Thanks Barry for your answer !
I guess my concern is of the second type:
By grid metric terms I meant essentially grid spacings which look like:
dx(i,j), dy(i,j) and dz(k) where (i,j,k) are indices running along the 3
dimensions of the grid.
Storing dx(i,j) into a 3D array seemed like a bit waste of memory to me
but I must
be wrong. The elliptic problem I am solving for is close to a poisson
I guess I can at least store dx and dy into a single dxy 3D array.
Le 16/09/16 à 19:43, Barry Smith a écrit :
On Sep 16, 2016, at 9:29 AM, Aurelien PONTE <aurelien.po...@ifremer.fr> wrote:
I've started using petsc4py in order to solve a 3D problem (inversion of
I would like to store 2D metric terms describing the grid
What do you mean by 2D metric terms describing the grid?
Do you want to store something like a little dense 2d array for each grid
point? If so create another 3D DA with a dof = the product of the dimensions of
the little dense 2d array and then store the little dense 2d arrays at in a
global vector obtained from that DA.
Or is the grid uniform in one dimension and not uniform in the other two and hence you want to
store the information about the non-uniformness in only a 2d array so as to not "waste"
the redundant information in the third direction? Then I recommend just "waste" the
redundant information in the third dimension; it is trivial compared to all the data you need to
solve the problem.
Or do you mean something else?
I am working on but don't know
how to do that given my domain is tiled in 3D directions:
self.da = PETSc.DMDA().create([self.grid.Nx, self.grid.Ny, self.grid.Nz],
I create my 3D vectors with, for example:
self.Q = self.da.createGlobalVec()
What am I supposed to do for a 2D vector?
Is it a bad idea?
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