discrete1.utils.hybrid module

Functions to be used with the collision-based hybrid method.

Allows for discretizing energy grids and material cross sections

discrete1.utils.hybrid.coarsen_external(external, edges_g, edges_gidx, weight=False)

Coarsen (… x groups) arrays to (… x groups').

Parameters:

• external (numpy.ndarray) – Array with float type of shape (…, groups). Array to coarsen.

• edges_g (numpy.ndarray) – 1D array with float type of shape (groups + 1,). Energy group bounds.

• edges_gidx (numpy.ndarray) – 1D array with int type of shape (groups' + 1,). Index of energy group bounds for new energy grid.

• weight (bool, optional) – Weighting the collapsing groups. Default is False.

Returns:

Array with float type of shape (…, groups').

Return type:

numpy.ndarray

discrete1.utils.hybrid.coarsen_materials(xs_total, xs_scatter, xs_fission, edges_g, edges_gidx)

Coarsen (materials, groups) arrays to (materials, groups').

Parameters:

• xs_total (numpy.ndarray) – 2D array with float type of shape (materials, groups). Total cross section.

• xs_scatter (numpy.ndarray) – 3D array with float type of shape (materials, groups, groups). Scatter cross section.

• xs_fission (numpy.ndarray) – 3D array with float type of shape (materials, groups, groups). Fission cross section.

• edges_g (numpy.ndarray) – 1D array with float type of shape (groups + 1,). Energy group bounds.

• edges_gidx (numpy.ndarray) – 1D array with int type of shape (groups' + 1,). Index of energy group bounds for new energy grid.

Returns:

• coarse_total (numpy.ndarray) – 2D array with float type of shape (materials, groups'). Coarsened total cross section.

• coarse_scatter (numpy.ndarray) – 3D array with float type of shape (materials, groups', groups'). Coarsened scatter cross section.

• coarse_fission (numpy.ndarray) – 3D array with float type of shape (materials, groups', groups'). Coarsened fission cross section.

discrete1.utils.hybrid.coarsen_velocity(vector, edges_gidx)

Coarsen (groups,) vector to (groups',) where groups > groups'.

Parameters:

• vector (numpy.ndarray) – 1D array with float type of shape (groups,). Array to coarsen.

• edges_gidx (numpy.ndarray) – 1D array with int type of shape (groups' + 1,). Index of energy group bounds for new energy grid

Returns:

1D array with float type of shape (groups',).

Return type:

numpy.npdarray

discrete1.utils.hybrid.collided_index(groups_fine, edges_gidx)

Calculate which coarse group a fine energy group is a part of.

This is done through:

fine grid  :    |---g1---|--g2--|---g3---|--g4--|
coarse grid:    |-------g1------|-------g2------|

results in coarse_idx = [0, 0, 1, 1]

Parameters:

• groups_fine (int) – Number of fine energy groups.

• edges_gidx (numpy.ndarray) – 1D array with int type of shape (groups_coarse + 1,). Index of energy group bounds for coarse energy grid.

Returns:

1D array with int type of shape (groups_fine,).

Return type:

numpy.ndarray

discrete1.utils.hybrid.energy_coarse_index(fine, coarse)

Get the indices for resizing matrices.

Parameters:

• fine (int) – Larger energy group size.

• coarse (int) – Coarser energy group size.

Return type:

numpy.ndarray, int, shape (coarse + 1,)

discrete1.utils.hybrid.energy_grid_change(starting_grid, groups_u, groups_c)

Calculate the variables needed for refining and coarsening fluxes in vhybrid.

Parameters:

• starting_grid (int) – Value of starting energy grid, could be 87, 361, 618.

• groups_u (int) – Number of uncollided energy groups.

• groups_c (int) – Number of collided energy groups.

Returns:

• coarse_idx (numpy.ndarray) – 1D array with int type of shape (groups_fine,). Coarse group mapping.

• factor (numpy.ndarray) – 1D array with float type of shape (groups_fine,). Fine energy bin width / coarse energy bin width for specific location.

• edges_gidx_c (numpy.ndarray) – 1D array with int type of shape (groups_c + 1,). Coarse group index mapping.

• edges_g (numpy.ndarray) – 1D array with float type of shape (groups_u + 1,). Energy bound locations.

discrete1.utils.hybrid.hybrid_factor(delta_fine, delta_coarse, edges_gidx)

Calculate the fine energy bin width per coarse energy bin width.

This is done through

location (eV):    0        3      5        8      10
fine grid    :    |---g1---|--g2--|---g3---|--g4--|
coarse grid  :    |-------g1------|-------g2------|

results in factor = [3/5, 2/5, 3/5, 2/5] with widths [3, 2, 2, 3] for the fine groups and [5, 5] for the coarse groups

Parameters:

• delta_fine (numpy.ndarray) – 1D array with float type of shape (groups_fine,). Energy group width for fine grid.

• delta_coarse (numpy.ndarray) – 1D array with float type of shape (groups_fine,). Energy group width for coarse grid.

• edges_gidx (numpy.ndarray) – 1D array with int type of shape (groups_coarse + 1,). Index of energy group bounds for coarse energy grid.

Returns:

1D array of float type with shape (groups_fine,). Fine energy bin width / coarse energy bin width for specific location.

Return type:

numpy.ndarray

discrete1.utils.hybrid.indexing(edges_g, edges_gidx_fine, edges_gidx_coarse)

Calculate the variables needed for refining and coarsening fluxes.

Parameters:

• edges_g (numpy.ndarray) – 1D array with float type of shape (groups_fine + 1,). Energy group bounds for fine grid.

• edges_gidx_fine (numpy.ndarray) – 1D array with int type of shape (groups_fine + 1,). Index of energy group bounds for fine energy grid.

• edges_gidx_coarse (numpy.ndarray) – 1D array with int type of shape (groups_coarse + 1,) Index of energy group bounds for coarse energy grid.

Returns:

• coarse_idx (numpy.ndarray) – 1D array with int type of shape (groups_fine,). Coarse group mapping.

• fine_idx (numpy.ndarray) – 1D array with int type of shape (groups_coarse + 1,). Location of edges between the coarse and fine energy grids.

• factor (numpy.ndarray) – 1D array with float type of shape (groups_fine,). Fine energy bin width / coarse energy bin width for specific location.

discrete1.utils.hybrid.uncollided_index(groups_coarse, edges_gidx)

Calculate the location of edges between the coarse and fine energy grids.

This is done through

edge       :    0        1      2        3      4
fine grid  :    |---g1---|--g2--|---g3---|--g4--|
coarse grid:    |-------g1------|-------g2------|

results in fine_idx = [0, 2, 4]

Parameters:

• groups_coarse (int) – Number of coarse energy groups where groups_fine >= groups_coarse.

• edges_gidx (numpy.ndarray) – 1D array with int type of shape (groups_coarse + 1,). Index of energy group bounds for coarse energy grid.

Returns:

1D array with int type of shape (groups_coarse + 1,). Location of edges between the coarse and fine energy grids.

Return type:

numpy.ndarray