1D/2D/3D solutions for all three growth models. Problem in the linear solution.

growing_domain/diffusion_on_growing_domain.py

@@ -47,34 +47,37 @@ class GrowthDiffusion(object):
                         + self.Dc * df.inner(df.nabla_grad(self.c), df.nabla_grad(tc))
                         + self.Dc * df.inner(df.nabla_grad(self.c), df.nabla_grad(tc))
                         - df.inner(self.reaction_rate * self.c, tc) ) * df.dx
                         - df.inner(self.reaction_rate * self.c, tc) ) * df.dx
 
 
+
     def solve(self):
     def solve(self):
-        times = np.arange(0, self.maxtime+self.timestep, self.timestep)
-        fname = params['timestamp'] + '_concentration' 
-        cFile = df.XDMFFile(fname+ '.xdmf')
+        savesteps = int(self.savetime/self.timestep)
+        maxsteps = int(self.maxtime/self.timestep)
+        self.time = 0.0
+        cFile = df.XDMFFile('%s_concentration.xdmf' %params['timestamp'])
 
 
-        
         # initial condition
         # initial condition
         r2 = "+".join(['x['+str(i)+']*x['+str(i)+']' for i in range(self.dimension)])
         r2 = "+".join(['x['+str(i)+']*x['+str(i)+']' for i in range(self.dimension)])
         c0 = '1 + 0.1*cos(pi*m*(%s)/L)' % r2           
         c0 = '1 + 0.1*cos(pi*m*(%s)/L)' % r2           
         c0 = df.Expression(c0, pi=np.pi, m=1, L=self.system_size, degree=1)            
         c0 = df.Expression(c0, pi=np.pi, m=1, L=self.system_size, degree=1)            
         self.c0.assign(df.project(c0, self.SFS))
         self.c0.assign(df.project(c0, self.SFS))
         # save data
         # save data
-        cFile.write_checkpoint(self.c0, fname, times[0])
+        cFile.write_checkpoint(self.c0, 'concentration', self.time)
 
 
         # time stepping
         # time stepping
-        for i in progressbar.progressbar(range(1, len(times))):
+        for steps in progressbar.progressbar(range(1, maxsteps + 1)):
             # get velocity
             # get velocity
-            self.sigma.t = times[i-1]
+            self.sigma.t = self.time
             self.velocity.assign(df.project(self.sigma*self.growth_direction, self.VFS))
             self.velocity.assign(df.project(self.sigma*self.growth_direction, self.VFS))
             # solve
             # solve
             df.solve(self.form == 0, self.c)
             df.solve(self.form == 0, self.c)
             # update
             # update
             self.c0.assign(self.c)
             self.c0.assign(self.c)
             # save data
             # save data
-            cFile.write_checkpoint(self.c0, fname, times[i], append=True)
+            if steps % savesteps == 0:
+                cFile.write_checkpoint(self.c0, 'concentration', self.time, append=True)
             # move mesh
             # move mesh
             displacement = df.project(self.velocity*self.timestep, self.VFS)
             displacement = df.project(self.velocity*self.timestep, self.VFS)
             df.ALE.move(self.mesh, displacement)
             df.ALE.move(self.mesh, displacement)
+            self.time += self.timestep
 
 
         cFile.close()
         cFile.close()
     
     
@@ -95,5 +98,5 @@ if __name__ == '__main__':
     gd = GrowthDiffusion(params)
     gd = GrowthDiffusion(params)
     gd.solve()
     gd.solve()
     
     
-    with open(params['timestamp'] + '_parmeters.json', "w") as fp:
+    with open(params['timestamp'] + '_parameters.json', "w") as fp:
         json.dump(params, fp, indent=4)    
         json.dump(params, fp, indent=4)    
\ No newline at end of file

growing_domain/parameters.json

 {
 {
     "dimension" : 2,
     "dimension" : 2,
-    "resolution" : 5,
+    "resolution" : 15,
     "system_size" : 1,
     "system_size" : 1,
 
 
     "Dc" : 0.1,
     "Dc" : 0.1,
-    "growth" : "exponential",
+    "growth" : "linear",
     "growth_parameter" : 0.1,
     "growth_parameter" : 0.1,
     "reaction_rate" : 0.0,
     "reaction_rate" : 0.0,
 
 
     "timestep" : 0.01,
     "timestep" : 0.01,
-    "maxtime" : 1.0
+    "savetime": 0.1,
+    "maxtime" : 5.0
 }
 }