# burgers.pyΒΆ

In this script we solve the Burgers equation on a 1D or 2D periodic dommain, starting from a centered Gaussian and convecting in the positive direction of the first coordinate.

 7 import nutils, numpy 

The main function defines the parameter space for the script. Configurable parameters are the mesh density (in number of elements along an edge), number of dimensions, polynomial degree, time scale, Newton tolerance and the stopping time.

 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 def main(nelems: 'number of elements' = 20, ndims: 'spatial dimension' = 1, degree: 'polynomial degree' = 1, timescale: 'time scale (timestep=timescale/nelems)' = .5, newtontol: 'solver tolerance' = 1e-5, endtime: 'end time' = numpy.inf): domain, geom = nutils.mesh.rectilinear([numpy.linspace(0,1,nelems+1)]*ndims, periodic=range(ndims)) ns = nutils.function.Namespace() ns.x = geom ns.basis = domain.basis('discont', degree=degree) ns.u = 'basis_n ?lhs_n' ns.f = '.5 u^2' ns.C = 1 res = domain.integral('-basis_n,0 f d:x' @ ns, degree=5) res += domain.interfaces.integral('-[basis_n] n_0 ({f} - .5 C [u] n_0) d:x' @ ns, degree=degree*2) inertia = domain.integral('basis_n u d:x' @ ns, degree=5) sqr = domain.integral('(u - exp(-?y_i ?y_i)(y_i = 5 (x_i - 0.5_i)))^2 d:x' @ ns, degree=5) lhs0 = nutils.solver.optimize('lhs', sqr) timestep = timescale/nelems bezier = domain.sample('bezier', 7) for itime, lhs in nutils.log.enumerate('timestep', nutils.solver.impliciteuler('lhs', res, inertia, timestep=timestep, lhs0=lhs0, newtontol=newtontol)): x, u = bezier.eval(['x_i', 'u'] @ ns, lhs=lhs) nutils.export.triplot('solution.png', x, u, tri=bezier.tri, hull=bezier.hull, clim=(0,1)) if itime * timestep >= endtime: break return lhs 

If the script is executed (as opposed to imported), nutils.cli.run() calls the main function with arguments provided from the command line. For example, to simulate until 0.5 seconds run python3 burgers.py endtime=0.5 (view log).

 52 53 if __name__ == '__main__': nutils.cli.run(main) 

Once a simulation is developed and tested, it is good practice to save a few strategicly chosen return values for routine regression testing. Here we use the standard unittest framework, with nutils.numeric.assert_allclose64() facilitating the embedding of desired results as compressed base64 data.

 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 class test(nutils.testing.TestCase): @nutils.testing.requires('matplotlib') def test_1d_p1(self): lhs = main(ndims=1, nelems=10, timescale=.1, degree=1, endtime=.01) nutils.numeric.assert_allclose64(lhs, 'eNrbocann6u3yqjTyMLUwfSw2TWzKPNM8+9mH8wyTMNNZxptMir' 'W49ffpwYAI6cOVA==') @nutils.testing.requires('matplotlib') def test_1d_p2(self): lhs = main(ndims=1, nelems=10, timescale=.1, degree=2, endtime=.01) nutils.numeric.assert_allclose64(lhs, 'eNrr0c7SrtWfrD/d4JHRE6Ofxj6mnqaKZofNDpjZmQeYB5pHmL8' 'we23mb5ZvWmjKY/LV6KPRFIMZ+o368dp92gCxZxZG') @nutils.testing.requires('matplotlib') def test_2d_p1(self): lhs = main(ndims=2, nelems=4, timescale=.1, degree=1, endtime=.01) nutils.numeric.assert_allclose64(lhs, 'eNoNyKENhEAQRuGEQsCv2SEzyQZHDbRACdsDJNsBjqBxSBxBHIg' 'J9xsqQJ1Drro1L1/eYBZceGz8njrRyacm8UQLBvPYCw1airpyUVYSJLhKijK4IC01WDnqqxvX8OTl427' 'aU73sctPGr3qqceBnRzOjo0xy9JpJR73m6R6YMZo/Q+FCLQ==')