examples.convection.exponential2D.cylindricalMesh2DNonUniformΒΆ
This example solves the steady-state cylindrical convection-diffusion equation given by:
with coefficients \(D = 1\) and \(\vec{u} = (10,)\), or
>>> diffCoeff = 1.
>>> convCoeff = ((10.,), (0.,))
We define a 2D cylindrical mesh representing an annulus. The mesh is a
pseudo-1D mesh, but is a good test case for the
CylindricalGrid2D()
mesh. The mesh
has a non-constant cell spacing.
>>> from fipy import CellVariable, CylindricalGrid2D, DiffusionTerm, ExponentialConvectionTerm, Viewer
>>> from fipy.tools import numerix
>>> r0 = 1.
>>> r1 = 2.
>>> nr = 100
>>> Rratio = (r1 / r0)**(1 / float(nr))
>>> dr = r0 * (Rratio - 1) * Rratio**numerix.arange(nr)
>>> mesh = CylindricalGrid2D(dr=dr, dz=1., nz=1) + ((r0,), (0.,))
The solution variable is initialized to valueLeft
:
>>> valueLeft = 0.
>>> valueRight = 1.
>>> var = CellVariable(mesh=mesh, name = "variable")
and impose the boundary conditions
with
>>> var.constrain(valueLeft, mesh.facesLeft)
>>> var.constrain(valueRight, mesh.facesRight)
The equation is created with the DiffusionTerm
and
ExponentialConvectionTerm
.
>>> eq = (DiffusionTerm(coeff=diffCoeff)
... + ExponentialConvectionTerm(coeff=convCoeff))
More details of the benefits and drawbacks of each type of convection
term can be found in Numerical Schemes.
Essentially, the ExponentialConvectionTerm
and PowerLawConvectionTerm
will
both handle most types of convection-diffusion cases, with the
PowerLawConvectionTerm
being more efficient.
We solve the equation
>>> eq.solve(var=var)
and test the solution against the analytical result
>>> axis = 0
>>> try:
... from scipy.special import expi
... r = mesh.cellCenters[axis]
... U = convCoeff[0][0]
... AA = numerix.exp(U / diffCoeff * (r1 - r))
... BB = expi(U * r0 / diffCoeff) - expi(U * r / diffCoeff)
... CC = expi(U * r0 / diffCoeff) - expi(U * r1 / diffCoeff)
... analyticalArray = AA * BB / CC
... except ImportError:
... print("The SciPy library is unavailable. It is required for testing purposes.")
>>> print(var.allclose(analyticalArray, atol=1e-3))
1
If the problem is run interactively, we can view the result:
>>> if __name__ == '__main__':
... viewer = Viewer(vars=var)
... viewer.plot()