0, 0].velocity)
lb[0, 0, 0].density = 1.2
```

The first line prints the fluid velocity at node 0 0 0 to the screen. The second line sets this fluid node’s density to the value `1.2`

.

## 12.5. Removing total fluid momentum¶

Note

Only available for `LB_GPU`

Some simulations require the net momentum of the system to vanish. Even if the physics of the system fulfills this condition, numerical errors can introduce drift. To remove the momentum in the fluid call:

```
lb.remove_momentum()
```

## 12.6. Output for visualization¶

ESPResSo implements a number of commands to output fluid field data of the whole fluid into a file at once.

```
lb.print_vtk_velocity(path)
lb.print_vtk_boundary(path)
lb.print_velocity(path)
lb.print_boundary(path)
```

Currently supported fluid properties are the velocity, and boundary flag in ASCII VTK as well as Gnuplot compatible ASCII output.

The VTK format is readable by visualization software such as ParaView 1
or Mayavi2 2. If you plan to use ParaView for visualization, note that also the particle
positions can be exported using the VTK format (see `writevtk()`

).

The variant

```
lb.print_vtk_velocity(path, bb1, bb2)
```

allows you to only output part of the flow field by specifying an axis aligned
bounding box through the coordinates `bb1`

and `bb1`

(lists of three ints) of two of its corners. This
bounding box can be used to output a slice of the flow field. As an
example, executing

```
lb.print_vtk_velocity(path, [0, 0, 5], [10, 10, 5])
```

will output the cross-section of the velocity field in a plane perpendicular to the \(z\)-axis at \(z = 5\) (assuming the box size is 10 in the \(x\)- and \(y\)-direction).

## 12.7. Choosing between the GPU and CPU implementations¶

Note

Feature `LB_GPU`

required

Espresso contains an implementation of the LBM for NVIDIA
GPUs using the CUDA framework. On CUDA-supporting machines this can be
activated by compiling with the feature `LB_GPU`

. Within the
Python script, the `LBFluid`

object can be substituted with the `LBFluidGPU`

object to switch from CPU based to GPU based execution. For further
information on CUDA support see section GPU Acceleration with CUDA.

The following minimal example demonstrates how to use the GPU implementation of the LBM in analogy to the example for the CPU given in section Setting up a LB fluid:

```
import espressomd
sys = espressomd.System()
sys.box_l = [10, 20, 30]
sys.time_step = 0.01
sys.cell_system.skin = 0.4
lb = espressomd.lb.LBFluidGPU(agrid=1.0, dens=1.0, visc=1.0, tau=0.01)
sys.actors.add(lb)
sys.integrator.run(100)
```

For boundary conditions analogous to the CPU
implementation, the feature `LB_BOUNDARIES_GPU`

has to be activated.
The feature `LB_GPU`

allows the use of Lees-Edwards boundary conditions. Our implementation follows the paper of [WP02]. Note, that there is no extra python interface for the use of Lees-Edwards boundary conditions with the LB algorithm. All information are rather internally derived from the set of the Lees-Edwards offset in the system class. For further information Lees-Edwards boundary conditions please refer to section Lees-Edwards boundary conditions

## 12.8. Electrohydrodynamics¶

Note

This needs the feature

`LB_ELECTROHYDRODYNAMICS`

.

If the feature is activated, the Lattice Boltzmann Code can be used to implicitly model surrounding salt ions in an external electric field by having the charged particles create flow.

For that to work, you need to set the electrophoretic mobility (multiplied by the external \(E\)-field) \(\mu E\) on the particles that should be subject to the field. This effectively acts as a velocity offset between the particle and the LB fluid.

For more information on this method and how it works, read the publication [HHHS10].

## 12.9. Using shapes as lattice Boltzmann boundary¶

Note

Feature `LB_BOUNDARIES`

required

Lattice Boltzmann boundaries are implemented in the module
`espressomd.lbboundaries`

. You might want to take a look
at the classes `espressomd.lbboundaries.LBBoundary`

and `espressomd.lbboundaries.LBBoundaries`

for more information.

Adding a shape-based boundary is straightforward:

```
lbb = espressomd.lbboundaries.LBBoundary(shape=my_shape, velocity=[0, 0, 0])
system.lbboundaries.add(lbb)
```

or:

```
lbb = espressomd.lbboundaries.LBBoundary()
lbb.shape = my_shape
lbb.velocity = [0, 0, 0]
system.lbboundaries.add(lbb)
```

### 12.9.1. Minimal usage example¶

Note

Feature `LB_BOUNDARIES`

or `LB_BOUNDARIES_GPU`

required

In order to add a wall as boundary for a lattice Boltzmann fluid you could do the following:

```
wall = espressomd.shapes.Wall(dist=5, normal=[1, 0, 0])
lbb = espressomd.lbboundaries.LBBoundary(shape=wall, velocity=[0, 0, 0])
system.lbboundaries.add(lbb)
```

### 12.9.2. Setting up boundary conditions¶

The following example sets up a system consisting of a spherical boundary in the center of the simulation box acting as a no-slip boundary for the LB fluid that is driven by 4 walls with a slip velocity:

```
from espressomd import System, lb, lbboundaries, shapes
sys = System()
sys.box_l = [64, 64, 64]
sys.time_step = 0.01
sys.cell_system.skin = 0.4
lb = lb.LBFluid(agrid=1.0, dens=1.0, visc=1.0, tau=0.01)
sys.actors.add(lb)
v = [0, 0, 0.01] # the boundary slip
walls = [None] * 4
wall_shape = shapes.Wall(normal=[1, 0, 0], dist=1)
walls[0] = lbboundaries.LBBoundary(shape=wall_shape, velocity=v)
wall_shape = shapes.Wall(normal=[-1, 0, 0], dist=-63)
walls[1] = lbboundaries.LBBoundary(shape=wall_shape, velocity=v)
wall_shape = shapes.Wall(normal=[0, 1, 0], dist=1)
walls[2] = lbboundaries.LBBoundary(shape=wall_shape, velocity=v)
wall_shape = shapes.Wall(normal=[0, -1, 0], dist=-63)
walls[3] = lbboundaries.LBBoundary(shape=wall_shape, velocity=v)
for wall in walls:
system.lbboundaries.add(wall)
sphere_shape = shapes.Sphere(radius=5.5, center=[33, 33, 33], direction=1)
sphere = lbboundaries.LBBoundary(shape=sphere_shape)
sys.lbboundaries.add(sphere)
sys.integrator.run(4000)
print(sphere.get_force())
```

After integrating the system for a sufficient time to reach the steady state, the hydrodynamic drag force exerted on the sphere is evaluated.

The LB boundaries use the same `shapes`

objects to specify their geometry as `constraints`

do for particles. This allows the user to quickly set up a system with boundary conditions that simultaneously act on the fluid and particles. For a complete description of all available shapes, refer to `espressomd.shapes`

.

Intersecting boundaries are in principle possible but must be treated with care. In the current implementation, all nodes that are within at least one boundary are treated as boundary nodes.

Currently, only the so called “link-bounce-back” algorithm for wall
nodes is available. This creates a boundary that is located
approximately midway between the lattice nodes, so in the above example `wall[0]`

corresponds to a boundary at \(x=1.5\). Note that the
location of the boundary is unfortunately not entirely independent of
the viscosity. This can be seen when using the sample script with a high
viscosity.

The bounce back boundary conditions permit it to set the velocity at the boundary
to a nonzero value via the `v`

property of an `LBBoundary`

object. This allows to create shear flow and boundaries
moving relative to each other. The velocity boundary conditions are
implemented according to [Suc01] eq. 12.58. Using
this implementation as a blueprint for the boundary treatment, an
implementation of the Ladd-Coupling should be relatively
straightforward. The `LBBoundary`

object furthermore possesses a property `force`

, which keeps track of the hydrodynamic drag force exerted onto the boundary by the moving fluid.