Source code for elastica.interaction

__doc__ = """ Module containing interactions between a rod and its environment. """

import numpy as np
import numba
from ._linalg import _batch_matmul, _batch_matvec, _batch_cross
from elastica.utils import MaxDimension
from elastica.external_forces import NoForces


[docs]def find_slipping_elements(velocity_slip, velocity_threshold): """ This function takes the velocity of elements and checks if they are larger than the threshold velocity. If the velocity of elements is larger than threshold velocity, that means those elements are slipping. In other words, kinetic friction will be acting on those elements, not static friction. This function outputs an array called slip function, this array has a size of the number of elements. If the velocity of the element is smaller than the threshold velocity slip function value for that element is 1, which means static friction is acting on that element. If the velocity of the element is larger than the threshold velocity slip function value for that element is between 0 and 1, which means kinetic friction is acting on that element. Parameters ---------- velocity_slip : numpy.ndarray 2D (dim, blocksize) array containing data with 'float' type. Rod-like object element velocity. velocity_threshold : float Threshold velocity to determine slip. Returns ------- slip_function : numpy.ndarray 2D (dim, blocksize) array containing data with 'float' type. """ abs_velocity_slip = np.sqrt(np.einsum("ij, ij->j", velocity_slip, velocity_slip)) slip_points = np.where(np.fabs(abs_velocity_slip) > velocity_threshold) slip_function = np.ones((velocity_slip.shape[1])) slip_function[slip_points] = np.fabs( 1.0 - np.minimum(1.0, abs_velocity_slip[slip_points] / velocity_threshold - 1.0) ) return slip_function
# TODO: node_to_elements only used in friction, so that it is located here, we can change it. # Converting forces on nodes to elements
[docs]def nodes_to_elements(input): """ This function converts the rod-like object dofs on nodes to dofs on elements. For example, node velocity is converted to element velocity. Parameters ---------- input: numpy.ndarray 2D (dim, blocksize) array containing data with 'float' type. Returns ------- output: numpy.ndarray 2D (dim, blocksize) array containing data with 'float' type. """ # TODO: find a way with out initialzing output vector output = np.zeros((input.shape[0], input.shape[1] - 1)) output[..., :-1] += 0.5 * input[..., 1:-1] output[..., 1:] += 0.5 * input[..., 1:-1] output[..., 0] += input[..., 0] output[..., -1] += input[..., -1] return output
# base class for interaction # only applies normal force no friction
[docs]class InteractionPlane: """ The interaction plane class computes the plane reaction force on a rod-like object. For more details regarding the contact module refer to Eqn 4.8 of Gazzola et al. RSoS (2018). Attributes ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane. surface_tol: float Penetration tolerance between the plane and the rod-like object. """
[docs] def __init__(self, k, nu, plane_origin, plane_normal): """ Parameters ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane. """ self.k = k self.nu = nu self.plane_origin = plane_origin.reshape(3, 1) self.plane_normal = plane_normal.reshape(3) self.surface_tol = 1e-4
[docs] def apply_normal_force(self, system): """ In the case of contact with the plane, this function computes the plane reaction force on the element. Parameters ---------- system: object Rod-like object. Returns ------- plane_response_force_mag : numpy.ndarray 1D (blocksize) array containing data with 'float' type. Magnitude of plane response force acting on rod-like object. no_contact_point_idx : numpy.ndarray 1D (blocksize) array containing data with 'int' type. Index of rod-like object elements that are not in contact with the plane. """ # Compute plane response force nodal_total_forces = system.internal_forces + system.external_forces element_total_forces = nodes_to_elements(nodal_total_forces) force_component_along_normal_direction = np.einsum( "i, ij->j", self.plane_normal, element_total_forces ) forces_along_normal_direction = np.einsum( "i, j->ij", self.plane_normal, force_component_along_normal_direction ) # If the total force component along the plane normal direction is greater than zero that means, # total force is pushing rod away from the plane not towards the plane. Thus, response force # applied by the surface has to be zero. forces_along_normal_direction[ ..., np.where(force_component_along_normal_direction > 0) ] = 0.0 # Compute response force on the element. Plane response force # has to be away from the surface and towards the element. Thus # multiply forces along normal direction with negative sign. plane_response_force = -forces_along_normal_direction # Elastic force response due to penetration element_position = 0.5 * ( system.position_collection[..., :-1] + system.position_collection[..., 1:] ) distance_from_plane = np.einsum( "i, ij->j", self.plane_normal, (element_position - self.plane_origin) ) plane_penetration = np.minimum(distance_from_plane - system.radius, 0.0) elastic_force = -self.k * np.einsum( "i, j->ij", self.plane_normal, plane_penetration ) # Damping force response due to velocity towards the plane element_velocity = 0.5 * ( system.velocity_collection[..., :-1] + system.velocity_collection[..., 1:] ) normal_component_of_element_velocity = np.einsum( "i, ij->j", self.plane_normal, element_velocity ) damping_force = -self.nu * np.einsum( "i, j->ij", self.plane_normal, normal_component_of_element_velocity ) # Compute total plane response force plane_response_force_total = ( plane_response_force + elastic_force + damping_force ) # Check if the rod elements are in contact with plane. no_contact_point_idx = np.where( (distance_from_plane - system.radius) > self.surface_tol ) # If rod element does not have any contact with plane, plane cannot apply response # force on the element. Thus lets set plane response force to 0.0 for the no contact points. plane_response_force[..., no_contact_point_idx] = 0.0 plane_response_force_total[..., no_contact_point_idx] = 0.0 system.external_forces[..., :-1] += 0.5 * plane_response_force_total system.external_forces[..., 1:] += 0.5 * plane_response_force_total return ( np.sqrt(np.einsum("ij, ij->j", plane_response_force, plane_response_force)), no_contact_point_idx, )
# class for anisotropic frictional plane # NOTE: friction coefficients are passed as arrays in the order # mu_forward : mu_backward : mu_sideways # head is at x[0] and forward means head to tail # same convention for kinetic and static # mu named as to which direction it opposes
[docs]class AnisotropicFrictionalPlane(NoForces, InteractionPlane): """ This anisotropic friction plane class is for computing anisotropic friction forces on rods. A detailed explanation of the implemented equations can be found in Gazzola et al. RSoS. (2018). Attributes ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane. slip_velocity_tol: float Velocity tolerance to determine if the element is slipping or not. static_mu_array: numpy.ndarray 1D (3,) array containing data with 'float' type. [forward, backward, sideways] static friction coefficients. kinetic_mu_array: numpy.ndarray 1D (3,) array containing data with 'float' type. [forward, backward, sideways] kinetic friction coefficients. """
[docs] def __init__( self, k, nu, plane_origin, plane_normal, slip_velocity_tol, static_mu_array, kinetic_mu_array, ): """ Parameters ---------- k: float Stiffness coefficient between the plane and the rod-like object. nu: float Dissipation coefficient between the plane and the rod-like object. plane_origin: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. Origin of the plane. plane_normal: numpy.ndarray 2D (dim, 1) array containing data with 'float' type. The normal vector of the plane. slip_velocity_tol: float Velocity tolerance to determine if the element is slipping or not. static_mu_array: numpy.ndarray 1D (3,) array containing data with 'float' type. [forward, backward, sideways] static friction coefficients. kinetic_mu_array: numpy.ndarray 1D (3,) array containing data with 'float' type. [forward, backward, sideways] kinetic friction coefficients. """ InteractionPlane.__init__(self, k, nu, plane_origin, plane_normal) self.slip_velocity_tol = slip_velocity_tol ( self.static_mu_forward, self.static_mu_backward, self.static_mu_sideways, ) = static_mu_array ( self.kinetic_mu_forward, self.kinetic_mu_backward, self.kinetic_mu_sideways, ) = kinetic_mu_array
# kinetic and static friction should separate functions # for now putting them together to figure out common variables
[docs] def apply_forces(self, system, time=0.0): # calculate axial and rolling directions plane_response_force_mag, no_contact_point_idx = self.apply_normal_force(system) normal_plane_collection = np.repeat( self.plane_normal.reshape(3, 1), plane_response_force_mag.shape[0], axis=1 ) # First compute component of rod tangent in plane. Because friction forces acts in plane not out of plane. Thus # axial direction has to be in plane, it cannot be out of plane. We are projecting rod element tangent vector in # to the plane. So friction forces can only be in plane forces and not out of plane. tangent_along_normal_direction = np.einsum( "ij, ij->j", system.tangents, normal_plane_collection ) tangent_perpendicular_to_normal_direction = system.tangents - np.einsum( "j, ij->ij", tangent_along_normal_direction, normal_plane_collection ) tangent_perpendicular_to_normal_direction_mag = np.einsum( "ij, ij->j", tangent_perpendicular_to_normal_direction, tangent_perpendicular_to_normal_direction, ) # Normalize tangent_perpendicular_to_normal_direction. This is axial direction for plane. Here we are adding # small tolerance (1e-10) for normalization, in order to prevent division by 0. axial_direction = np.einsum( "ij, j-> ij", tangent_perpendicular_to_normal_direction, 1 / (tangent_perpendicular_to_normal_direction_mag + 1e-14), ) element_velocity = 0.5 * ( system.velocity_collection[..., :-1] + system.velocity_collection[..., 1:] ) # first apply axial kinetic friction velocity_mag_along_axial_direction = np.einsum( "ij,ij->j", element_velocity, axial_direction ) velocity_along_axial_direction = np.einsum( "j, ij->ij", velocity_mag_along_axial_direction, axial_direction ) # Friction forces depends on the direction of velocity, in other words sign # of the velocity vector. velocity_sign_along_axial_direction = np.sign( velocity_mag_along_axial_direction ) # Check top for sign convention kinetic_mu = 0.5 * ( self.kinetic_mu_forward * (1 + velocity_sign_along_axial_direction) + self.kinetic_mu_backward * (1 - velocity_sign_along_axial_direction) ) # Call slip function to check if elements slipping or not slip_function_along_axial_direction = find_slipping_elements( velocity_along_axial_direction, self.slip_velocity_tol ) kinetic_friction_force_along_axial_direction = -( (1.0 - slip_function_along_axial_direction) * kinetic_mu * plane_response_force_mag * velocity_sign_along_axial_direction * axial_direction ) # If rod element does not have any contact with plane, plane cannot apply friction # force on the element. Thus lets set kinetic friction force to 0.0 for the no contact points. kinetic_friction_force_along_axial_direction[..., no_contact_point_idx] = 0.0 system.external_forces[..., :-1] += ( 0.5 * kinetic_friction_force_along_axial_direction ) system.external_forces[..., 1:] += ( 0.5 * kinetic_friction_force_along_axial_direction ) # Now rolling kinetic friction rolling_direction = _batch_cross(axial_direction, normal_plane_collection) torque_arm = -system.radius * normal_plane_collection velocity_along_rolling_direction = np.einsum( "ij ,ij ->j ", element_velocity, rolling_direction ) directors_transpose = np.einsum("ijk -> jik", system.director_collection) # w_rot = Q.T @ omega @ Q @ r rotation_velocity = _batch_matvec( directors_transpose, _batch_cross( system.omega_collection, _batch_matvec(system.director_collection, torque_arm), ), ) rotation_velocity_along_rolling_direction = np.einsum( "ij,ij->j", rotation_velocity, rolling_direction ) slip_velocity_mag_along_rolling_direction = ( velocity_along_rolling_direction + rotation_velocity_along_rolling_direction ) slip_velocity_along_rolling_direction = np.einsum( "j, ij->ij", slip_velocity_mag_along_rolling_direction, rolling_direction ) slip_velocity_sign_along_rolling_direction = np.sign( slip_velocity_mag_along_rolling_direction ) slip_function_along_rolling_direction = find_slipping_elements( slip_velocity_along_rolling_direction, self.slip_velocity_tol ) kinetic_friction_force_along_rolling_direction = -( (1.0 - slip_function_along_rolling_direction) * self.kinetic_mu_sideways * plane_response_force_mag * slip_velocity_sign_along_rolling_direction * rolling_direction ) # If rod element does not have any contact with plane, plane cannot apply friction # force on the element. Thus lets set kinetic friction force to 0.0 for the no contact points. kinetic_friction_force_along_rolling_direction[..., no_contact_point_idx] = 0.0 system.external_forces[..., :-1] += ( 0.5 * kinetic_friction_force_along_rolling_direction ) system.external_forces[..., 1:] += ( 0.5 * kinetic_friction_force_along_rolling_direction ) # torque = Q @ r @ Fr system.external_torques += _batch_matvec( system.director_collection, _batch_cross(torque_arm, kinetic_friction_force_along_rolling_direction), ) # now axial static friction nodal_total_forces = system.internal_forces + system.external_forces element_total_forces = nodes_to_elements(nodal_total_forces) force_component_along_axial_direction = np.einsum( "ij,ij->j", element_total_forces, axial_direction ) force_component_sign_along_axial_direction = np.sign( force_component_along_axial_direction ) # check top for sign convention static_mu = 0.5 * ( self.static_mu_forward * (1 + force_component_sign_along_axial_direction) + self.static_mu_backward * (1 - force_component_sign_along_axial_direction) ) max_friction_force = ( slip_function_along_axial_direction * static_mu * plane_response_force_mag ) # friction = min(mu N, pushing force) static_friction_force_along_axial_direction = -( np.minimum( np.fabs(force_component_along_axial_direction), max_friction_force ) * force_component_sign_along_axial_direction * axial_direction ) # If rod element does not have any contact with plane, plane cannot apply friction # force on the element. Thus lets set static friction force to 0.0 for the no contact points. static_friction_force_along_axial_direction[..., no_contact_point_idx] = 0.0 system.external_forces[..., :-1] += ( 0.5 * static_friction_force_along_axial_direction ) system.external_forces[..., 1:] += ( 0.5 * static_friction_force_along_axial_direction ) # now rolling static friction # there is some normal, tangent and rolling directions inconsitency from Elastica total_torques = _batch_matvec( directors_transpose, (system.internal_torques + system.external_torques) ) # Elastica has opposite defs of tangents in interaction.h and rod.cpp total_torques_along_axial_direction = np.einsum( "ij,ij->j", total_torques, axial_direction ) force_component_along_rolling_direction = np.einsum( "ij,ij->j", element_total_forces, rolling_direction ) noslip_force = -( ( system.radius * force_component_along_rolling_direction - 2.0 * total_torques_along_axial_direction ) / 3.0 / system.radius ) max_friction_force = ( slip_function_along_rolling_direction * self.static_mu_sideways * plane_response_force_mag ) noslip_force_sign = np.sign(noslip_force) static_friction_force_along_rolling_direction = ( np.minimum(np.fabs(noslip_force), max_friction_force) * noslip_force_sign * rolling_direction ) # If rod element does not have any contact with plane, plane cannot apply friction # force on the element. Thus lets set plane static friction force to 0.0 for the no contact points. static_friction_force_along_rolling_direction[..., no_contact_point_idx] = 0.0 system.external_forces[..., :-1] += ( 0.5 * static_friction_force_along_rolling_direction ) system.external_forces[..., 1:] += ( 0.5 * static_friction_force_along_rolling_direction ) system.external_torques += _batch_matvec( system.director_collection, _batch_cross(torque_arm, static_friction_force_along_rolling_direction), )
# Slender body module
[docs]@numba.njit def sum_over_elements(input): """ This function sums all elements of the input array. Using a Numba njit decorator shows better performance compared to python sum(), .sum() and np.sum() Parameters ---------- input: numpy.ndarray 1D (blocksize) array containing data with 'float' type. Returns ------- float """ """ Developer Note ----- Faster than sum(), .sum() and np.sum() For blocksize = 200 sum(): 36.9 µs ± 3.99 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) .sum(): 3.17 µs ± 90.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) np.sum(): 5.17 µs ± 364 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) This version: 513 ns ± 24.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) """ output = 0.0 for i in range(input.shape[0]): output += input[i] return output
[docs]@numba.njit def node_to_element_velocity(node_velocity): """ This function computes the velocity of the elements. Here we define a separate function because benchmark results showed that using Numba, we get almost 3 times faster calculation. Parameters ---------- node_velocity: numpy.ndarray 2D (dim, blocksize) array containing data with 'float' type. Returns ------- element_velocity: numpy.ndarray 2D (dim, blocksize) array containing data with 'float' type. """ """ Developer Note ---- Faster than pure python for blocksize 100 python: 3.81 µs ± 427 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) this version: 1.11 µs ± 19.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) """ element_velocity = 0.5 * (node_velocity[..., :-1] + node_velocity[..., 1:]) return element_velocity
[docs]@numba.njit def slender_body_forces( tangents, velocity_collection, dynamic_viscosity, lengths, radius ): r""" This function computes hydrodynamic forces on a body using slender body theory. The below implementation is from Eq. 4.13 in Gazzola et al. RSoS. (2018). .. math:: F_{h}=\frac{-4\pi\mu}{\ln{(L/r)}}\left(\mathbf{I}-\frac{1}{2}\mathbf{t}^{\textrm{T}}\mathbf{t}\right)\mathbf{v} Parameters ---------- tangents: numpy.ndarray 2D (dim, blocksize) array containing data with 'float' type. Rod-like element tangent directions. velocity_collection: numpy.ndarray 2D (dim, blocksize) array containing data with 'float' type. Rod-like object velocity collection. dynamic_viscosity: float Dynamic viscosity of the fluid. length: numpy.ndarray 1D (blocksize) array containing data with 'float' type. Rod-like object element lengths. radius: numpy.ndarray 1D (blocksize) array containing data with 'float' type. Rod-like object element radius. Returns ------- stokes_force: numpy.ndarray 2D (dim, blocksize) array containing data with 'float' type. """ """ Developer Note ---- Faster than numpy einsum implementation for blocksize 100 numpy: 39.5 µs ± 6.78 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) this version: 3.91 µs ± 310 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) """ f = np.empty((tangents.shape[0], tangents.shape[1])) total_length = sum_over_elements(lengths) element_velocity = node_to_element_velocity(velocity_collection) for k in range(tangents.shape[1]): # compute the entries of t`t. a[#][#] are the the # entries of t`t matrix a11 = tangents[0, k] * tangents[0, k] a12 = tangents[0, k] * tangents[1, k] a13 = tangents[0, k] * tangents[2, k] a21 = tangents[1, k] * tangents[0, k] a22 = tangents[1, k] * tangents[1, k] a23 = tangents[1, k] * tangents[2, k] a31 = tangents[2, k] * tangents[0, k] a32 = tangents[2, k] * tangents[1, k] a33 = tangents[2, k] * tangents[2, k] # factor = - 4*pi*mu/ln(L/r) factor = ( -4.0 * np.pi * dynamic_viscosity / np.log(total_length / radius[k]) * lengths[k] ) # Fh = factor * ((I - 0.5 * a) * v) f[0, k] = factor * ( (1.0 - 0.5 * a11) * element_velocity[0, k] + (0.0 - 0.5 * a12) * element_velocity[1, k] + (0.0 - 0.5 * a13) * element_velocity[2, k] ) f[1, k] = factor * ( (0.0 - 0.5 * a21) * element_velocity[0, k] + (1.0 - 0.5 * a22) * element_velocity[1, k] + (0.0 - 0.5 * a23) * element_velocity[2, k] ) f[2, k] = factor * ( (0.0 - 0.5 * a31) * element_velocity[0, k] + (0.0 - 0.5 * a32) * element_velocity[1, k] + (1.0 - 0.5 * a33) * element_velocity[2, k] ) return f
# slender body theory
[docs]class SlenderBodyTheory(NoForces): """ This slender body theory class is for flow-structure interaction problems. This class applies hydrodynamic forces on the body using the slender body theory given in Eq. 4.13 of Gazzola et al. RSoS (2018). Attributes ---------- dynamic_viscosity: float Dynamic viscosity of the fluid. """
[docs] def __init__(self, dynamic_viscosity): """ Parameters ---------- dynamic_viscosity : float Dynamic viscosity of the fluid. """ super(SlenderBodyTheory, self).__init__() self.dynamic_viscosity = dynamic_viscosity
[docs] def apply_forces(self, system, time=0.0): stokes_force = slender_body_forces( system.tangents, system.velocity_collection, self.dynamic_viscosity, system.lengths, system.radius, ) system.external_forces[..., :-1] += 0.5 * stokes_force system.external_forces[..., 1:] += 0.5 * stokes_force