Elastic_trampoline_files

examples/RigidBallSoftTrampoline/collision_force.py

@@ -0,0 +1,48 @@
+import numpy as np
+import scipy.sparse as spp
+
+
+def collision_force(k, phi_1, phi_2, blend_w, grid_size_z, grid_size_r, eps, dx):
+    """
+    computes the collision force between 2 bodies
+    using their level sets
+    """
+
+    gradient = (
+        spp.diags(
+            [-0.5, 0.5, -0.5, 0.5],
+            [-1, 1, grid_size_r - 1, -grid_size_r + 1],
+            shape=(grid_size_r, grid_size_r),
+            format="csr",
+        )
+        / dx
+    )
+
+    gradient_T = (
+        spp.diags(
+            [-0.5, 0.5, -0.5, 0.5],
+            [-1, 1, grid_size_z - 1, -grid_size_z + 1],
+            shape=(grid_size_z, grid_size_z),
+            format="csr",
+        )
+        / dx
+    )
+
+    phi_diff = 0.5 * (phi_2 - phi_1)
+    delta_phi = phi_diff * 0
+    delta_phi += (
+        (np.fabs(phi_diff) < blend_w)
+        * 0.5
+        * (1 + np.cos(np.pi * phi_diff / blend_w))
+        / blend_w
+    )
+    phi_diff_x = np.mat(phi_diff) * gradient_T
+    phi_diff_y = gradient * np.mat(phi_diff)
+    s = np.sqrt(np.square(phi_diff_x) + np.square(phi_diff_y))
+    phi_diff_x /= s + eps
+    phi_diff_y /= s + eps
+    S = phi_diff / np.sqrt(phi_diff**2 + eps**2)
+    body_mask = np.logical_or((phi_1 > 0), (phi_2 > 0))
+    force_x = body_mask * S * phi_diff_x * k * delta_phi
+    force_y = body_mask * S * phi_diff_y * k * delta_phi
+    return force_x, force_y

examples/RigidBallSoftTrampoline/curl.py

@@ -0,0 +1,31 @@
+import numpy as np
+import scipy.sparse as spp
+
+
+def curl(ax, ay, grid_size_z, grid_size_r, dx):
+    """
+    compute curl of the vector using CD
+    """
+
+    gradient = (
+        spp.diags(
+            [-0.5, 0.5, -0.5, 0.5],
+            [-1, 1, grid_size_r - 1, -grid_size_r + 1],
+            shape=(grid_size_r, grid_size_r),
+            format="csr",
+        )
+        / dx
+    )
+
+    gradient_T = (
+        spp.diags(
+            [-0.5, 0.5, -0.5, 0.5],
+            [-1, 1, grid_size_z - 1, -grid_size_z + 1],
+            shape=(grid_size_z, grid_size_z),
+            format="csr",
+        )
+        / dx
+    )
+
+    vort = np.mat(ay) * gradient_T - gradient * np.mat(ax)
+    return vort