# finitestrain.pyΒΆ

In this script we solve the nonlinear Saint Venant-Kichhoff problem on a unit square domain (optionally with a circular cutout), clamped at both the left and right boundary in such a way that an arc is formed over a specified angle. The configuration is constructed such that a symmetric solution is expected.

  9 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 from nutils import mesh, function, solver, export, cli, testing import numpy def main(nelems:int, etype:str, btype:str, degree:int, poisson:float, angle:float, restol:float, trim:bool): ''' Deformed hyperelastic plate. .. arguments:: nelems [10] Number of elements along edge. etype [square] Type of elements (square/triangle/mixed). btype [std] Type of basis function (std/spline). degree [1] Polynomial degree. poisson [.25] Poisson's ratio, nonnegative and stricly smaller than 1/2. angle [20] Rotation angle for right clamp (degrees). restol [1e-10] Newton tolerance. trim [no] Create circular-shaped hole. ''' domain, geom = mesh.unitsquare(nelems, etype) if trim: domain = domain.trim(function.norm2(geom-.5)-.2, maxrefine=2) bezier = domain.sample('bezier', 5) ns = function.Namespace() ns.x = geom ns.angle = angle * numpy.pi / 180 ns.lmbda = 2 * poisson ns.mu = 1 - 2 * poisson ns.ubasis = domain.basis(btype, degree=degree).vector(2) ns.u_i = 'ubasis_ki ?lhs_k' ns.X_i = 'x_i + u_i' ns.strain_ij = '.5 (u_i,j + u_j,i)' ns.energy = 'lmbda strain_ii strain_jj + 2 mu strain_ij strain_ij' sqr = domain.boundary['left'].integral('u_k u_k d:x' @ ns, degree=degree*2) sqr += domain.boundary['right'].integral('((u_0 - x_1 sin(2 angle) - cos(angle) + 1)^2 + (u_1 - x_1 (cos(2 angle) - 1) + sin(angle))^2) d:x' @ ns, degree=degree*2) cons = solver.optimize('lhs', sqr, droptol=1e-15) energy = domain.integral('energy d:x' @ ns, degree=degree*2) lhs0 = solver.optimize('lhs', energy, constrain=cons) X, energy = bezier.eval(['X_i', 'energy'] @ ns, lhs=lhs0) export.triplot('linear.png', X, energy, tri=bezier.tri, hull=bezier.hull) ns.strain_ij = '.5 (u_i,j + u_j,i + u_k,i u_k,j)' ns.energy = 'lmbda strain_ii strain_jj + 2 mu strain_ij strain_ij' energy = domain.integral('energy d:x' @ ns, degree=degree*2) lhs1 = solver.minimize('lhs', energy, lhs0=lhs0, constrain=cons).solve(restol) X, energy = bezier.eval(['X_i', 'energy'] @ ns, lhs=lhs1) export.triplot('nonlinear.png', X, energy, tri=bezier.tri, hull=bezier.hull) return lhs0, lhs1 

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 finitestrain.py (view log). To select quadratic splines and a cutout add python3 finitestrain.py btype=spline degree=2 trim=yes (view log).

 77 78 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.

  86 87 88 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 class test(testing.TestCase): @testing.requires('matplotlib') def test_default(self): lhs0, lhs1 = main(nelems=4, etype='square', btype='std', degree=1, poisson=.25, angle=10, restol=1e-10, trim=False) with self.subTest('linear'): self.assertAlmostEqual64(lhs0, ''' eNpjYMAE5ZeSL/HqJ146YeB4cbvhl/PzjPrOcVy8da7b4Og5W6Osc/rGt88+MvY+u+yC7NlcQ+GzEsYP z/w3nn1mvon7mdsXJM8oG304vdH45Oluk2WnlU1bTgMAv04qwA==''') with self.subTest('non-linear'): self.assertAlmostEqual64(lhs1, ''' eNpjYMAEZdrKl2/p37soY1h84aKh2/lmI4Zz7loq5y0MD55rNtI652Rcefa48aUzzZcjzj4ylDjrYnz6 jIBJ8Zl2E9Yzty9InlE2+nB6o/HJ090my04rm7acBgAKcSdV''') @testing.requires('matplotlib') def test_mixed(self): lhs0, lhs1 = main(nelems=4, etype='mixed', btype='std', degree=1, poisson=.25, angle=10, restol=1e-10, trim=False) with self.subTest('linear'): self.assertAlmostEqual64(lhs0, ''' eNpjYICAqxfbL+Xov7kIYi80OA+mtxleOA+iVxjNPBdncOdc6sXT51yNgs8ZGX89e8/Y66zqBaOz/Ya8 Z4WMX575ZTz5zAqTgDPKRh9O374geWaj8cnT3SbLTiubtpwGAJ6hLHk=''') with self.subTest('non-linear'): self.assertAlmostEqual64(lhs1, ''' eNpjYIAA7fv2l6UMEi6C2H8N7l0A0VcMzc+D6H4jznPyhpfOdelwnm80EjznYTz57CnjG2eWX0o/+9VQ +KyT8cUzzCbZZ2abiJ9RNvpw+vYFyTMbjU+e7jZZdlrZtOU0AJN4KHY=''') @testing.requires('matplotlib') def test_spline(self): lhs0, lhs1 = main(nelems=4, etype='square', btype='spline', degree=2, poisson=.25, angle=10, restol=1e-10, trim=False) with self.subTest('linear'): self.assertAlmostEqual64(lhs0, ''' eNpjYMAOrl3J0vmixaY7QS9N545+w9VaA5eLXYZp51MvVl/I1F164YeBxAVlI//zzMZB52KN35+dd+H9 2Vd6b85yGx0/a22cd/aXMetZH5PTZ7ZfaDmzTL/nzFGj3DPPje3OLDBhPvPC5N7p2xckz/gZsJwRML5z Wstk++m7JlNPK5u2nAYATqg9sA==''') with self.subTest('non-linear'): self.assertAlmostEqual64(lhs1, ''' eNpjYMAOnLUP6ejq9ukI67vflTVQvdRt0H8h3fDBOT7trReK9adeyDFcez7YaN+5X0Z7z7oYB5/9rKx9 ztdA6Fyq0dqzScbGZ78bLzmja5J8RvzSrjN9BgvOfDFKP/PTWOfMSpO3p8+YbDx9+4LkGT8DljMCxndO a5lsP33XZOppZdOW0wApLzra''')