Methods (lbmpy.methods)¶

LBM Method Interfaces¶

class AbstractLbMethod(stencil)¶

Abstract base class for all LBM methods.

property stencil¶: Discrete set of velocities, represented as nested tuple

property pre_collision_pdf_symbols¶: Tuple of symbols representing the pdf values before collision

property post_collision_pdf_symbols¶: Tuple of symbols representing the pdf values after collision

abstract conserved_quantity_computation()¶: Returns an instance of class lbmpy.methods.AbstractConservedQuantityComputation

abstract weights()¶: Returns a sequence of weights, one for each lattice direction

abstract get_equilibrium()¶: Returns equation collection, to compute equilibrium values. The equations have the post collision symbols as left hand sides and are functions of the conserved quantities

abstract get_collision_rule()¶: Returns an LbmCollisionRule i.e. an equation collection with a reference to the method. This collision rule defines the collision operator.

class AbstractConservedQuantityComputation¶

This class defines how conserved quantities are computed as functions of the pdfs. Conserved quantities are used for output and as input to the equilibrium in the collision step

Depending on the method they might also be computed slightly different, e.g. due to a force model.

An additional method describes how to get the conserved quantities for the equilibrium for initialization. In most cases the inputs can be used directly, but for some methods they have to be altered slightly. For example in zero centered hydrodynamic schemes with force model, the density has to be decreased by one, and the given velocity has to be shifted dependent on the force.

abstract property conserved_quantities¶: Dict, mapping names (symbol) to dimensionality (int) For example: {‘density’ : 1, ‘velocity’ : 3} The naming strings can be used in output_equations_from_pdfs() and equilibrium_input_equations_from_init_values()

defined_symbols(order='all')¶: Returns a dict, mapping names of conserved quantities to their symbols

abstract property default_values¶: Returns a dict of symbol to default value, where “default” means that the equilibrium simplifies to the weights if these values are inserted. Hydrodynamic example: rho=1, u_i = 0

abstract equilibrium_input_equations_from_pdfs(pdfs)¶

Returns an equation collection that defines all necessary quantities to compute the equilibrium as functions of the pdfs. For hydrodynamic LBM schemes this is usually the density and velocity.

Parameters: pdfs – values or symbols for the pdf values

abstract output_equations_from_pdfs(pdfs, output_quantity_names_to_symbols)¶

Returns an equation collection that defines conserved quantities for output. These conserved quantities might be slightly different that the ones used as input for the equilibrium e.g. due to a force model.

Parameters

pdfs – values for the pdf entries
output_quantity_names_to_symbols – dict mapping of conserved quantity names (See conserved_quantities()) to symbols or field accesses where they should be written to

abstract equilibrium_input_equations_from_init_values(**kwargs)¶: Returns an equation collection that defines all necessary quantities to compute the equilibrium as function of given conserved quantities. Parameters can be names that are given by symbol names of conserved_quantities(). For all parameters not specified each implementation should use sensible defaults. For example hydrodynamic schemes use density=1 and velocity=0.

LBM with conserved zeroth and first order¶

class DensityVelocityComputation(stencil, compressible, force_model=None, zeroth_order_moment_symbol=rho, first_order_moment_symbols=u_0, u_1, u_2)¶

property conserved_quantities¶: Dict, mapping names (symbol) to dimensionality (int) For example: {‘density’ : 1, ‘velocity’ : 3} The naming strings can be used in output_equations_from_pdfs() and equilibrium_input_equations_from_init_values()

defined_symbols(order='all')¶: Returns a dict, mapping names of conserved quantities to their symbols

property default_values¶: Returns a dict of symbol to default value, where “default” means that the equilibrium simplifies to the weights if these values are inserted. Hydrodynamic example: rho=1, u_i = 0

equilibrium_input_equations_from_pdfs(pdfs)¶

Returns an equation collection that defines all necessary quantities to compute the equilibrium as functions of the pdfs. For hydrodynamic LBM schemes this is usually the density and velocity.

Parameters: pdfs – values or symbols for the pdf values

equilibrium_input_equations_from_init_values(density=1, velocity=0, 0, 0)¶: Returns an equation collection that defines all necessary quantities to compute the equilibrium as function of given conserved quantities. Parameters can be names that are given by symbol names of conserved_quantities(). For all parameters not specified each implementation should use sensible defaults. For example hydrodynamic schemes use density=1 and velocity=0.

output_equations_from_pdfs(pdfs, output_quantity_names_to_symbols)¶

Returns an equation collection that defines conserved quantities for output. These conserved quantities might be slightly different that the ones used as input for the equilibrium e.g. due to a force model.

Parameters

pdfs – values for the pdf entries
output_quantity_names_to_symbols – dict mapping of conserved quantity names (See conserved_quantities()) to symbols or field accesses where they should be written to

Moment-based methods¶

Creation Functions¶

create_srt(stencil, relaxation_rate, maxwellian_moments=False, **kwargs)¶

Creates a single relaxation time (SRT) lattice Boltzmann model also known as BGK model.

Parameters

stencil – nested tuple defining the discrete velocity space. See lbmpy.stencils.get_stencil()
relaxation_rate – relaxation rate (inverse of the relaxation time) usually called \(\omega\) in LBM literature
maxwellian_moments – determines if the discrete or continuous maxwellian equilibrium is used to compute the equilibrium moments

Returns

lbmpy.methods.MomentBasedLbMethod instance

create_trt(stencil, relaxation_rate_even_moments, relaxation_rate_odd_moments, maxwellian_moments=False, **kwargs)¶

Creates a two relaxation time (TRT) lattice Boltzmann model, where even and odd moments are relaxed differently. In the SRT model the exact wall position of no-slip boundaries depends on the viscosity, the TRT method does not have this problem.

Parameters are similar to lbmpy.methods.create_srt(), but instead of one relaxation rate there are two relaxation rates: one for even moments (determines viscosity) and one for odd moments. If unsure how to choose the odd relaxation rate, use the function lbmpy.methods.create_trt_with_magic_number()

create_trt_with_magic_number(stencil, relaxation_rate, magic_number=3 / 16, **kwargs)¶: Creates a two relaxation time (TRT) lattice Boltzmann method, where the relaxation time for odd moments is determines from the even moment relaxation time and a “magic number”. For possible parameters see lbmpy.methods.create_trt()

create_mrt_orthogonal(stencil, relaxation_rate_getter, maxwellian_moments=False, weighted=None, nested_moments=None, **kwargs)¶

Returns an orthogonal multi-relaxation time model for the stencils D2Q9, D3Q15, D3Q19 and D3Q27. These MRT methods are just one specific version - there are many MRT methods possible for all these stencils which differ by the linear combination of moments and the grouping into equal relaxation times. To create a generic MRT method use create_with_discrete_maxwellian_eq_moments

Parameters

stencil – nested tuple defining the discrete velocity space. See get_stencil
relaxation_rate_getter – function getting a list of moments as argument, returning the associated relaxation
maxwellian_moments – determines if the discrete or continuous maxwellian equilibrium is used to compute the equilibrium moments
weighted – whether to use weighted or unweighted orthogonality
nested_moments – a list of lists of modes, grouped by common relaxation times. This is usually used in conjunction with mrt_orthogonal_modes_literature. If this argument is not provided, Gram-Schmidt orthogonalization of the default modes is performed.

create_with_continuous_maxwellian_eq_moments(stencil, moment_to_relaxation_rate_dict, compressible=False, force_model=None, equilibrium_order=2, cumulant=False, c_s_sq=1 / 3)¶: Creates a moment-based LBM by taking a list of moments with corresponding relaxation rate. These moments are relaxed against the moments of the continuous Maxwellian distribution. For parameter description see lbmpy.methods.create_with_discrete_maxwellian_eq_moments(). By using the continuous Maxwellian we automatically get a compressible model.

create_with_discrete_maxwellian_eq_moments(stencil, moment_to_relaxation_rate_dict, compressible=False, force_model=None, equilibrium_order=2, cumulant=False, c_s_sq=1 / 3)¶

Creates a moment-based LBM by taking a list of moments with corresponding relaxation rate.

These moments are relaxed against the moments of the discrete Maxwellian distribution.

Parameters

stencil – nested tuple defining the discrete velocity space. See get_stencil
moment_to_relaxation_rate_dict – dict that has as many entries as the stencil. Each moment, which can be represented by an exponent tuple or in polynomial form (see lbmpy.moments), is mapped to a relaxation rate.
compressible – incompressible LBM methods split the density into \(\rho = \rho_0 + \Delta \rho\) where \(\rho_0\) is chosen as one, and the first moment of the pdfs is \(\Delta \rho\) . This approximates the incompressible Navier-Stokes equations better than the standard compressible model.
force_model – force model instance, or None if no external forces
equilibrium_order – approximation order of macroscopic velocity \(\mathbf{u}\) in the equilibrium
cumulant – if True relax cumulants instead of moments
c_s_sq – Speed of sound squared

Returns

lbmpy.methods.MomentBasedLbMethod instance

Class¶

class MomentBasedLbMethod(stencil, moment_to_relaxation_info_dict, conserved_quantity_computation=None, force_model=None)¶

property conserved_quantity_computation¶: Returns an instance of class lbmpy.methods.AbstractConservedQuantityComputation

property weights¶: Returns a sequence of weights, one for each lattice direction

get_equilibrium(conserved_quantity_equations=None, include_force_terms=False)¶: Returns equation collection, to compute equilibrium values. The equations have the post collision symbols as left hand sides and are functions of the conserved quantities

get_collision_rule(conserved_quantity_equations=None, keep_rrs_symbolic=True)¶: Returns an LbmCollisionRule i.e. an equation collection with a reference to the method. This collision rule defines the collision operator.