PBD + MPM examples

11_mpm_plastic/readme.md

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+# 2D Sand with a Sphere Collider
+
+A 2D sand block falling onto a static red sphere collider simulated by Material Point Method (MPM). The sand undergoes irreversible deformation and splashing upon impact, demonstrating granular flow and frictional boundary response.
+
+![results](results.gif)
+
+## Dependencies
+```
+pip install numpy taichi
+```
+
+## Run
+```
+python simulator.py
+```

11_mpm_plastic/simulator.py

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+# Material Point Method Simulation
+import numpy as np  # numpy for linear algebra
+import taichi as ti # taichi for fast and parallelized computation 
+
+ti.init(arch=ti.cpu)
+
+# sampling material particles with poisson-disk sampling
+################################
+# Poisson Disk Sampling Tool
+################################
+def poisson_disk_sampling(radius, domain_size, k=30):
+    """Bridson's algorithm for Poisson-disk sampling in 2D"""
+    cell_size = radius / np.sqrt(2)
+    grid_shape = (int(domain_size[0] / cell_size) + 1, int(domain_size[1] / cell_size) + 1)
+    grid = -np.ones(grid_shape, dtype=int)
+    samples = []
+    active_list = []
+
+    def in_domain(p):
+        return 0 <= p[0] < domain_size[0] and 0 <= p[1] < domain_size[1]
+
+    def get_cell_coords(p):
+        return int(p[0] / cell_size), int(p[1] / cell_size)
+
+    def get_nearby_samples(p):
+        i, j = get_cell_coords(p)
+        neighbors = []
+        for di in [-2, -1, 0, 1, 2]:
+            for dj in [-2, -1, 0, 1, 2]:
+                ni, nj = i + di, j + dj
+                if 0 <= ni < grid.shape[0] and 0 <= nj < grid.shape[1]:
+                    idx = grid[ni, nj]
+                    if idx != -1:
+                        neighbors.append(samples[idx])
+        return neighbors
+
+    # Start with a random point
+    first_point = np.array([np.random.uniform(0, domain_size[0]), np.random.uniform(0, domain_size[1])])
+    samples.append(first_point)
+    active_list.append(0)
+    grid[get_cell_coords(first_point)] = 0
+
+    while active_list:
+        idx = np.random.choice(active_list)
+        base_point = samples[idx]
+        found = False
+        for _ in range(k):
+            angle = np.random.uniform(0, 2 * np.pi)
+            r = np.random.uniform(radius, 2 * radius)
+            new_point = base_point + r * np.array([np.cos(angle), np.sin(angle)])
+            if in_domain(new_point):
+                neighbors = get_nearby_samples(new_point)
+                if all(np.linalg.norm(new_point - n) >= radius for n in neighbors):
+                    samples.append(new_point)
+                    active_list.append(len(samples) - 1)
+                    grid[get_cell_coords(new_point)] = len(samples) - 1
+                    found = True
+        if not found:
+            active_list.remove(idx)
+    return np.array(samples)
+
+# ANCHOR: data_def
+# simulation setup
+grid_size = 128 # background Eulerian grid's resolution, in 2D is [128, 128]
+dx = 1.0 / grid_size # the domain size is [1m, 1m] in 2D, so dx for each cell is (1/128)m
+dt = 2e-4 # time step size in second
+ppc = 8 # average particle-per-cell
+
+density = 400 # kg / m^3
+E, nu = 3.537e5, 0.3 # sand's Young's modulus and Poisson's ratio
+mu, lam = E / (2 * (1 + nu)), E * nu / ((1 + nu) * (1 - 2 * nu)) # sand's Lame parameters
+sdf_friction = 0.5 # frictional coefficient of SDF boundary condition
+friction_angle_in_degrees = 25.0 # Drucker Prager friction angle
+# ANCHOR: D_def
+D = (1./4.) * dx * dx # constant D for Quadratic B-spline used for APIC
+# ANCHOR_END: D_def
+
+# sampling material particles with poisson-disk sampling
+poisson_samples = poisson_disk_sampling(dx / np.sqrt(ppc), [0.2, 0.4]) # simulating a [30cm, 50cm] sand block
+
+# material particles data
+N_particles = len(poisson_samples)
+x = ti.Vector.field(2, float, N_particles) # the position of particles
+x.from_numpy(np.array(poisson_samples) + [0.4, 0.55])
+v = ti.Vector.field(2, float, N_particles) # the velocity of particles
+vol = ti.field(float, N_particles)         # the volume of particle
+vol.fill(0.2 * 0.4 / N_particles) # get the volume of each particle as V_rest / N_particles
+m = ti.field(float, N_particles)           # the mass of particle
+m.fill(vol[0] * density)
+F = ti.Matrix.field(2, 2, float, N_particles)  # the deformation gradient of particles
+F.from_numpy(np.tile(np.eye(2), (N_particles, 1, 1)))
+C = ti.Matrix.field(2, 2, float, N_particles)  # the affine-matrix of particles
+
+diff_log_J = ti.field(float, N_particles) # tracks changes in the log of the volume gained during extension
+
+# grid data
+grid_m = ti.field(float, (grid_size, grid_size))
+grid_v = ti.Vector.field(2, float, (grid_size, grid_size))
+# ANCHOR_END: data_def
+
+# ANCHOR: reset_grid
+def reset_grid():
+    # after each transfer, the grid is reset
+    grid_m.fill(0)
+    grid_v.fill(0)
+# ANCHOR_END: reset_grid
+
+################################
+# Stvk Hencky Elasticity
+################################
+# ANCHOR: stvk
+@ti.func
+def StVK_Hencky_PK1_2D(F):
+    U, sig, V = ti.svd(F)
+    inv_sig = sig.inverse()
+    e = ti.Matrix([[ti.log(sig[0, 0]), 0], [0, ti.log(sig[1, 1])]])
+    return U @ (2 * mu * inv_sig @ e + lam * e.trace() * inv_sig) @ V.transpose()
+# ANCHOR_END: stvk
+
+################################
+# Drucker Prager plasticity
+################################
+# ANCHOR: drucker_prager
+@ti.func
+def Drucker_Prager_return_mapping(F, diff_log_J):
+    dim = ti.static(F.n)
+    sin_phi = ti.sin(friction_angle_in_degrees/ 180.0 * ti.math.pi)
+    friction_alpha = ti.sqrt(2.0 / 3.0) * 2.0 * sin_phi / (3.0 - sin_phi)
+    U, sig_diag, V = ti.svd(F)
+    sig = ti.Vector([ti.max(sig_diag[i,i], 0.05) for i in ti.static(range(dim))])
+    epsilon = ti.log(sig)
+    epsilon += diff_log_J / dim # volume correction treatment
+    trace_epsilon = epsilon.sum()
+    shifted_trace = trace_epsilon
+    if shifted_trace >= 0:
+        for d in ti.static(range(dim)):
+            epsilon[d] = 0.
+    else:
+        epsilon_hat = epsilon - (trace_epsilon / dim)
+        epsilon_hat_norm = ti.sqrt(epsilon_hat.dot(epsilon_hat)+1e-8)
+        delta_gamma = epsilon_hat_norm + (dim * lam + 2. * mu) / (2. * mu) * (shifted_trace) * friction_alpha
+        epsilon -= (ti.max(delta_gamma, 0) / epsilon_hat_norm) * epsilon_hat
+    sig_out = ti.exp(epsilon)
+    for d in ti.static(range(dim)):
+        sig_diag[d,d] = sig_out[d]
+    return U @ sig_diag @ V.transpose()
+# ANCHOR_END: drucker_prager
+
+# Particle-to-Grid (P2G) Transfers
+# ANCHOR: p2g
+@ti.kernel
+def particle_to_grid_transfer():
+    for p in range(N_particles):
+        base = (x[p] / dx - 0.5).cast(int)
+        fx = x[p] / dx - base.cast(float)
+        # quadratic B-spline interpolating functions
+        w = [0.5 * (1.5 - fx) ** 2, 0.75 - (fx - 1) ** 2, 0.5 * (fx - 0.5) ** 2]
+        # gradient of the interpolating function
+        dw_dx = [fx - 1.5, 2 * (1.0 - fx), fx - 0.5]
+
+        P = StVK_Hencky_PK1_2D(F[p])
+        for i in ti.static(range(3)):
+            for j in ti.static(range(3)):
+                offset = ti.Vector([i, j])
+                dpos = (offset.cast(float) - fx) * dx
+                weight = w[i][0] * w[j][1]
+                grad_weight = ti.Vector([(1. / dx) * dw_dx[i][0] * w[j][1], 
+                                          w[i][0] * (1. / dx) * dw_dx[j][1]])
+
+                grid_m[base + offset] += weight * m[p] # mass transfer
+                # ANCHOR: apic_p2g
+                grid_v[base + offset] += weight * m[p] * (v[p] + C[p] @ dpos) # momentum Transfer, APIC formulation
+                # ANCHOR_END: apic_p2g
+                # internal force (stress) transfer
+                fi = -vol[p] * P @ F[p].transpose() @ grad_weight
+                grid_v[base + offset] += dt * fi
+# ANCHOR_END: p2g
+
+# Grid Update
+@ti.kernel
+def update_grid():
+    for i, j in grid_m:
+        if grid_m[i, j] > 0:
+            grid_v[i, j] = grid_v[i, j] / grid_m[i, j] # extract updated nodal velocity from transferred nodal momentum
+            grid_v[i, j] += ti.Vector([0, -9.81]) * dt # gravity
+
+            # Dirichlet BC near the bounding box
+            if i <= 3 or i > grid_size - 3 or j <= 2 or j > grid_size - 3:
+                grid_v[i, j] = 0
+
+            x_i = (ti.Vector([i, j])) * dx # position of the grid-node
+
+            # ANCHOR: sphere_sdf
+            # a sphere SDF as boundary condition
+            sphere_center = ti.Vector([0.5, 0.5])
+            sphere_radius = 0.05 + dx # add a dx-gap to avoid penetration
+            if (x_i - sphere_center).norm() < sphere_radius:
+                normal = (x_i - sphere_center).normalized()
+                diff_vel = -grid_v[i, j]
+                dotnv = normal.dot(diff_vel)
+                dotnv_frac = dotnv * (1.0 - sdf_friction)
+                grid_v[i, j] += diff_vel * sdf_friction + normal * dotnv_frac
+            # ANCHOR_END: sphere_sdf
+
+
+# Grid-to-Particle (G2P) Transfers
+# ANCHOR: g2p
+@ti.kernel
+def grid_to_particle_transfer():
+    for p in range(N_particles):
+        base = (x[p] / dx - 0.5).cast(int)
+        fx = x[p] / dx - base.cast(float)
+        # quadratic B-spline interpolating functions 
+        w = [0.5 * (1.5 - fx) ** 2, 0.75 - (fx - 1) ** 2, 0.5 * (fx - 0.5) ** 2]
+        # gradient of the interpolating function
+        dw_dx = [fx - 1.5, 2 * (1.0 - fx), fx - 0.5]
+
+        new_v = ti.Vector.zero(float, 2)
+        new_C = ti.Matrix.zero(float, 2, 2)
+        v_grad_wT = ti.Matrix.zero(float, 2, 2)
+        for i in ti.static(range(3)):
+            for j in ti.static(range(3)):
+                offset = ti.Vector([i, j])
+                dpos = (offset.cast(float) - fx) * dx
+                weight = w[i][0] * w[j][1]
+                grad_weight = ti.Vector([(1. / dx) * dw_dx[i][0] * w[j][1], 
+                                          w[i][0] * (1. / dx) * dw_dx[j][1]])
+
+                new_v += weight * grid_v[base + offset]
+                # ANCHOR: apic_g2p
+                new_C += weight * grid_v[base + offset].outer_product(dpos) / D
+                # ANCHOR_END: apic_g2p
+                v_grad_wT += grid_v[base + offset].outer_product(grad_weight)
+
+        v[p] = new_v
+        C[p] = new_C
+        # note the updated F here is the trial elastic deformation gradient
+        F[p] = (ti.Matrix.identity(float, 2) + dt * v_grad_wT) @ F[p]
+# ANCHOR_END: g2p
+
+# Deformation Gradient and Particle State Update
+# ANCHOR: particle_update
+@ti.kernel
+def update_particle_state():
+    for p in range(N_particles):
+        # trial elastic deformation gradient
+        F_tr = F[p]
+        # apply return mapping to correct the trial elastic state, projecting the stress induced by F_tr
+        # back onto the yield surface, following the direction specified by the plastic flow rule.
+        new_F = Drucker_Prager_return_mapping(F_tr, diff_log_J[p])
+        # track the volume change incurred by return mapping to correct volume, following https://dl.acm.org/doi/10.1145/3072959.3073651 sec 4.3.4
+        diff_log_J[p] += -ti.log(new_F.determinant()) + ti.log(F_tr.determinant()) 
+        F[p] = new_F
+        # advection
+        x[p] += dt * v[p]
+# ANCHOR_END: particle_update
+
+# ANCHOR: time_step
+def step():
+    # a single time step of the Material Point Method (MPM) simulation
+    reset_grid()
+    particle_to_grid_transfer()
+    update_grid()
+    grid_to_particle_transfer()
+    update_particle_state()
+# ANCHOR_END: time_step
+
+################################
+# Main 
+################################
+gui = ti.GUI("2D MPM Sand", res = 512, background_color = 0xFFFFFF)
+frame = 0
+while True:
+    for s in range(50):
+        step()
+
+    gui.circles(np.array([[0.5, 0.5]]), radius = 0.05 * gui.res[0], color = 0xFF0000)
+    gui.circles(x.to_numpy(), radius = 1.5, color = 0xD6B588)
+    gui.show()