optimization for inv_free

Author: Roushelfy

4_friction/simulator.py

@@ -10,8 +10,8 @@
 # simulation setup
 side_len = 1
 rho = 1000      # density of square
-k = 2e4         # spring stiffness
-n_seg = 4       # num of segments per side of the square
+k = 4e4         # spring stiffness
+n_seg = 10       # num of segments per side of the square
 h = 0.01        # time step size in s
 DBC = []        # no nodes need to be fixed
 # ANCHOR: slope_setup

6_inv_free/simulator.py

@@ -14,7 +14,7 @@
 E = 1e5         # Young's modulus
 nu = 0.4        # Poisson's ratio
 # ANCHOR_END: lame_param
-n_seg = 4       # num of segments per side of the square
+n_seg = 6      # num of segments per side of the square
 h = 0.01        # time step size in s
 DBC = [(n_seg + 1) * (n_seg + 1)]       # dirichlet node index
 DBC_v = [np.array([0.0, -0.5])]         # dirichlet node velocity
@@ -50,6 +50,7 @@
 resolution = np.array([900, 900])
 offset = resolution / 2
 scale = 200
+DBC_stiff = [1000]
 def screen_projection(x):
     return [offset[0] + scale * x[0], resolution[1] - (offset[1] + scale * x[1])]
 
@@ -84,7 +85,7 @@ def screen_projection(x):
     pygame.display.flip()   # flip the display
 
     # step forward simulation and wait for screen refresh
-    [x, v] = time_integrator.step_forward(x, e, v, m, vol, IB, mu_lame, lam, ground_n, ground_o, contact_area, mu, is_DBC, DBC, DBC_v, DBC_limit, h, 1e-2)
+    [x, v] = time_integrator.step_forward(x, e, v, m, vol, IB, mu_lame, lam, ground_n, ground_o, contact_area, mu, is_DBC, DBC, DBC_v, DBC_limit,DBC_stiff, h, 1e-2)
     time_step += 1
     pygame.time.wait(int(h * 1000))
     square_mesh.write_to_file(time_step, x, e)

6_inv_free/time_integrator.py

@@ -13,7 +13,10 @@
 import FrictionEnergy
 import SpringEnergy
 
-def step_forward(x, e, v, m, vol, IB, mu_lame, lam, n, o, contact_area, mu, is_DBC, DBC, DBC_v, DBC_limit, h, tol):
+
+
+
+def step_forward(x, e, v, m, vol, IB, mu_lame, lam, n, o, contact_area, mu, is_DBC, DBC, DBC_v, DBC_limit, DBC_stiff_,h, tol):
     x_tilde = x + v * h     # implicit Euler predictive position
     x_n = copy.deepcopy(x)
     mu_lambda = BarrierEnergy.compute_mu_lambda(x, n, o, contact_area, mu)  # compute mu * lambda for each node using x^n
@@ -23,7 +26,7 @@ def step_forward(x, e, v, m, vol, IB, mu_lame, lam, n, o, contact_area, mu, is_D
             DBC_target.append(x_n[DBC[i]] + h * DBC_v[i])
         else:
             DBC_target.append(x_n[DBC[i]])
-    DBC_stiff = 1000  # initialize stiffness for DBC springs
+    DBC_stiff = DBC_stiff_[0]
 
     # Newton loop
     iter = 0
@@ -35,7 +38,8 @@ def step_forward(x, e, v, m, vol, IB, mu_lame, lam, n, o, contact_area, mu, is_D
 
         if (LA.norm(p, inf) / h <= tol) & (sum(DBC_satisfied) != len(DBC)):
             # increase DBC stiffness and recompute energy value record
-            DBC_stiff *= 2
+            DBC_stiff_[0] *= 2
+            DBC_stiff = DBC_stiff_[0]
             E_last = IP_val(x, e, x_tilde, m, vol, IB, mu_lame, lam, n, o, contact_area, (x - x_n) / h, mu_lambda, DBC, DBC_target, DBC_stiff, h)
 
         # filter line search