# platewithhole.py¶

In this script we solve the linear plane strain elasticity problem for an infinite plate with a circular hole under tension. We do this by placing the circle in the origin of a unit square, imposing symmetry conditions on the left and bottom, and Dirichlet conditions constraining the displacements to the analytical solution to the right and top. The traction-free circle is removed by means of the Finite Cell Method (FCM).

 10 11 12 13 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 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 from nutils import mesh, function, solver, export, cli, testing import numpy, treelog def main(nelems:int, etype:str, btype:str, degree:int, traction:float, maxrefine:int, radius:float, poisson:float): ''' Horizontally loaded linear elastic plate with FCM hole. .. arguments:: nelems [9] Number of elements along edge. etype [square] Type of elements (square/triangle/mixed). btype [std] Type of basis function (std/spline), with availability depending on the selected element type. degree [2] Polynomial degree. traction [.1] Far field traction (relative to Young's modulus). maxrefine [2] Number or refinement levels used for the finite cell method. radius [.5] Cut-out radius. poisson [.3] Poisson's ratio, nonnegative and strictly smaller than 1/2. ''' domain0, geom = mesh.unitsquare(nelems, etype) domain = domain0.trim(function.norm2(geom) - radius, maxrefine=maxrefine) ns = function.Namespace() ns.x = geom ns.lmbda = 2 * poisson ns.mu = 1 - poisson ns.ubasis = domain.basis(btype, degree=degree).vector(2) ns.u_i = 'ubasis_ni ?lhs_n' ns.X_i = 'x_i + u_i' ns.strain_ij = '(u_i,j + u_j,i) / 2' ns.stress_ij = 'lmbda strain_kk δ_ij + 2 mu strain_ij' ns.r2 = 'x_k x_k' ns.R2 = radius**2 / ns.r2 ns.k = (3-poisson) / (1+poisson) # plane stress parameter ns.scale = traction * (1+poisson) / 2 ns.uexact_i = 'scale (x_i ((k + 1) (0.5 + R2) + (1 - R2) R2 (x_0^2 - 3 x_1^2) / r2) - 2 δ_i1 x_1 (1 + (k - 1 + R2) R2))' ns.du_i = 'u_i - uexact_i' sqr = domain.boundary['left,bottom'].integral('(u_i n_i)^2 d:x' @ ns, degree=degree*2) cons = solver.optimize('lhs', sqr, droptol=1e-15) sqr = domain.boundary['top,right'].integral('du_k du_k d:x' @ ns, degree=20) cons = solver.optimize('lhs', sqr, droptol=1e-15, constrain=cons) res = domain.integral('ubasis_ni,j stress_ij d:x' @ ns, degree=degree*2) lhs = solver.solve_linear('lhs', res, constrain=cons) bezier = domain.sample('bezier', 5) X, stressxx = bezier.eval(['X_i', 'stress_00'] @ ns, lhs=lhs) export.triplot('stressxx.png', X, stressxx, tri=bezier.tri, hull=bezier.hull) err = domain.integral('_n d:x' @ ns, degree=max(degree,3)*2).eval(lhs=lhs)**.5 treelog.user('errors: L2={:.2e}, H1={:.2e}'.format(*err)) return err, cons, 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 keep with the default arguments simply run python3 platewithhole.py (view log). To select mixed elements and quadratic basis functions add python3 platewithhole.py etype=mixed degree=2 (view log).

 80 81 if __name__ == '__main__': cli.run(main) 

Once a simulation is developed and tested, it is good practice to save a few strategic return values for regression testing. The nutils.testing module, which builds on the standard unittest framework, facilitates this by providing nutils.testing.TestCase.assertAlmostEqual64() for the embedding of desired results as compressed base64 data.

  89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 class test(testing.TestCase): @testing.requires('matplotlib') def test_spline(self): err, cons, lhs = main(nelems=4, etype='square', btype='spline', degree=2, traction=.1, maxrefine=2, radius=.5, poisson=.3) with self.subTest('l2-error'): self.assertAlmostEqual(err[0], .00033, places=5) with self.subTest('h1-error'): self.assertAlmostEqual(err[1], .00672, places=5) with self.subTest('constraints'): self.assertAlmostEqual64(cons, ''' eNpjaGBoYGBAxvrnGBow4X89g3NQFSjQwLAGq7i10Wus4k+NfM8fNWZgOGL89upc47WX0ozvXjAzPn1e 1TjnPACrACoJ''') with self.subTest('left-hand side'): self.assertAlmostEqual64(lhs, ''' eNpbZHbajIHhxzkGBhMgtgdi/XPypyRPvjFxO/PccPq5Vn2vcxr6luf+6xmcm2LMwLDQePf5c0bTzx8x 5D7vaTjnnIFhzbmlQPH5xhV39Y3vXlxtJHoh2EjvvLXR63MbgOIbjRdfrTXeecnUeO+Fn0Yrzj818j1/ FCh+xPjt1bnGay+lGd+9YGZ8+ryqcc55AK+AP/0=''') @testing.requires('matplotlib') def test_mixed(self): err, cons, lhs = main(nelems=4, etype='mixed', btype='std', degree=2, traction=.1, maxrefine=2, radius=.5, poisson=.3) with self.subTest('l2-error'): self.assertAlmostEqual(err[0], .00024, places=5) with self.subTest('h1-error'): self.assertAlmostEqual(err[1], .00739, places=5) with self.subTest('constraints'): self.assertAlmostEqual64(cons, ''' eNpjaGDADhlwiOEU1z8HZusbgukkg5BzRJqKFRoa1oD1HzfceA5NH9FmgKC10SuwOdONpM7DxDYa77gM MueoMQPDEePzV2Hic42XXmoynnQRxvc3dryQbnz3Aoj91Mj3vJnx6fOqxjnnAQzkV94=''') with self.subTest('left-hand side'): self.assertAlmostEqual64(lhs, ''' eNoNzE8og3EcBvC3uUo5rNUOnBSK9/19n0Ic0Eo5oJBmRxcaB04kUnPgoETmT2w7LVrtMBy4auMw+35/ 7/vaykFSFEopKTnIe/jU01PPU6FNWcQIn+Or5CBfSqCGD1uDYhi7/KbW+dma5aK65gX6Y8Po8HSzZQ7y vBniHyvFV9aq17V7TK42O9kwFS9YUzxhjXIcZxLCnIzjTsfxah/BMFJotjUlZYz6xYeoPqEPKaigbKhb 9lOj9NGa9KgtVmqJH9UT36gcp71dEr6HaVS5GS8f46AcQ9itx739SQXdBL8dRqeTo1odox35poh2yJVh apEueucsRWWPgpJFoLKPNzeHC/fU+yl48pDyMi6dCFbsBNJODNu2iawOoE4PoVdP4kH/UkZeaEDaUJQG zMg/DouRUg==''')