1D Poisson example

[pe_rasouliyahoo_com]

My question might be very trivial but I am looking for a quick solution. I need to solve 1D Poisson equation. I tried to use following examples: d_Poisson_2D_f.f90 and  d_Poisson_3D_f.f90 by simply reducing the mesh (nx=4, ny=1 and nx=4, ny=1, nz=1) in the examples but in both case it fails to converge by giving the following error:


d_Poisson_2D_f.f90 error:

 =======================================================================

 The number of mesh intervals in x-direction is nx=6
 The number of mesh intervals in y-direction is ny=1

 In the mesh point (0.167,0.000) the error between the computed and the true solution is equal to -0.272E+00
 In the mesh point (0.167,1.000) the error between the computed and the true solution is equal to  0.272E+00

 The computed solution seems to be inaccurate.
 Double precision 2D Poisson example FAILED to compute the solution...


and d_Poisson_3D_f.f90 error:

------------------------------------------------------------------------------
MKL TRIG TRANSFORMS ERROR:
The dimension of the trigonometric transform 1 should be
an integer number greater or equal to 2.

 -------------------------------------------------------------------------------
MKL POISSON LIBRARY ERROR:
Fatal error: Trigonometric Transform commit step has failed to complete.
Error code = 1. Computations has been stopped...
 Double precision 3D Poisson example FAILED to compute the solution...


Is there any quick resolution for it or is there a 1D Poisson fortran example out there?

thanks

[Alexander_K_Intel2]

Hi

If you want to get solution of 1D Poisson solver of size N and rhs with element f(k) you need to made next steps:

1. Set Nz = N, Nx and Ny any number greater or equal to 2. 

2. Set zero Neumann condition in both x and y dimension

3. Set rhs(i,j,k) = f(k)

In such case you get Nx*Ny solution of 1D Poisson equation (x(k) = solution(i,j,k) independently of i and j).

But I'm strongly recommend you to use LAPACK functionality to solve such equation

With best regards,

Alexander Kalinkin