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bossak_scheme.py
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bossak_scheme.py
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# ----------------------------------------------------------------------
# author : Martin Ruchti
# contact : [email protected]
# ----------------------------------------------------------------------
from __future__ import print_function, absolute_import, division
from numpy import *
from pyKratos import *
'''
The implementation of this scheme follows the implementation of the residual based predictor corrector Bossak scheme
from Kratos Multiphysics.
The Bossak scheme is a special version of the generalized alpha scheme (alpha_f = 0)
'''
class BossakScheme:
def __init__(self, model_part, alpha):
self.model_part = model_part
self.alphaBossak = alpha
self.gammaNewmark = 0.5 - self.alphaBossak
self.betaNewmark = 0.25 * ( ( 1.0 - self.alphaBossak )**2 )
#init parameters with 0.0
self.ma0 = 0.0
self.ma1 = 0.0
self.ma2 = 0.0
self.ma3 = 0.0
self.ma4 = 0.0
self.ma5 = 0.0
self.mam = 0.0
def CalculateLocalSystem(self, elem):
return elem.CalculateLocalSystem(self.model_part.ProcessInfo)
def CalculateLocalAdjointSystem(self, elem, dragForceDirection, stepRelative):
if(stepRelative == 0):
return elem.CalculateInitialLocalAdjointSystem(dragForceDirection, self.model_part.ProcessInfo)
else:
return elem.CalculateLocalAdjointSystem(dragForceDirection, self.model_part.ProcessInfo, stepRelative)
def CalculateLocalAdjointDOTSystem(self, elem, dragForceDirection, stepRelative):
if(stepRelative == 0):
return elem.CalculateInitialLocalAdjointDOTSystem(dragForceDirection, self.model_part.ProcessInfo)
else:
return elem.CalculateLocalAdjointDOTSystem(dragForceDirection, self.model_part.ProcessInfo, stepRelative)
def EquationId(self, elem):
return elem.EquationId()
#compute the integration coefficients
def ComputeCoefficients(self):
#retrieve current time step from process info
dt = self.model_part.ProcessInfo[DELTA_TIME]
#check that dt is not 0, because this would cause the inertia terms to
#be inf.
if(dt == 0):
raise Exception("Dt can not be zero!!")
#calculate coefficients for the newmark/alpha scheme
self.ma0 = 1.0 / (self.gammaNewmark * dt);
#self.ma1 = dt * self.betaNewmark / self.gammaNewmark;
self.ma2 = (-1.0 + self.gammaNewmark) / self.gammaNewmark;
self.ma3 = dt;
self.ma4 = dt**2 *(-2.0 * self.betaNewmark + 1.0) / 2.0;
self.ma5 = dt**2 * self.betaNewmark;
#self.mam = (1.0 - self.alphaBossak) / (self.gammaNewmark * dt);
#write coefficients to the process info to be accessed within the elements
self.model_part.ProcessInfo.update( {BOSSAK_COEFFICIENTS: array( [self.alphaBossak,self.gammaNewmark] ) } )
def Predict(self):
self.ComputeCoefficients()
#if nodes are not fixed, set the new value to the old one
#this is the starting point for the iteration process
for node in self.model_part.NodeIterators():
if node.IsFixed(VELOCITY_X)==False:
node.SetSolutionStepValue(VELOCITY_X,0,node.GetSolutionStepValue(VELOCITY_X,1))
if node.IsFixed(VELOCITY_Y)==False:
node.SetSolutionStepValue(VELOCITY_Y,0,node.GetSolutionStepValue(VELOCITY_Y,1))
if node.IsFixed(PRESSURE)==False:
node.SetSolutionStepValue(PRESSURE,0,node.GetSolutionStepValue(PRESSURE,1))
#update displacement and accceleration
self.UpdateAcceleration(self.model_part.NodeIterators())
self.UpdateDisplacements(self.model_part.NodeIterators())
def UpdateDisplacements(self,nodes):
for node in nodes:
if(node.GetSolutionStepValue(IS_LAGRANGIAN,0) == True):
#update component x of displacement
dn1 = node.GetSolutionStepValue(DISPLACEMENT_X,1)
an0 = node.GetSolutionStepValue(ACCELERATION_X,0)
an1 = node.GetSolutionStepValue(ACCELERATION_X,1)
vn1 = node.GetSolutionStepValue(VELOCITY_X,1)
dn0 = dn1 + self.ma3 * vn1 + self.ma4 * an1 + self.ma5 * an0
node.SetSolutionStepValue(DISPLACEMENT_X,0,dn0)
#update component y of displacement
dn1 = node.GetSolutionStepValue(DISPLACEMENT_Y,1)
an0 = node.GetSolutionStepValue(ACCELERATION_Y,0)
an1 = node.GetSolutionStepValue(ACCELERATION_Y,1)
vn1 = node.GetSolutionStepValue(VELOCITY_Y,1)
dn0 = dn1 + self.ma3 * vn1 + self.ma4 * an1 + self.ma5 * an0
node.SetSolutionStepValue(DISPLACEMENT_Y,0,dn0)
def UpdateAcceleration(self,nodes):
for node in nodes:
#predict acceleration_x
if node.IsFixed(ACCELERATION_X)==False:
aOld = node.GetSolutionStepValue(ACCELERATION_X,1)
deltaVel = node.GetSolutionStepValue(VELOCITY_X,0) - node.GetSolutionStepValue(VELOCITY_X,1)
aNewX = self.ma0 * deltaVel + self.ma2*aOld
node.SetSolutionStepValue(ACCELERATION_X, 0, aNewX)
#predict acceleration_y
if node.IsFixed(ACCELERATION_Y)==False:
aOld = node.GetSolutionStepValue(ACCELERATION_Y,1)
deltaVel = node.GetSolutionStepValue(VELOCITY_Y,0) - node.GetSolutionStepValue(VELOCITY_Y,1)
aNewY = self.ma0 * deltaVel + self.ma2*aOld
node.SetSolutionStepValue(ACCELERATION_Y, 0, aNewY)
def Update(self, dofset, dx):
# here we do x = old_value + dx
#this is expected to update VELOCITY (and eventually other quantities)
for dof, value in zip(dofset, dx):
if dof.IsFixed() == False:
old_value = dof.GetValue()
dof.SetValue(old_value + value)
#update the dependent values
self.UpdateDisplacements(self.model_part.NodeIterators())
self.UpdateAcceleration(self.model_part.NodeIterators())