/[escript]/trunk/escript/py_src/flows.py
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revision 2156 by gross, Mon Dec 15 05:09:02 2008 UTC revision 2445 by gross, Fri May 29 03:23:25 2009 UTC
# Line 16  http://www.uq.edu.au/esscc Line 16  http://www.uq.edu.au/esscc
16  Primary Business: Queensland, Australia"""  Primary Business: Queensland, Australia"""
17  __license__="""Licensed under the Open Software License version 3.0  __license__="""Licensed under the Open Software License version 3.0
18  http://www.opensource.org/licenses/osl-3.0.php"""  http://www.opensource.org/licenses/osl-3.0.php"""
19  __url__="http://www.uq.edu.au/esscc/escript-finley"  __url__="https://launchpad.net/escript-finley"
20    
21  """  """
22  Some models for flow  Some models for flow
# Line 34  __author__="Lutz Gross, l.gross@uq.edu.a Line 34  __author__="Lutz Gross, l.gross@uq.edu.a
34  from escript import *  from escript import *
35  import util  import util
36  from linearPDEs import LinearPDE, LinearPDESystem, LinearSinglePDE  from linearPDEs import LinearPDE, LinearPDESystem, LinearSinglePDE
37  from pdetools import HomogeneousSaddlePointProblem,Projector, ArithmeticTuple, PCG, NegativeNorm  from pdetools import HomogeneousSaddlePointProblem,Projector, ArithmeticTuple, PCG, NegativeNorm, GMRES
38    
39  class DarcyFlow(object):  class DarcyFlow(object):
40      """      """
41      solves the problem      solves the problem
42    
43      M{u_i+k_{ij}*p_{,j} = g_i}      M{u_i+k_{ij}*p_{,j} = g_i}
44      M{u_{i,i} = f}      M{u_{i,i} = f}
45    
46      where M{p} represents the pressure and M{u} the Darcy flux. M{k} represents the permeability,      where M{p} represents the pressure and M{u} the Darcy flux. M{k} represents the permeability,
47    
48      @note: The problem is solved in a least squares formulation.      @note: The problem is solved in a least squares formulation.
49      """      """
50    
51      def __init__(self, domain):      def __init__(self, domain, weight=None, useReduced=False):
52          """          """
53          initializes the Darcy flux problem          initializes the Darcy flux problem
54          @param domain: domain of the problem          @param domain: domain of the problem
55          @type domain: L{Domain}          @type domain: L{Domain}
56          """          """
57          self.domain=domain          self.domain=domain
58            if weight == None:
59               s=self.domain.getSize()
60               self.__l=(3.*util.longestEdge(self.domain)*s/util.sup(s))**2
61            else:
62               self.__l=weight
63          self.__pde_v=LinearPDESystem(domain)          self.__pde_v=LinearPDESystem(domain)
64          self.__pde_v.setValue(D=util.kronecker(domain), A=util.outer(util.kronecker(domain),util.kronecker(domain)))          if useReduced: self.__pde_v.setReducedOrderOn()
65          self.__pde_v.setSymmetryOn()          self.__pde_v.setSymmetryOn()
66            self.__pde_v.setValue(D=util.kronecker(domain), A=self.__l*util.outer(util.kronecker(domain),util.kronecker(domain)))
67            # self.__pde_v.setSolverMethod(preconditioner=self.__pde_v.ILU0)
68          self.__pde_p=LinearSinglePDE(domain)          self.__pde_p=LinearSinglePDE(domain)
69          self.__pde_p.setSymmetryOn()          self.__pde_p.setSymmetryOn()
70            if useReduced: self.__pde_p.setReducedOrderOn()
71          self.__f=Scalar(0,self.__pde_v.getFunctionSpaceForCoefficient("X"))          self.__f=Scalar(0,self.__pde_v.getFunctionSpaceForCoefficient("X"))
72          self.__g=Vector(0,self.__pde_v.getFunctionSpaceForCoefficient("Y"))          self.__g=Vector(0,self.__pde_v.getFunctionSpaceForCoefficient("Y"))
73            self.setTolerance()
74            self.setAbsoluteTolerance()
75            self.setSubProblemTolerance()
76    
77      def setValue(self,f=None, g=None, location_of_fixed_pressure=None, location_of_fixed_flux=None, permeability=None):      def setValue(self,f=None, g=None, location_of_fixed_pressure=None, location_of_fixed_flux=None, permeability=None):
78          """          """
# Line 75  class DarcyFlow(object): Line 86  class DarcyFlow(object):
86          @type location_of_fixed_pressure: scalar value on the domain (e.g. L{Data})          @type location_of_fixed_pressure: scalar value on the domain (e.g. L{Data})
87          @param location_of_fixed_flux:  mask for locations where flux is fixed.          @param location_of_fixed_flux:  mask for locations where flux is fixed.
88          @type location_of_fixed_flux: vector values on the domain (e.g. L{Data})          @type location_of_fixed_flux: vector values on the domain (e.g. L{Data})
89          @param permeability: permeability tensor. If scalar C{s} is given the tensor with          @param permeability: permeability tensor. If scalar C{s} is given the tensor with
90                               C{s} on the main diagonal is used. If vector C{v} is given the tensor with                               C{s} on the main diagonal is used. If vector C{v} is given the tensor with
91                               C{v} on the main diagonal is used.                               C{v} on the main diagonal is used.
92          @type permeability: scalar, vector or tensor values on the domain (e.g. L{Data})          @type permeability: scalar, vector or tensor values on the domain (e.g. L{Data})
93    
# Line 85  class DarcyFlow(object): Line 96  class DarcyFlow(object):
96                 or the normal component of the flux (C{location_of_fixed_flux[i]>0} if direction of the normal                 or the normal component of the flux (C{location_of_fixed_flux[i]>0} if direction of the normal
97                 is along the M{x_i} axis.                 is along the M{x_i} axis.
98          """          """
99          if f !=None:          if f !=None:
100             f=util.interpolate(f, self.__pde_v.getFunctionSpaceForCoefficient("X"))             f=util.interpolate(f, self.__pde_v.getFunctionSpaceForCoefficient("X"))
101             if f.isEmpty():             if f.isEmpty():
102                 f=Scalar(0,self.__pde_v.getFunctionSpaceForCoefficient("X"))                 f=Scalar(0,self.__pde_v.getFunctionSpaceForCoefficient("X"))
103             else:             else:
104                 if f.getRank()>0: raise ValueError,"illegal rank of f."                 if f.getRank()>0: raise ValueError,"illegal rank of f."
105             self.f=f             self.__f=f
106          if g !=None:            if g !=None:
107             g=util.interpolate(g, self.__pde_p.getFunctionSpaceForCoefficient("Y"))             g=util.interpolate(g, self.__pde_p.getFunctionSpaceForCoefficient("Y"))
108             if g.isEmpty():             if g.isEmpty():
109               g=Vector(0,self.__pde_v.getFunctionSpaceForCoefficient("Y"))               g=Vector(0,self.__pde_v.getFunctionSpaceForCoefficient("Y"))
# Line 118  class DarcyFlow(object): Line 129  class DarcyFlow(object):
129             self.__permeability=perm             self.__permeability=perm
130             self.__pde_p.setValue(A=util.transposed_tensor_mult(self.__permeability,self.__permeability))             self.__pde_p.setValue(A=util.transposed_tensor_mult(self.__permeability,self.__permeability))
131    
132        def setTolerance(self,rtol=1e-4):
133            """
134            sets the relative tolerance C{rtol} used to terminate the solution process. The iteration is terminated if
135    
136            M{|g-v-Qp| <= atol + rtol * min( max( |g-v|, |Qp| ), max( |v|, |g-Qp| ) ) }
137    
138            where C{atol} is an absolut tolerance (see L{setAbsoluteTolerance}), M{|f|^2 = integrate(length(f)^2)} and M{(Qp)_i=k_{ij}p_{,j}} for the permeability M{k_{ij}}.
139    
140      def getFlux(self,p, fixed_flux=Data(),tol=1.e-8, show_details=False):          @param rtol: relative tolerance for the pressure
141            @type rtol: non-negative C{float}
142          """          """
143          returns the flux for a given pressure C{p} where the flux is equal to C{fixed_flux}          if rtol<0:
144          on locations where C{location_of_fixed_flux} is positive (see L{setValue}).              raise ValueError,"Relative tolerance needs to be non-negative."
145          Note that C{g} and C{f} are used, L{setValue}.          self.__rtol=rtol
146                def getTolerance(self):
147          @param p: pressure.          """
148          @type p: scalar value on the domain (e.g. L{Data}).          returns the relative tolerance
149          @param fixed_flux: flux on the locations of the domain marked be C{location_of_fixed_flux}.  
150          @type fixed_flux: vector values on the domain (e.g. L{Data}).          @return: current relative tolerance
151          @param tol: relative tolerance to be used.          @rtype: C{float}
         @type tol: positive float.  
         @return: flux  
         @rtype: L{Data}  
         @note: the method uses the least squares solution M{u=(I+D^*D)^{-1}(D^*f-g-Qp)} where M{D} is the M{div} operator and M{(Qp)_i=k_{ij}p_{,j}}  
                for the permeability M{k_{ij}}  
152          """          """
153          self.__pde_v.setTolerance(tol)          return self.__rtol
154          self.__pde_v.setValue(Y=self.__g, X=self.__f*util.kronecker(self.domain), r=fixed_flux)  
155          return self.__pde_v.getSolution(verbose=show_details)      def setAbsoluteTolerance(self,atol=0.):
156            """
157            sets the absolute tolerance C{atol} used to terminate the solution process. The iteration is terminated if
158    
159            M{|g-v-Qp| <= atol + rtol * min( max( |g-v|, |Qp| ), max( |v|, |g-Qp| ) ) }
160    
161      def solve(self,u0,p0,atol=0,rtol=1e-8, max_iter=100, verbose=False, show_details=False, sub_rtol=1.e-8):          where C{rtol} is an absolut tolerance (see L{setTolerance}), M{|f|^2 = integrate(length(f)^2)} and M{(Qp)_i=k_{ij}p_{,j}} for the permeability M{k_{ij}}.
162           """  
163            @param atol: absolute tolerance for the pressure
164            @type atol: non-negative C{float}
165            """
166            if atol<0:
167                raise ValueError,"Absolute tolerance needs to be non-negative."
168            self.__atol=atol
169        def getAbsoluteTolerance(self):
170           """
171           returns the absolute tolerance
172          
173           @return: current absolute tolerance
174           @rtype: C{float}
175           """
176           return self.__atol
177    
178        def setSubProblemTolerance(self,rtol=None):
179             """
180             Sets the relative tolerance to solve the subproblem(s). If C{rtol} is not present
181             C{self.getTolerance()**2} is used.
182    
183             @param rtol: relative tolerence
184             @type rtol: positive C{float}
185             """
186             if rtol == None:
187                  if self.getTolerance()<=0.:
188                      raise ValueError,"A positive relative tolerance must be set."
189                  self.__sub_tol=max(util.EPSILON**(0.75),self.getTolerance()**2)
190             else:
191                 if rtol<=0:
192                     raise ValueError,"sub-problem tolerance must be positive."
193                 self.__sub_tol=max(util.EPSILON**(0.75),rtol)
194    
195        def getSubProblemTolerance(self):
196             """
197             Returns the subproblem reduction factor.
198    
199             @return: subproblem reduction factor
200             @rtype: C{float}
201             """
202             return self.__sub_tol
203    
204        def solve(self,u0,p0, max_iter=100, verbose=False, show_details=False, max_num_corrections=10):
205             """
206           solves the problem.           solves the problem.
207    
208           The iteration is terminated if the error in the pressure is less then C{rtol * |q| + atol} where           The iteration is terminated if the residual norm is less then self.getTolerance().
          C{|q|} denotes the norm of the right hand side (see escript user's guide for details).  
209    
210           @param u0: initial guess for the flux. At locations in the domain marked by C{location_of_fixed_flux} the value of C{u0} is kept unchanged.           @param u0: initial guess for the flux. At locations in the domain marked by C{location_of_fixed_flux} the value of C{u0} is kept unchanged.
211           @type u0: vector value on the domain (e.g. L{Data}).           @type u0: vector value on the domain (e.g. L{Data}).
212           @param p0: initial guess for the pressure. At locations in the domain marked by C{location_of_fixed_pressure} the value of C{p0} is kept unchanged.           @param p0: initial guess for the pressure. At locations in the domain marked by C{location_of_fixed_pressure} the value of C{p0} is kept unchanged.
213           @type p0: scalar value on the domain (e.g. L{Data}).           @type p0: scalar value on the domain (e.g. L{Data}).
          @param atol: absolute tolerance for the pressure  
          @type atol: non-negative C{float}  
          @param rtol: relative tolerance for the pressure  
          @type rtol: non-negative C{float}  
          @param sub_rtol: tolerance to be used in the sub iteration. It is recommended that M{sub_rtol<rtol*5.e-3}  
          @type sub_rtol: positive-negative C{float}  
214           @param verbose: if set some information on iteration progress are printed           @param verbose: if set some information on iteration progress are printed
215           @type verbose: C{bool}           @type verbose: C{bool}
216           @param show_details:  if set information on the subiteration process are printed.           @param show_details:  if set information on the subiteration process are printed.
217           @type show_details: C{bool}           @type show_details: C{bool}
218           @return: flux and pressure           @return: flux and pressure
219           @rtype: C{tuple} of L{Data}.           @rtype: C{tuple} of L{Data}.
220    
221           @note: The problem is solved as a least squares form           @note: The problem is solved as a least squares form
222    
223           M{(I+D^*D)u+Qp=D^*f+g}           M{(I+D^*D)u+Qp=D^*f+g}
224           M{Q^*u+Q^*Qp=Q^*g}           M{Q^*u+Q^*Qp=Q^*g}
225    
226           where M{D} is the M{div} operator and M{(Qp)_i=k_{ij}p_{,j}} for the permeability M{k_{ij}}.           where M{D} is the M{div} operator and M{(Qp)_i=k_{ij}p_{,j}} for the permeability M{k_{ij}}.
227           We eliminate the flux form the problem by setting           We eliminate the flux form the problem by setting
228    
229           M{u=(I+D^*D)^{-1}(D^*f-g-Qp)} with u=u0 on location_of_fixed_flux           M{u=(I+D^*D)^{-1}(D^*f-g-Qp)} with u=u0 on location_of_fixed_flux
230    
231           form the first equation. Inserted into the second equation we get           form the first equation. Inserted into the second equation we get
232    
233           M{Q^*(I-(I+D^*D)^{-1})Qp= Q^*(g-(I+D^*D)^{-1}(D^*f+g))} with p=p0  on location_of_fixed_pressure           M{Q^*(I-(I+D^*D)^{-1})Qp= Q^*(g-(I+D^*D)^{-1}(D^*f+g))} with p=p0  on location_of_fixed_pressure
234            
235           which is solved using the PCG method (precondition is M{Q^*Q}). In each iteration step           which is solved using the PCG method (precondition is M{Q^*Q}). In each iteration step
236           PDEs with operator M{I+D^*D} and with M{Q^*Q} needs to be solved using a sub iteration scheme.           PDEs with operator M{I+D^*D} and with M{Q^*Q} needs to be solved using a sub iteration scheme.
237           """           """
238           self.verbose=verbose           self.verbose=verbose
239           self.show_details= show_details and self.verbose           self.show_details= show_details and self.verbose
240           self.__pde_v.setTolerance(sub_rtol)           rtol=self.getTolerance()
241           self.__pde_p.setTolerance(sub_rtol)           atol=self.getAbsoluteTolerance()
242           u2=u0*self.__pde_v.getCoefficient("q")           if self.verbose: print "DarcyFlux: initial sub tolerance = %e"%self.getSubProblemTolerance()
243           #  
244           # first the reference velocity is calculated from           num_corrections=0
245           #           converged=False
246           #   (I+D^*D)u_ref=D^*f+g (including bundray conditions for u)           p=p0
247           #           norm_r=None
248           self.__pde_v.setValue(Y=self.__g, X=self.__f*util.kronecker(self.domain), r=u0)           while not converged:
249           u_ref=self.__pde_v.getSolution(verbose=show_details)                 v=self.getFlux(p, fixed_flux=u0, show_details=self.show_details)
250           if self.verbose: print "DarcyFlux: maximum reference flux = ",util.Lsup(u_ref)                 Qp=self.__Q(p)
251           self.__pde_v.setValue(r=Data())                 norm_v=self.__L2(v)
252           #                 norm_Qp=self.__L2(Qp)
253           #   and then we calculate a reference pressure                 if norm_v == 0.:
254           #                    if norm_Qp == 0.:
255           #       Q^*Qp_ref=Q^*g-Q^*u_ref ((including bundray conditions for p)                       return v,p
256           #                    else:
257           self.__pde_p.setValue(X=util.transposed_tensor_mult(self.__permeability,(self.__g-u_ref)), r=p0)                      fac=norm_Qp
258           p_ref=self.__pde_p.getSolution(verbose=self.show_details)                 else:
259           if self.verbose: print "DarcyFlux: maximum reference pressure = ",util.Lsup(p_ref)                    if norm_Qp == 0.:
260           self.__pde_p.setValue(r=Data())                      fac=norm_v
261           #                    else:
262           #   (I+D^*D)du + Qdp = - Qp_ref                       u=du+u_ref                      fac=2./(1./norm_v+1./norm_Qp)
263           #   Q^*du + Q^*Qdp = Q^*g-Q^*u_ref-Q^*Qp_ref=0        p=dp+pref                 ATOL=(atol+rtol*fac)
264           #                 if self.verbose:
265           #      du= -(I+D^*D)^(-1} Q(p_ref+dp)  u = u_ref+du                      print "DarcyFlux: L2 norm of v = %e."%norm_v
266           #                      print "DarcyFlux: L2 norm of k.grad(p) = %e."%norm_Qp
267           #  => Q^*(I-(I+D^*D)^(-1})Q dp = Q^*(I+D^*D)^(-1} Qp_ref                      print "DarcyFlux: absolute tolerance ATOL = %e."%ATOL
268           #  or Q^*(I-(I+D^*D)^(-1})Q p = Q^*Qp_ref                 if norm_r == None or norm_r>ATOL:
269           #                     if num_corrections>max_num_corrections:
270           #   r= Q^*( (I+D^*D)^(-1} Qp_ref - Q dp + (I+D^*D)^(-1})Q dp) = Q^*(-du-Q dp)                           raise ValueError,"maximum number of correction steps reached."
271           #            with du=-(I+D^*D)^(-1} Q(p_ref+dp)                     p,r, norm_r=PCG(self.__g-util.interpolate(v,Function(self.domain))-Qp,self.__Aprod,p,self.__Msolve_PCG,self.__inner_PCG,atol=0.5*ATOL, rtol=0.,iter_max=max_iter, verbose=self.verbose)
272           #                     num_corrections+=1
273           #  we use the (du,Qdp) to represent the resudual                 else:
274           #  Q^*Q is a preconditioner                     converged=True
275           #             return v,p
276           #  <(Q^*Q)^{-1}r,r> -> right hand side norm is <Qp_ref,Qp_ref>      def __L2(self,v):
277           #           return util.sqrt(util.integrate(util.length(util.interpolate(v,Function(self.domain)))**2))
          Qp_ref=util.tensor_mult(self.__permeability,util.grad(p_ref))  
          norm_rhs=util.sqrt(util.integrate(util.inner(Qp_ref,Qp_ref)))  
          ATOL=max(norm_rhs*rtol +atol, 200. * util.EPSILON * norm_rhs)  
          if not ATOL>0:  
              raise ValueError,"Negative absolute tolerance (rtol = %e, norm right hand side =%, atol =%e)."%(rtol, norm_rhs, atol)  
          if self.verbose: print "DarcyFlux: norm of right hand side = %e (absolute tolerance = %e)"%(norm_rhs,ATOL)  
          #  
          #   caclulate the initial residual  
          #  
          self.__pde_v.setValue(X=Data(), Y=-util.tensor_mult(self.__permeability,util.grad(p0)), r=Data())  
          du=self.__pde_v.getSolution(verbose=show_details)  
          r=ArithmeticTuple(util.tensor_mult(self.__permeability,util.grad(p0-p_ref)), du)  
          dp,r=PCG(r,self.__Aprod_PCG,p0,self.__Msolve_PCG,self.__inner_PCG,atol=ATOL, rtol=0.,iter_max=max_iter, verbose=self.verbose)  
          util.saveVTK("d.vtu",p=dp,p_ref=p_ref)  
          return u_ref+r[1],dp  
           
     def __Aprod_PCG(self,p):  
           if self.show_details: print "DarcyFlux: Applying operator"  
           Qp=util.tensor_mult(self.__permeability,util.grad(p))  
           self.__pde_v.setValue(Y=Qp,X=Data())  
           w=self.__pde_v.getSolution(verbose=self.show_details)  
           return ArithmeticTuple(-Qp,w)  
278    
279        def __Q(self,p):
280              return util.tensor_mult(self.__permeability,util.grad(p))
281    
282        def __Aprod(self,dp):
283              self.__pde_v.setTolerance(self.getSubProblemTolerance())
284              if self.show_details: print "DarcyFlux: Applying operator"
285              Qdp=self.__Q(dp)
286              self.__pde_v.setValue(Y=-Qdp,X=Data(), r=Data())
287              du=self.__pde_v.getSolution(verbose=self.show_details, iter_max = 100000)
288              # self.__pde_v.getOperator().saveMM("proj.mm")
289              return Qdp+du
290        def __inner_GMRES(self,r,s):
291             return util.integrate(util.inner(r,s))
292    
293      def __inner_PCG(self,p,r):      def __inner_PCG(self,p,r):
294           a=util.tensor_mult(self.__permeability,util.grad(p))           return util.integrate(util.inner(self.__Q(p), r))
          out=-util.integrate(util.inner(a,r[0]+r[1]))  
          return out  
295    
296      def __Msolve_PCG(self,r):      def __Msolve_PCG(self,r):
297              self.__pde_p.setTolerance(self.getSubProblemTolerance())
298            if self.show_details: print "DarcyFlux: Applying preconditioner"            if self.show_details: print "DarcyFlux: Applying preconditioner"
299            self.__pde_p.setValue(X=-util.transposed_tensor_mult(self.__permeability,r[0]+r[1]))            self.__pde_p.setValue(X=util.transposed_tensor_mult(self.__permeability,r), Y=Data(), r=Data())
300            return self.__pde_p.getSolution(verbose=self.show_details)            # self.__pde_p.getOperator().saveMM("prec.mm")
301              return self.__pde_p.getSolution(verbose=self.show_details, iter_max = 100000)
302    
303        def getFlux(self,p=None, fixed_flux=Data(), show_details=False):
304            """
305            returns the flux for a given pressure C{p} where the flux is equal to C{fixed_flux}
306            on locations where C{location_of_fixed_flux} is positive (see L{setValue}).
307            Note that C{g} and C{f} are used, see L{setValue}.
308    
309            @param p: pressure.
310            @type p: scalar value on the domain (e.g. L{Data}).
311            @param fixed_flux: flux on the locations of the domain marked be C{location_of_fixed_flux}.
312            @type fixed_flux: vector values on the domain (e.g. L{Data}).
313            @param tol: relative tolerance to be used.
314            @type tol: positive C{float}.
315            @return: flux
316            @rtype: L{Data}
317            @note: the method uses the least squares solution M{u=(I+D^*D)^{-1}(D^*f-g-Qp)} where M{D} is the M{div} operator and M{(Qp)_i=k_{ij}p_{,j}}
318                   for the permeability M{k_{ij}}
319            """
320            self.__pde_v.setTolerance(self.getSubProblemTolerance())
321            g=self.__g
322            f=self.__f
323            self.__pde_v.setValue(X=self.__l*f*util.kronecker(self.domain), r=fixed_flux)
324            if p == None:
325               self.__pde_v.setValue(Y=g)
326            else:
327               self.__pde_v.setValue(Y=g-self.__Q(p))
328            return self.__pde_v.getSolution(verbose=show_details, iter_max=100000)
329    
330  class StokesProblemCartesian(HomogeneousSaddlePointProblem):  class StokesProblemCartesian(HomogeneousSaddlePointProblem):
331        """       """
332        solves       solves
333    
334            -(eta*(u_{i,j}+u_{j,i}))_j + p_i = f_i-stress_{ij,j}            -(eta*(u_{i,j}+u_{j,i}))_j + p_i = f_i-stress_{ij,j}
335                  u_{i,i}=0                  u_{i,i}=0
# Line 264  class StokesProblemCartesian(Homogeneous Line 337  class StokesProblemCartesian(Homogeneous
337            u=0 where  fixed_u_mask>0            u=0 where  fixed_u_mask>0
338            eta*(u_{i,j}+u_{j,i})*n_j-p*n_i=surface_stress +stress_{ij}n_j            eta*(u_{i,j}+u_{j,i})*n_j-p*n_i=surface_stress +stress_{ij}n_j
339    
340        if surface_stress is not given 0 is assumed.       if surface_stress is not given 0 is assumed.
341    
342        typical usage:       typical usage:
343    
344              sp=StokesProblemCartesian(domain)              sp=StokesProblemCartesian(domain)
345              sp.setTolerance()              sp.setTolerance()
346              sp.initialize(...)              sp.initialize(...)
347              v,p=sp.solve(v0,p0)              v,p=sp.solve(v0,p0)
348        """       """
349        def __init__(self,domain,**kwargs):       def __init__(self,domain,**kwargs):
350           """           """
351           initialize the Stokes Problem           initialize the Stokes Problem
352    
# Line 287  class StokesProblemCartesian(Homogeneous Line 360  class StokesProblemCartesian(Homogeneous
360           self.__pde_u=LinearPDE(domain,numEquations=self.domain.getDim(),numSolutions=self.domain.getDim())           self.__pde_u=LinearPDE(domain,numEquations=self.domain.getDim(),numSolutions=self.domain.getDim())
361           self.__pde_u.setSymmetryOn()           self.__pde_u.setSymmetryOn()
362           # self.__pde_u.setSolverMethod(self.__pde_u.DIRECT)           # self.__pde_u.setSolverMethod(self.__pde_u.DIRECT)
363           # self.__pde_u.setSolverMethod(preconditioner=LinearPDE.RILU)           # self.__pde_u.setSolverMethod(preconditioner=LinearPDE.ILU0)
364                
365           self.__pde_prec=LinearPDE(domain)           self.__pde_prec=LinearPDE(domain)
366           self.__pde_prec.setReducedOrderOn()           self.__pde_prec.setReducedOrderOn()
367           # self.__pde_prec.setSolverMethod(self.__pde_prec.LUMPING)           # self.__pde_prec.setSolverMethod(self.__pde_prec.LUMPING)
# Line 296  class StokesProblemCartesian(Homogeneous Line 369  class StokesProblemCartesian(Homogeneous
369    
370           self.__pde_proj=LinearPDE(domain)           self.__pde_proj=LinearPDE(domain)
371           self.__pde_proj.setReducedOrderOn()           self.__pde_proj.setReducedOrderOn()
372         self.__pde_proj.setValue(D=1)
373           self.__pde_proj.setSymmetryOn()           self.__pde_proj.setSymmetryOn()
          self.__pde_proj.setValue(D=1.)  
374    
375        def initialize(self,f=Data(),fixed_u_mask=Data(),eta=1,surface_stress=Data(),stress=Data()):  
376         def initialize(self,f=Data(),fixed_u_mask=Data(),eta=1,surface_stress=Data(),stress=Data()):
377          """          """
378          assigns values to the model parameters          assigns values to the model parameters
379    
# Line 314  class StokesProblemCartesian(Homogeneous Line 388  class StokesProblemCartesian(Homogeneous
388          @param stress: initial stress          @param stress: initial stress
389      @type stress: L{Tensor} object on L{FunctionSpace} L{Function} or similar      @type stress: L{Tensor} object on L{FunctionSpace} L{Function} or similar
390          @note: All values needs to be set.          @note: All values needs to be set.
391    
392          """          """
393          self.eta=eta          self.eta=eta
394          A =self.__pde_u.createCoefficient("A")          A =self.__pde_u.createCoefficient("A")
395      self.__pde_u.setValue(A=Data())      self.__pde_u.setValue(A=Data())
396          for i in range(self.domain.getDim()):          for i in range(self.domain.getDim()):
397          for j in range(self.domain.getDim()):          for j in range(self.domain.getDim()):
398              A[i,j,j,i] += 1.              A[i,j,j,i] += 1.
399              A[i,j,i,j] += 1.              A[i,j,i,j] += 1.
400      self.__pde_prec.setValue(D=1/self.eta)      self.__pde_prec.setValue(D=1/self.eta)
401          self.__pde_u.setValue(A=A*self.eta,q=fixed_u_mask,Y=f,y=surface_stress)          self.__pde_u.setValue(A=A*self.eta,q=fixed_u_mask)
402            self.__f=f
403            self.__surface_stress=surface_stress
404          self.__stress=stress          self.__stress=stress
405    
406        def B(self,v):       def Bv(self,v):
407          """           """
408          returns div(v)           returns inner product of element p and div(v)
         @rtype: equal to the type of p  
409    
410          @note: boundary conditions on p should be zero!           @param p: a pressure increment
411          """           @param v: a residual
412          if self.show_details: print "apply divergence:"           @return: inner product of element p and div(v)
         self.__pde_proj.setValue(Y=-util.div(v))  
         self.__pde_proj.setTolerance(self.getSubProblemTolerance())  
         return self.__pde_proj.getSolution(verbose=self.show_details)  
   
       def inner_pBv(self,p,Bv):  
          """  
          returns inner product of element p and Bv  (overwrite)  
           
          @type p: equal to the type of p  
          @type Bv: equal to the type of result of operator B  
413           @rtype: C{float}           @rtype: C{float}
   
          @rtype: equal to the type of p  
414           """           """
415           s0=util.interpolate(p,Function(self.domain))           self.__pde_proj.setValue(Y=-util.div(v))
416           s1=util.interpolate(Bv,Function(self.domain))           self.__pde_prec.setTolerance(self.getSubProblemTolerance())
417           return util.integrate(s0*s1)           return self.__pde_proj.getSolution()
418    
419        def inner_p(self,p0,p1):       def inner_pBv(self,p,Bv):
420           """           """
421           returns inner product of element p0 and p1  (overwrite)           returns inner product of element p and Bv=-div(v)
422            
423           @type p0: equal to the type of p           @param p: a pressure increment
424           @type p1: equal to the type of p           @param v: a residual
425             @return: inner product of element p and Bv=-div(v)
426           @rtype: C{float}           @rtype: C{float}
427             """
428             return util.integrate(util.interpolate(p,Function(self.domain))*util.interpolate(Bv,Function(self.domain)))
429    
430         def inner_p(self,p0,p1):
431             """
432             Returns inner product of p0 and p1
433    
434           @rtype: equal to the type of p           @param p0: a pressure
435             @param p1: a pressure
436             @return: inner product of p0 and p1
437             @rtype: C{float}
438           """           """
439           s0=util.interpolate(p0/self.eta,Function(self.domain))           s0=util.interpolate(p0/self.eta,Function(self.domain))
440           s1=util.interpolate(p1/self.eta,Function(self.domain))           s1=util.interpolate(p1/self.eta,Function(self.domain))
441           return util.integrate(s0*s1)           return util.integrate(s0*s1)
442    
443        def inner_v(self,v0,v1):       def norm_v(self,v):
444           """           """
445           returns inner product of two element v0 and v1  (overwrite)           returns the norm of v
           
          @type v0: equal to the type of v  
          @type v1: equal to the type of v  
          @rtype: C{float}  
446    
447           @rtype: equal to the type of v           @param v: a velovity
448             @return: norm of v
449             @rtype: non-negative C{float}
450           """           """
451       gv0=util.grad(v0)           return util.sqrt(util.integrate(util.length(util.grad(v))))
      gv1=util.grad(v1)  
          return util.integrate(util.inner(gv0,gv1))  
452    
453        def solve_A(self,u,p):       def getV(self, p, v0):
454           """           """
455           solves Av=f-Au-B^*p (v=0 on fixed_u_mask)           return the value for v for a given p (overwrite)
456    
457             @param p: a pressure
458             @param v0: a initial guess for the value v to return.
459             @return: v given as M{v= A^{-1} (f-B^*p)}
460           """           """
          if self.show_details: print "solve for velocity:"  
461           self.__pde_u.setTolerance(self.getSubProblemTolerance())           self.__pde_u.setTolerance(self.getSubProblemTolerance())
462             self.__pde_u.setValue(Y=self.__f, y=self.__surface_stress, r=v0)
463           if self.__stress.isEmpty():           if self.__stress.isEmpty():
464              self.__pde_u.setValue(X=-2*self.eta*util.symmetric(util.grad(u))+p*util.kronecker(self.domain))              self.__pde_u.setValue(X=p*util.kronecker(self.domain))
465           else:           else:
466              self.__pde_u.setValue(X=self.__stress-2*self.eta*util.symmetric(util.grad(u))+p*util.kronecker(self.domain))              self.__pde_u.setValue(X=self.__stress+p*util.kronecker(self.domain))
467           out=self.__pde_u.getSolution(verbose=self.show_details)           out=self.__pde_u.getSolution(verbose=self.show_details)
468           return  out           return  out
469    
470        def solve_prec(self,p):       def norm_Bv(self,Bv):
471           if self.show_details: print "apply preconditioner:"          """
472            Returns Bv (overwrite).
473    
474            @rtype: equal to the type of p
475            @note: boundary conditions on p should be zero!
476            """
477            return util.sqrt(util.integrate(util.interpolate(Bv,Function(self.domain))**2))
478    
479         def solve_AinvBt(self,p):
480             """
481             Solves M{Av=B^*p} with accuracy L{self.getSubProblemTolerance()}
482    
483             @param p: a pressure increment
484             @return: the solution of M{Av=B^*p}
485             @note: boundary conditions on v should be zero!
486             """
487             self.__pde_u.setTolerance(self.getSubProblemTolerance())
488             self.__pde_u.setValue(Y=Data(), y=Data(), r=Data(),X=-p*util.kronecker(self.domain))
489             out=self.__pde_u.getSolution(verbose=self.show_details)
490             return  out
491    
492         def solve_prec(self,Bv):
493             """
494             applies preconditioner for for M{BA^{-1}B^*} to M{Bv}
495             with accuracy L{self.getSubProblemTolerance()}
496    
497             @param v: velocity increment
498             @return: M{p=P(Bv)} where M{P^{-1}} is an approximation of M{BA^{-1}B^*}
499             @note: boundary conditions on p are zero.
500             """
501             self.__pde_prec.setValue(Y=Bv)
502           self.__pde_prec.setTolerance(self.getSubProblemTolerance())           self.__pde_prec.setTolerance(self.getSubProblemTolerance())
503           self.__pde_prec.setValue(Y=p)           return self.__pde_prec.getSolution(verbose=self.show_details)
504           q=self.__pde_prec.getSolution(verbose=self.show_details)  
505           return q  # rename solve_prec and change argument v to Bv
506    # chnage the argument of inner_pBv to v->Bv
507    # add Bv
508    # inner p still needed?
509    # change norm_Bv argument to Bv

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