Source code for advection_weno.simulation

from __future__ import print_function

import advection
import advection_weno.fluxes as flx
import mesh.integration as integration
import mesh.array_indexer as ai

[docs]class Simulation(advection.Simulation):
[docs] def substep(self, myd): """ take a single substep in the RK timestepping starting with the conservative state defined as part of myd """ myg = myd.grid k = myg.scratch_array() flux_x, flux_y = flx.fluxes(myd, self.rp, self.dt) F_x = ai.ArrayIndexer(d=flux_x, grid=myg) F_y = ai.ArrayIndexer(d=flux_y, grid=myg) k.v()[:, :] = \ (F_x.v() - F_x.ip(1))/myg.dx + \ (F_y.v() - return k
[docs] def method_compute_timestep(self): """ Compute the advective timestep (CFL) constraint. We use the driver.cfl parameter to control what fraction of the CFL step we actually take. """ cfl = self.rp.get_param("driver.cfl") u = self.rp.get_param("advection.u") v = self.rp.get_param("advection.v") # the timestep is 1/sum{|U|/dx} xtmp = max(abs(u), self.SMALL)/self.cc_data.grid.dx ytmp = max(abs(v), self.SMALL)/self.cc_data.grid.dy self.dt = cfl/(xtmp + ytmp)
[docs] def evolve(self): """ Evolve the linear advection equation through one timestep. We only consider the "density" variable in the CellCenterData2d object that is part of the Simulation. """ tm_evolve ="evolve") tm_evolve.begin() myd = self.cc_data method = self.rp.get_param("advection.temporal_method") rk = integration.RKIntegrator(myd.t, self.dt, method=method) rk.set_start(myd) for s in range(rk.nstages()): ytmp = rk.get_stage_start(s) ytmp.fill_BC_all() k = self.substep(ytmp) rk.store_increment(s, k) rk.compute_final_update() if self.particles is not None: myg = self.cc_data.grid u = self.rp.get_param("advection.u") v = self.rp.get_param("advection.v") u2d = myg.scratch_array() + u v2d = myg.scratch_array() + v self.particles.update_particles(self.dt, u2d, v2d) # increment the time myd.t += self.dt self.n += 1 tm_evolve.end()