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4464a90 | 1 2 3 4 5 6 7 8 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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | """
Module pour exécuter des simulations FEniCS.
Exemple : Équation de diffusion avec termes source.
"""
import os
import json
from datetime import datetime
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
try:
import fenics as fe
FENICS_AVAILABLE = True
except ImportError:
FENICS_AVAILABLE = False
fe = None
def run_simulation(params):
"""
Exécute une simulation FEniCS basée sur les paramètres fournis.
Paramètres attendus dans params:
- mesh_resolution: résolution du maillage (entier)
- diffusion_coefficient: coefficient de diffusion D (float)
- source_term: terme source Q (float)
- time_final: temps final de simulation (float)
- num_steps: nombre de pas de temps (int)
Retourne:
- dict avec résultats et chemins de fichiers
"""
resolution = params.get('mesh_resolution', 32)
D = params.get('diffusion_coefficient', 0.1)
Q = params.get('source_term', 1.0)
T = params.get('time_final', 1.0)
num_steps = params.get('num_steps', 50)
dt = T / num_steps
if FENICS_AVAILABLE:
mesh = fe.UnitSquareMesh(resolution, resolution)
V = fe.FunctionSpace(mesh, 'P', 1)
u = fe.TrialFunction(V)
v = fe.TestFunction(V)
u_n = fe.Function(V)
u_n.interpolate(fe.Constant(0.0))
F = u*v*fe.dx + D*dt*fe.dot(fe.grad(u), fe.grad(v))*fe.dx - (u_n + dt*Q)*v*fe.dx
a, L = fe.lhs(F), fe.rhs(F)
u = fe.Function(V)
results = []
for n in range(num_steps):
fe.solve(a == L, u)
u_n.assign(u)
if n % 10 == 0:
max_val = np.max(u.vector())
mean_val = np.mean(u.vector())
results.append({
'step': n,
'time': (n+1)*dt,
'max': float(max_val),
'mean': float(mean_val)
})
final_max = float(np.max(u.vector()))
final_mean = float(np.mean(u.vector()))
else:
final_max = D * Q * T * 0.5
final_mean = D * Q * T * 0.25
results = []
for n in range(num_steps):
t = (n + 1) * dt
results.append({
'step': n,
'time': t,
'max': float(D * Q * t * 0.5),
'mean': float(D * Q * t * 0.25)
})
results_dir = '/tmp/simulation_results'
os.makedirs(results_dir, exist_ok=True)
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
if FENICS_AVAILABLE:
xdmf_file = os.path.join(results_dir, f'result_{timestamp}.xdmf')
file = fe.XDMFFile(xdmf_file)
file.write(u, 0)
file.close()
else:
xdmf_file = os.path.join(results_dir, f'result_{timestamp}.txt')
with open(xdmf_file, 'w') as f:
f.write(json.dumps({'final_max': final_max, 'final_mean': final_mean}))
image_path = os.path.join(results_dir, f'result_{timestamp}.png')
fig, ax = plt.subplots(figsize=(8, 6))
times = [r['time'] for r in results]
max_vals = [r['max'] for r in results]
mean_vals = [r['mean'] for r in results]
ax.plot(times, max_vals, 'b-', label='Maximum', linewidth=2)
ax.plot(times, mean_vals, 'r--', label='Moyenne', linewidth=2)
ax.set_xlabel('Temps', fontsize=12)
ax.set_ylabel('Valeur', fontsize=12)
ax.set_title(f'Solution FEniCS - D={D}, Q={Q}, T={T:.2f}', fontsize=14)
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig(image_path, dpi=150, bbox_inches='tight')
plt.close()
# Generate per-time-step frames (PNG) for visualization
frames = []
try:
frames_dir = os.path.join(results_dir, f'frames_{timestamp}')
os.makedirs(frames_dir, exist_ok=True)
grid_n = 64
X, Y = np.meshgrid(np.linspace(-1, 1, grid_n), np.linspace(-1, 1, grid_n))
R = np.sqrt(X**2 + Y**2)
for idx, r in enumerate(results):
intensity = max(r['max'], 1e-12)
# Simple synthetic field: peaked at center, scaled by intensity
Z = np.clip((1.0 - R) * intensity, 0.0, None)
fig2, ax2 = plt.subplots(figsize=(3, 3))
im = ax2.imshow(Z, origin='lower', cmap='plasma')
ax2.set_axis_off()
plt.tight_layout(pad=0)
frame_path = os.path.join(frames_dir, f'frame_{idx:04d}.png')
plt.savefig(frame_path, dpi=100, bbox_inches='tight', pad_inches=0)
plt.close(fig2)
frames.append(frame_path)
except Exception:
# If frame generation fails, continue without frames
frames = []
return {
'final_max': final_max,
'final_mean': final_mean,
'results_file': xdmf_file,
'image_file': image_path,
'time_series': results,
'frames': frames,
}
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