pyro.advection_fv4 package#
The pyro fourth-order accurate advection solver. This implements a the method of McCorquodale and Colella (2011), with 4th order accurate spatial reconstruction together with 4th order Runge-Kutta time integration.
Subpackages#
Submodules#
pyro.advection_fv4.fluxes module#
- pyro.advection_fv4.fluxes.fluxes(my_data, rp)[source]#
Construct the fluxes through the interfaces for the linear advection equation:
\[a_t + u a_x + v a_y = 0\]We use a fourth-order Godunov method to construct the interface states, using Runge-Kutta integration. Since this is 4th-order, we need to be aware of the difference between a face-average and face-center for the fluxes.
In the pure advection case, there is no Riemann problem we need to solve – we just simply do upwinding. So there is only one ‘state’ at each interface, and the zone the information comes from depends on the sign of the velocity.
Our convection is that the fluxes are going to be defined on the left edge of the computational zones:
| | | | | | | | -+------+------+------+------+------+------+-- | i-1 | i | i+1 | a_l,i a_r,i a_l,i+1
a_r,i and a_l,i+1 are computed using the information in zone i,j.
- Parameters:
- my_dataFV object
The data object containing the grid and advective scalar that we are advecting.
- rpRuntimeParameters object
The runtime parameters for the simulation
- Returns:
- outndarray, ndarray
The fluxes averaged over the x and y faces
pyro.advection_fv4.interface module#
pyro.advection_fv4.simulation module#
- class pyro.advection_fv4.simulation.Simulation(solver_name, problem_name, problem_func, rp, *, problem_finalize_func=None, problem_source_func=None, timers=None, data_class=<class 'pyro.mesh.patch.CellCenterData2d'>)[source]#
Bases:
Simulation- initialize()[source]#
Initialize the grid and variables for advection and set the initial conditions for the chosen problem.