Rods

Rods#

Base class for rods

class elastica.rod.rod_base.RodBase[source]#

Base class for all rods.

Notes

All new rod classes must be derived from this RodBase class.

Cosserat Rod#

	On Nodes (+1)	On Elements (n_elements)	On Voronoi (-1)
Geometry	position	director, tangents length, rest_length radius volume dilatation	rest voronoi length voronoi dilatation
Kinematics	velocity acceleration external forces damping forces	angular velocity (omega) angular acceleration (alpha) mass second moment of inertia +inverse dilatation rates external torques damping torques
Elasticity	internal forces	shear matrix (modulus) shear/stretch strain (sigma) rest shear/stretch strain internal torques internal stress	bend matrix (modulus) bend/twist strain (kappa) rest bend/twist strain internal couple
Material	mass	density dissipation constant (force, torque)

Rod classes and implementation details

class elastica.rod.cosserat_rod.CosseratRod(n_elements, position, velocity, omega, acceleration, angular_acceleration, directors, radius, mass_second_moment_of_inertia, inv_mass_second_moment_of_inertia, shear_matrix, bend_matrix, density, volume, mass, internal_forces, internal_torques, external_forces, external_torques, lengths, rest_lengths, tangents, dilatation, dilatation_rate, voronoi_dilatation, rest_voronoi_lengths, sigma, kappa, rest_sigma, rest_kappa, internal_stress, internal_couple, ring_rod_flag)[source]#

Cosserat Rod class. This is the preferred class for rods because it is derived from some of the essential base classes.

Attributes:

n_elems: int: The number of elements of the rod.
position_collection: numpy.ndarray: 2D (dim, n_nodes) array containing data with ‘float’ type. Array containing node position vectors.
velocity_collection: numpy.ndarray: 2D (dim, n_nodes) array containing data with ‘float’ type. Array containing node velocity vectors.
acceleration_collection: numpy.ndarray: 2D (dim, n_nodes) array containing data with ‘float’ type. Array containing node acceleration vectors.
omega_collection: numpy.ndarray: 2D (dim, n_elems) array containing data with ‘float’ type. Array containing element angular velocity vectors.
alpha_collection: numpy.ndarray: 2D (dim, n_elems) array containing data with ‘float’ type. Array contining element angular acceleration vectors.
director_collection: numpy.ndarray: 3D (dim, dim, n_elems) array containing data with ‘float’ type. Array containing element director matrices.
rest_lengths: numpy.ndarray: 1D (n_elems) array containing data with ‘float’ type. Rod element lengths at rest configuration.
density: numpy.ndarray: 1D (n_elems) array containing data with ‘float’ type. Rod elements densities.
volume: numpy.ndarray: 1D (n_elems) array containing data with ‘float’ type. Rod element volumes.
mass: numpy.ndarray: 1D (n_nodes) array containing data with ‘float’ type. Rod node masses. Note that masses are stored on the nodes, not on elements.
mass_second_moment_of_inertia: numpy.ndarray: 3D (dim, dim, n_elems) array containing data with ‘float’ type. Rod element mass second moment of interia.
inv_mass_second_moment_of_inertia: numpy.ndarray: 3D (dim, dim, n_elems) array containing data with ‘float’ type. Rod element inverse mass moment of inertia.
rest_voronoi_lengths: numpy.ndarray: 1D (n_voronoi) array containing data with ‘float’ type. Rod lengths on the voronoi domain at the rest configuration.
internal_forces: numpy.ndarray: 2D (dim, n_nodes) array containing data with ‘float’ type. Rod node internal forces. Note that internal forces are stored on the node, not on elements.
internal_torques: numpy.ndarray: 2D (dim, n_elems) array containing data with ‘float’ type. Rod element internal torques.
external_forces: numpy.ndarray: 2D (dim, n_nodes) array containing data with ‘float’ type. External forces acting on rod nodes.
external_torques: numpy.ndarray: 2D (dim, n_elems) array containing data with ‘float’ type. External torques acting on rod elements.
lengths: numpy.ndarray: 1D (n_elems) array containing data with ‘float’ type. Rod element lengths.
tangents: numpy.ndarray: 2D (dim, n_elems) array containing data with ‘float’ type. Rod element tangent vectors.
radius: numpy.ndarray: 1D (n_elems) array containing data with ‘float’ type. Rod element radius.
dilatation: numpy.ndarray: 1D (n_elems) array containing data with ‘float’ type. Rod element dilatation.
voronoi_dilatation: numpy.ndarray: 1D (n_voronoi) array containing data with ‘float’ type. Rod dilatation on voronoi domain.
dilatation_rate: numpy.ndarray: 1D (n_elems) array containing data with ‘float’ type. Rod element dilatation rates.

classmethod straight_rod(n_elements, start, direction, normal, base_length, base_radius, density, *, nu=None, youngs_modulus, **kwargs)[source]#

Cosserat rod constructor for straight-rod geometry.

Parameters:

n_elementsint: Number of element. Must be greater than 3. Generally recommended to start with 40-50, and adjust the resolution.
startNDArray[3, float]: Starting coordinate in 3D
directionNDArray[3, float]: Direction of the rod in 3D
normalNDArray[3, float]: Normal vector of the rod in 3D
base_lengthfloat: Total length of the rod
base_radiusfloat: Uniform radius of the rod
densityfloat: Density of the rod
nufloat: Damping coefficient for Rayleigh damping
youngs_modulusfloat: Young’s modulus
**kwargsdict, optional: The “position” and/or “directors” can be overrided by passing “position” and “directors” argument. Remember, the shape of the “position” is (3,n_elements+1) and the shape of the “directors” is (3,3,n_elements).

Returns:

CosseratRod

Notes

Since we expect the Cosserat Rod to simulate soft rod, Poisson’s ratio is set to 0.5 by default. It is possible to give additional argument “shear_modulus” or “poisson_ratio” to specify extra modulus.

classmethod ring_rod(n_elements, ring_center_position, direction, normal, base_length, base_radius, density, *, nu=None, youngs_modulus, **kwargs)[source]#

Cosserat rod constructor for straight-rod geometry.

Parameters:

n_elementsint: Number of element. Must be greater than 3. Generarally recommended to start with 40-50, and adjust the resolution.
ring_center_positionNDArray[3, float]: Center coordinate for ring rod in 3D
directionNDArray[3, float]: Direction of the rod in 3D
normalNDArray[3, float]: Normal vector of the rod in 3D
base_lengthfloat: Total length of the rod
base_radiusfloat: Uniform radius of the rod
densityfloat: Density of the rod
nufloat: Damping coefficient for Rayleigh damping
youngs_modulusfloat: Young’s modulus
**kwargsdict, optional: The “position” and/or “directors” can be overrided by passing “position” and “directors” argument. Remember, the shape of the “position” is (3,n_elements+1) and the shape of the “directors” is (3,3,n_elements).

Returns:

CosseratRod

Notes

compute_translational_energy()[source]#: Compute total translational energy of the rod at the instance.

compute_rotational_energy()[source]#: Compute total rotational energy of the rod at the instance.

compute_velocity_center_of_mass()[source]#: Compute velocity center of mass of the rod at the instance.

compute_position_center_of_mass()[source]#: Compute position center of mass of the rod at the instance.

compute_bending_energy()[source]#: Compute total bending energy of the rod at the instance.

compute_shear_energy()[source]#: Compute total shear energy of the rod at the instance.

compute_link(type_of_additional_segment='next_tangent')#

See Knot Theory (Mixin) for the detail.

Parameters:

type_of_additional_segmentstr: Determines the method to compute new segments (elements) added to the rod. Valid inputs are “next_tangent”, “end_to_end”, “net_tangent”, otherwise program uses the center line.

compute_twist()#: See Knot Theory (Mixin) for the detail.

compute_writhe(type_of_additional_segment='next_tangent')#

See Knot Theory (Mixin) for the detail.

Parameters:

type_of_additional_segmentstr: Determines the method to compute new segments (elements) added to the rod. Valid inputs are “next_tangent”, “end_to_end”, “net_tangent”, otherwise program uses the center line.

Knot Theory (Mixin)#

This script is for computing the link-writhe-twist (LWT) of a rod using the method from Klenin & Langowski 2000 paper. Algorithms are adapted from section S2 of Charles et. al. PRL 2019 paper.

Following example cases includes computing LWT quantities to study the bifurcation:

The details discussion is included in N Charles et. al. PRL (2019).

class elastica.rod.knot_theory.KnotTheoryCompatibleProtocol(*args, **kwargs)[source]#: Required properties to use KnotTheory mixin

class elastica.rod.knot_theory.KnotTheory[source]#

This mixin should be used in RodBase-derived class that satisfies KnotCompatibleProtocol. The theory behind this module is based on the method from Klenin & Langowski 2000 paper.

KnotTheory can be mixed with any rod-class based on RodBase:

class MyRod(RodBase, KnotTheory):
    def __init__(self):
        super().__init__()
rod = MyRod(...)

total_twist = rod.compute_twist()
total_link = rod.compute_link()

There are few alternative way of handling edge-condition in computing Link and Writhe. Here, we provide three methods: “next_tangent”, “end_to_end”, and “net_tangent”. The default type_of_additional_segment is set to “next_tangent.”

type_of_additional_segment	Description
next_tangent	Adds a two new point at the begining and end of the center line. Distance of these points are given in segment_length. Direction of these points are computed using the rod tangents at the begining and end.
end_to_end	Adds a two new point at the begining and end of the center line. Distance of these points are given in segment_length. Direction of these points are computed using the rod node end positions.
net_tangent	Adds a two new point at the begining and end of the center line. Distance of these points are given in segment_length. Direction of these points are point wise avarege of nodes at the first and second half of the rod.

compute_twist()[source]#: See Knot Theory (Mixin) for the detail.

compute_writhe(type_of_additional_segment='next_tangent')[source]#

See Knot Theory (Mixin) for the detail.

Parameters:

type_of_additional_segmentstr: Determines the method to compute new segments (elements) added to the rod. Valid inputs are “next_tangent”, “end_to_end”, “net_tangent”, otherwise program uses the center line.

compute_link(type_of_additional_segment='next_tangent')[source]#

See Knot Theory (Mixin) for the detail.

Parameters:

type_of_additional_segmentstr: Determines the method to compute new segments (elements) added to the rod. Valid inputs are “next_tangent”, “end_to_end”, “net_tangent”, otherwise program uses the center line.

elastica.rod.knot_theory.compute_twist(center_line, normal_collection)[source]#

Compute the twist of a rod, using center_line and normal collection.

Methods used in this function is adapted from method 2a Klenin & Langowski 2000 paper.

Warning

If center line is straight, although the normals of each element is pointing different direction computed twist will be zero.

Typical runtime of this function is longer than simulation steps. While we provide a function to compute topological quantities at every timesteps, we highly recommend to compute LWT during the post-processing stage.:

import elastica
...
normal_collection = director_collection[:,0,...] # shape of director (time, 3, 3, n_elems)
elastica.compute_twist(
    center_line,                                 # shape (time, 3, n_nodes)
    normal_collection                            # shape (time, 3, n_elems)
)

Parameters:

center_linenumpy.ndarray: 3D (time, 3, n_nodes) array containing data with ‘float’ type. Time history of rod node positions.
normal_collectionnumpy.ndarray: 3D (time, 3, n_elems) array containing data with ‘float’ type. Time history of rod elements normal direction.

Returns:

total_twistnumpy.ndarray
local_twistnumpy.ndarray

elastica.rod.knot_theory.compute_writhe(center_line, segment_length, type_of_additional_segment)[source]#

This function computes the total writhe history of a rod.

Equations used are from method 1a from Klenin & Langowski 2000 paper.

import elastica
...
elastica.compute_writhe(
    center_line,                               # shape (time, 3, n_nodes)
    segment_length,
    type_of_additional_segment="next_tangent"
)

Parameters:

center_linenumpy.ndarray: 3D (time, 3, n_nodes) array containing data with ‘float’ type. Time history of rod node positions.
segment_lengthfloat: Length of added segments.
type_of_additional_segmentstr: Determines the method to compute new segments (elements) added to the rod. Valid inputs are “next_tangent”, “end_to_end”, “net_tangent”, otherwise program uses the center line.

Returns:

total_writhenumpy.ndarray

elastica.rod.knot_theory.compute_link(center_line, normal_collection, radius, segment_length, type_of_additional_segment)[source]#

This function computes the total link history of a rod.

Equations used are from method 1a from Klenin & Langowski 2000 paper.

import elastica
...
normal_collection = director_collection[:,0,...] # shape of director (time, 3, 3, n_elems)
elastica.compute_link(
    center_line,                                 # shape (time, 3, n_nodes)
    normal_collection,                           # shape (time 3, n_elems)
    radius,                                      # shape (time, n_elems)
    segment_length,
    type_of_additional_segment="next_tangent"
)

Parameters:

center_linenumpy.ndarray: 3D (time, 3, n_nodes) array containing data with ‘float’ type. Time history of rod node positions.
normal_collectionnumpy.ndarray: 3D (time, 3, n_elems) array containing data with ‘float’ type. Time history of rod elements normal direction.
radiusnumpy.ndarray: 2D (time, n_elems) array containing data with ‘float’ type. Time history of rod element radius.
segment_lengthfloat: Length of added segments.
type_of_additional_segmentstr: Determines the method to compute new segments (elements) added to the rod. Valid inputs are “next_tangent”, “end_to_end”, “net_tangent”, otherwise program uses the center line.

Returns:

total_linknumpy.ndarray

Rods

Contents

Rods#

Cosserat Rod#

Knot Theory (Mixin)#