ValueError: too many values to unpack (expected 2) in solver part

Linear Algebra
1 
from fenics import *

import matplotlib.pyplot as plt

from IPython.display import HTML, display

import time

import random

def progress(value, max=100):

  return HTML("""

    Iterations: {value} of {max}

    <progress

    value='{value}'

    max='{max}',

    style='width: 100%'

     >

    {value}

    </progress>

    """.format(value=value, max=max))

class InitialConditions(UserExpression):

  def __init__(self, **kwargs):

    random.seed(4 + MPI.rank(MPI.comm_world))

    super().__init__(**kwargs)

  def eval(self, values, x):

    values[0] = 0.00 

    values[1] = 0.00

    values[2] = 0.02*(0.5 - random.random())

    values[3] = 0.00

  def value_shape(self):

    return (4,)
class CahnHilliardEqaution(NonlinearProblem):

  def __init__(self, a, Res):

    NonlinearProblem.__init__(self)

    self.Res = Res

    self.a = a

  def F(self, b, x):

    assemble(self.Res, tensor=b)

  def J(self, A, x):

    assemble(self.a, tensor=A)

tau     = 2.5e-1 #T / n_steps #delta 

A1=0.4

mesh =  UnitSquareMesh(128,128)

FE  = FiniteElement("P", mesh.ufl_cell(), 1)    # scalar element

V   = FunctionSpace(mesh,MixedElement([FE,FE,FE,FE])) # mixed space

q    = Function(V)

dq   =TrialFunction(V)

old_q  = Function(V)

mu_phi,mu_qe,phi,qe         = split(q)

dmu_phi,dmu_qe,dphi,dqe     = split(dq)

old_mu_phi,old_mu_qe,old_phi,old_qe = split(old_q)

E = 0.5*inner(grad(phi),grad(phi))*dx + (phi*ln(phi))*dx 

Res  = derivative(E, q, dq) - inner(mu_phi,dphi)*dx - inner(mu_qe,dqe)*dx 

a = derivative(Res, q, dq)

problem = CahnHilliardEqaution(a, Res)

solver = NewtonSolver()

q_init = InitialConditions()

q.interpolate(q_init)

old_q.interpolate(q_init)

file = File("output.pvd", "compressed")

t = 0.0

T = 50*tau

while (t < T):

    t += tau

    old_q.vector()[:] = q.vector()

    z=solver.solve(problem,q.vector())

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2

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3

Thank you!.
I tried my best to minimize the code, but!!!
Also, I am working on colab.

4

I cannot reproduce your problem.

This is very unclear to me. What are you trying to compute here?
You are differentiating in the direction of dq twice, which to me looks very odd.
What is the energy functional you are trying to minimize?