为equatiohn/pde/linear_elasticity.py添加单元测试

test/equation/test_linear_elasticity.py

@@ -0,0 +1,322 @@
+import paddle
+import pytest
+from paddle import nn
+
+from ppsci import equation
+
+
+def jacobian(y: paddle.Tensor, x: paddle.Tensor) -> paddle.Tensor:
+    return paddle.grad(y, x, create_graph=True)[0]
+
+
+def hessian(y: paddle.Tensor, x: paddle.Tensor) -> paddle.Tensor:
+    return jacobian(jacobian(y, x), x)
+
+
+def stress_disp_xx_expected_result(u, v, w, x, y, z, lambda_, mu, dim, sigma_xx):
+    stress_disp_xx = (
+        lambda_ * (jacobian(u, x) + jacobian(v, y)) + 2 * mu * jacobian(u, x) - sigma_xx
+    )
+    if dim == 3:
+        stress_disp_xx += lambda_ * jacobian(w, z)
+    return stress_disp_xx
+
+
+def stress_disp_yy_expected_result(u, v, w, x, y, z, lambda_, mu, dim, sigma_yy):
+    stress_disp_yy = (
+        lambda_ * (jacobian(u, x) + jacobian(v, y)) + 2 * mu * jacobian(v, y) - sigma_yy
+    )
+    if dim == 3:
+        stress_disp_yy += lambda_ * jacobian(w, z)
+    return stress_disp_yy
+
+
+def stress_disp_zz_expected_result(u, v, w, x, y, z, lambda_, mu, sigma_zz):
+    stress_disp_zz = (
+        lambda_ * (jacobian(u, x) + jacobian(v, y) + jacobian(w, z))
+        + 2 * mu * jacobian(w, z)
+        - sigma_zz
+    )
+    return stress_disp_zz
+
+
+def stress_disp_xy_expected_result(u, v, x, y, mu, sigma_xy):
+    stress_disp_xy = mu * (jacobian(u, y) + jacobian(v, x)) - sigma_xy
+    return stress_disp_xy
+
+
+def stress_disp_xz_expected_result(u, w, x, z, mu, sigma_xz):
+    stress_disp_xz = mu * (jacobian(u, z) + jacobian(w, x)) - sigma_xz
+    return stress_disp_xz
+
+
+def stress_disp_yz_expected_result(v, w, y, z, mu, sigma_yz):
+    stress_disp_yz = mu * (jacobian(v, z) + jacobian(w, y)) - sigma_yz
+    return stress_disp_yz
+
+
+def equilibrium_x_expected_result(
+    u, x, y, z, t, rho, dim, time, sigma_xx, sigma_xy, sigma_xz=None
+):
+    equilibrium_x = -jacobian(sigma_xx, x) - jacobian(sigma_xy, y)
+    if dim == 3:
+        equilibrium_x -= jacobian(sigma_xz, z)
+    if time:
+        equilibrium_x += rho * hessian(u, t)
+    return equilibrium_x
+
+
+def equilibrium_y_expected_result(
+    v, x, y, z, t, rho, dim, time, sigma_yy, sigma_xy, sigma_yz=None
+):
+    equilibrium_y = -jacobian(sigma_xy, x) - jacobian(sigma_yy, y)
+    if dim == 3:
+        equilibrium_y -= jacobian(sigma_yz, z)
+    if time:
+        equilibrium_y += rho * hessian(v, t)
+    return equilibrium_y
+
+
+def equilibrium_z_expected_result(
+    w, x, y, z, t, rho, time, sigma_xz, sigma_yz, sigma_zz
+):
+    equilibrium_z = (
+        -jacobian(sigma_xz, x) - jacobian(sigma_yz, y) - jacobian(sigma_zz, z)
+    )
+    if time:
+        equilibrium_z += rho * hessian(w, t)
+    return equilibrium_z
+
+
+def traction_x_expected_result(
+    normal_x, normal_y, sigma_xx, sigma_xy, normal_z=None, sigma_xz=None
+):
+    traction_x = normal_x * sigma_xx + normal_y * sigma_xy
+    if normal_z is not None and sigma_xz is not None:
+        traction_x += normal_z * sigma_xz
+    return traction_x
+
+
+def traction_y_expected_result(
+    normal_x, normal_y, sigma_xy, sigma_yy, normal_z=None, sigma_yz=None
+):
+    traction_y = normal_x * sigma_xy + normal_y * sigma_yy
+    if normal_z is not None and sigma_yz is not None:
+        traction_y += normal_z * sigma_yz
+    return traction_y
+
+
+def traction_z_expected_result(
+    normal_x, normal_y, normal_z, sigma_xz, sigma_yz, sigma_zz
+):
+    traction_z = normal_x * sigma_xz + normal_y * sigma_yz + normal_z * sigma_zz
+    return traction_z
+
+
+def navier_x_expected_result(u, v, w, x, y, z, t, lambda_, mu, rho, dim, time):
+    duxvywz = jacobian(u, x) + jacobian(v, y)
+    duxxuyyuzz = hessian(u, x) + hessian(u, y)
+    if dim == 3:
+        duxvywz += jacobian(w, z)
+        duxxuyyuzz += hessian(u, z)
+    navier_x = -(lambda_ + mu) * jacobian(duxvywz, x) - mu * duxxuyyuzz
+    if time:
+        navier_x += rho * hessian(u, t)
+    return navier_x
+
+
+def navier_y_expected_result(u, v, w, x, y, z, t, lambda_, mu, rho, dim, time):
+    duxvywz = jacobian(u, x) + jacobian(v, y)
+    dvxxvyyvzz = hessian(v, x) + hessian(v, y)
+    if dim == 3:
+        duxvywz += jacobian(w, z)
+        dvxxvyyvzz += hessian(v, z)
+    navier_y = -(lambda_ + mu) * jacobian(duxvywz, y) - mu * dvxxvyyvzz
+    if time:
+        navier_y += rho * hessian(v, t)
+    return navier_y
+
+
+def navier_z_expected_result(u, v, w, x, y, z, t, lambda_, mu, rho, time):
+    duxvywz = jacobian(u, x) + jacobian(v, y) + jacobian(w, z)
+    dwxxvyyvzz = hessian(w, x) + hessian(w, y) + hessian(w, z)
+    navier_z = -(lambda_ + mu) * jacobian(duxvywz, z) - mu * dwxxvyyvzz
+    if time:
+        navier_z += rho * hessian(w, t)
+    return navier_z
+
+
+@pytest.mark.parametrize(
+    "E, nu, lambda_, mu, rho, dim, time",
+    [
+        (None, None, 1e3, 1e3, 1, 2, False),
+        (None, None, 1e3, 1e3, 1, 2, True),
+        (None, None, 1e3, 1e3, 1, 3, False),
+        (None, None, 1e3, 1e3, 1, 3, True),
+    ],
+)
+def test_linear_elasticity(E, nu, lambda_, mu, rho, dim, time):
+    batch_size = 13
+    x = paddle.randn([batch_size, 1])
+    y = paddle.randn([batch_size, 1])
+    z = paddle.randn([batch_size, 1]) if dim == 3 else None
+    t = paddle.randn([batch_size, 1]) if time else None
+    normal_x = paddle.randn([batch_size, 1])
+    normal_y = paddle.randn([batch_size, 1])
+    normal_z = paddle.randn([batch_size, 1]) if dim == 3 else None
+
+    x.stop_gradient = False
+    y.stop_gradient = False
+    if time:
+        t.stop_gradient = False
+    if dim == 3:
+        z.stop_gradient = False
+
+    input_data = paddle.concat([x, y], axis=1)
+    if time:
+        input_data = paddle.concat([t, input_data], axis=1)
+    if dim == 3:
+        input_data = paddle.concat([input_data, z], axis=1)
+
+    model = nn.Sequential(
+        nn.Linear(input_data.shape[1], 9 if dim == 3 else 5),
+        nn.Tanh(),
+    )
+
+    output = model(input_data)
+
+    u, v, *other_outputs = paddle.split(output, num_or_sections=output.shape[1], axis=1)
+
+    if dim == 3:
+        w = other_outputs[0]
+        sigma_xx, sigma_xy, sigma_xz, sigma_yy, sigma_yz, sigma_zz = other_outputs[1:]
+    else:
+        w = None
+        sigma_xx, sigma_xy, sigma_yy = other_outputs[0:3]
+        sigma_xz, sigma_yz, sigma_zz = None, None, None
+
+    expected_stress_disp_xx = stress_disp_xx_expected_result(
+        u, v, w, x, y, z, lambda_, mu, dim, sigma_xx
+    )
+    expected_stress_disp_yy = stress_disp_yy_expected_result(
+        u, v, w, x, y, z, lambda_, mu, dim, sigma_yy
+    )
+    expected_stress_disp_xy = stress_disp_xy_expected_result(u, v, x, y, mu, sigma_xy)
+    expected_equilibrium_x = equilibrium_x_expected_result(
+        u, x, y, z, t, rho, dim, time, sigma_xx, sigma_xy, sigma_xz
+    )
+    expected_equilibrium_y = equilibrium_y_expected_result(
+        v, x, y, z, t, rho, dim, time, sigma_yy, sigma_xy, sigma_yz
+    )
+    expected_traction_x = traction_x_expected_result(
+        normal_x, normal_y, sigma_xx, sigma_xy, normal_z, sigma_xz
+    )
+    expected_traction_y = traction_y_expected_result(
+        normal_x, normal_y, sigma_xy, sigma_yy, normal_z, sigma_yz
+    )
+    expected_navier_x = navier_x_expected_result(
+        u, v, w, x, y, z, t, lambda_, mu, rho, dim, time
+    )
+    expected_navier_y = navier_y_expected_result(
+        u, v, w, x, y, z, t, lambda_, mu, rho, dim, time
+    )
+    if dim == 3:
+        expected_stress_disp_zz = stress_disp_zz_expected_result(
+            u, v, w, x, y, z, lambda_, mu, sigma_zz
+        )
+        expected_stress_disp_xz = stress_disp_xz_expected_result(
+            u, w, x, z, mu, sigma_xz
+        )
+        expected_stress_disp_yz = stress_disp_yz_expected_result(
+            v, w, y, z, mu, sigma_yz
+        )
+        expected_equilibrium_z = equilibrium_z_expected_result(
+            w, x, y, z, t, rho, time, sigma_xz, sigma_yz, sigma_zz
+        )
+        expected_navier_z = navier_z_expected_result(
+            u, v, w, x, y, z, t, lambda_, mu, rho, time
+        )
+        expected_traction_z = traction_z_expected_result(
+            normal_x, normal_y, normal_z, sigma_xz, sigma_yz, sigma_zz
+        )
+
+    linear_elasticity = equation.LinearElasticity(
+        E=E, nu=nu, lambda_=lambda_, mu=mu, rho=rho, dim=dim, time=time
+    )
+
+    data_dict = {
+        "x": x,
+        "y": y,
+        "u": u,
+        "v": v,
+        "z": z,
+        "w": w,
+        "t": t,
+        "sigma_xx": sigma_xx,
+        "sigma_xy": sigma_xy,
+        "sigma_xz": sigma_xz,
+        "sigma_yy": sigma_yy,
+        "sigma_yz": sigma_yz,
+        "sigma_zz": sigma_zz,
+        "normal_x": normal_x,
+        "normal_y": normal_y,
+        "normal_z": normal_z,
+    }
+
+    test_output_names = [
+        "stress_disp_xx",
+        "stress_disp_yy",
+        "stress_disp_xy",
+        "equilibrium_x",
+        "equilibrium_y",
+        "navier_x",
+        "navier_y",
+        "traction_x",
+        "traction_y",
+    ]
+
+    if dim == 3:
+        test_output_names.extend(
+            [
+                "stress_disp_zz",
+                "stress_disp_xz",
+                "stress_disp_yz",
+                "equilibrium_z",
+                "navier_z",
+                "traction_z",
+            ]
+        )
+
+    test_output = {}
+    for name in test_output_names:
+        test_output[name] = linear_elasticity.equations[name](data_dict)
+
+    expected_output = {
+        "stress_disp_xx": expected_stress_disp_xx,
+        "stress_disp_yy": expected_stress_disp_yy,
+        "stress_disp_xy": expected_stress_disp_xy,
+        "equilibrium_x": expected_equilibrium_x,
+        "equilibrium_y": expected_equilibrium_y,
+        "navier_x": expected_navier_x,
+        "navier_y": expected_navier_y,
+        "traction_x": expected_traction_x,
+        "traction_y": expected_traction_y,
+    }
+    if dim == 3:
+        expected_output.update(
+            {
+                "stress_disp_zz": expected_stress_disp_zz,
+                "stress_disp_xz": expected_stress_disp_xz,
+                "stress_disp_yz": expected_stress_disp_yz,
+                "equilibrium_z": expected_equilibrium_z,
+                "navier_z": expected_navier_z,
+                "traction_z": expected_traction_z,
+            }
+        )
+
+    for name in test_output_names:
+        assert paddle.allclose(expected_output[name], test_output[name])
+
+
+if __name__ == "__main__":
+    pytest.main()