# Diff of /trunk/escript/py_src/flows.py

revision 2263 by gross, Fri Feb 6 06:50:39 2009 UTC revision 2264 by gross, Wed Feb 11 06:48:28 2009 UTC
# 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      """      """
# Line 64  class DarcyFlow(object): Line 64  class DarcyFlow(object):
64          if useReduced: self.__pde_p.setReducedOrderOn()          if useReduced: self.__pde_p.setReducedOrderOn()
65          self.__f=Scalar(0,self.__pde_v.getFunctionSpaceForCoefficient("X"))          self.__f=Scalar(0,self.__pde_v.getFunctionSpaceForCoefficient("X"))
66          self.__g=Vector(0,self.__pde_v.getFunctionSpaceForCoefficient("Y"))          self.__g=Vector(0,self.__pde_v.getFunctionSpaceForCoefficient("Y"))
67          self.__ATOL= None          self.setTolerance()
68            self.setAbsoluteTolerance()
69            self.setSubProblemTolerance()
70
71      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):
72          """          """
# Line 78  class DarcyFlow(object): Line 80  class DarcyFlow(object):
80          @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})
81          @param location_of_fixed_flux:  mask for locations where flux is fixed.          @param location_of_fixed_flux:  mask for locations where flux is fixed.
82          @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})
83          @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
84                               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
85                               C{v} on the main diagonal is used.                               C{v} on the main diagonal is used.
86          @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})
87
# Line 88  class DarcyFlow(object): Line 90  class DarcyFlow(object):
90                 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
91                 is along the M{x_i} axis.                 is along the M{x_i} axis.
92          """          """
93          if f !=None:          if f !=None:
94             f=util.interpolate(f, self.__pde_v.getFunctionSpaceForCoefficient("X"))             f=util.interpolate(f, self.__pde_v.getFunctionSpaceForCoefficient("X"))
95             if f.isEmpty():             if f.isEmpty():
96                 f=Scalar(0,self.__pde_v.getFunctionSpaceForCoefficient("X"))                 f=Scalar(0,self.__pde_v.getFunctionSpaceForCoefficient("X"))
97             else:             else:
98                 if f.getRank()>0: raise ValueError,"illegal rank of f."                 if f.getRank()>0: raise ValueError,"illegal rank of f."
99             self.f=f             self.f=f
100          if g !=None:            if g !=None:
101             g=util.interpolate(g, self.__pde_p.getFunctionSpaceForCoefficient("Y"))             g=util.interpolate(g, self.__pde_p.getFunctionSpaceForCoefficient("Y"))
102             if g.isEmpty():             if g.isEmpty():
103               g=Vector(0,self.__pde_v.getFunctionSpaceForCoefficient("Y"))               g=Vector(0,self.__pde_v.getFunctionSpaceForCoefficient("Y"))
# Line 121  class DarcyFlow(object): Line 123  class DarcyFlow(object):
123             self.__permeability=perm             self.__permeability=perm
124             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))
125
126        def setTolerance(self,rtol=1e-4):
def getFlux(self,p=None, fixed_flux=Data(),tol=1.e-8, show_details=False):
"""
returns the flux for a given pressure C{p} where the flux is equal to C{fixed_flux}
on locations where C{location_of_fixed_flux} is positive (see L{setValue}).
Note that C{g} and C{f} are used, see L{setValue}.

@param p: pressure.
@type p: scalar value on the domain (e.g. L{Data}).
@param fixed_flux: flux on the locations of the domain marked be C{location_of_fixed_flux}.
@type fixed_flux: vector values on the domain (e.g. L{Data}).
@param tol: relative tolerance to be used.
@type tol: positive C{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}}
127          """          """
128          self.__pde_v.setTolerance(tol)          sets the relative tolerance C{rtol} used to terminate the solution process. The iteration is terminated if
129          g=self.__g
130          f=self.__f          M{|g-v-Qp| <= atol + rtol * min( max( |g-v|, |Qp| ), max( |v|, |g-Qp| ) ) }
131          self.__pde_v.setValue(X=f*util.kronecker(self.domain), r=fixed_flux)
132          if p == None:          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}}.
self.__pde_v.setValue(Y=g)
else:
self.__pde_v.setValue(Y=g-util.tensor_mult(self.__permeability,util.grad(p)))
return self.__pde_v.getSolution(verbose=show_details)
133
134      def getPressure(self,v=None, fixed_pressure=Data(),tol=1.e-8, show_details=False):          @param rtol: relative tolerance for the pressure
135            @type rtol: non-negative C{float}
136          """          """
137          returns the pressure for a given flux C{v} where the pressure is equal to C{fixed_pressure}          if rtol<0:
138          on locations where C{location_of_fixed_pressure} is positive (see L{setValue}).              raise ValueError,"Relative tolerance needs to be non-negative."
139          Note that C{g} is used, see L{setValue}.          self.__rtol=rtol
140                def getTolerance(self):
@param v: flux.
@type v: vector-valued on the domain (e.g. L{Data}).
@param fixed_pressure: pressure on the locations of the domain marked be C{location_of_fixed_pressure}.
@type fixed_pressure: vector values on the domain (e.g. L{Data}).
@param tol: relative tolerance to be used.
@type tol: positive C{float}.
@return: pressure
@rtype: L{Data}
@note: the method uses the least squares solution M{p=(Q^*Q)^{-1}Q^*(g-u)} where and M{(Qp)_i=k_{ij}p_{,j}}
for the permeability M{k_{ij}}
141          """          """
142          self.__pde_v.setTolerance(tol)          returns the relative tolerance
g=self.__g
self.__pde_p.setValue(r=fixed_pressure)
if v == None:
self.__pde_p.setValue(X=util.transposed_tensor_mult(self.__permeability,g-v))
else:
self.__pde_p.setValue(X=util.transposed_tensor_mult(self.__permeability,g))
return self.__pde_p.getSolution(verbose=show_details)
143
144      def setTolerance(self,atol=0,rtol=1e-8,p_ref=None,v_ref=None):          @return: current relative tolerance
145            @rtype: C{float}
146          """          """
147          set the tolerance C{ATOL} used to terminate the solution process. It is used          return self.__rtol

M{ATOL = atol + rtol * max( |g-v_ref|, |Qp_ref| )}

where M{|f|^2 = integrate(length(f)^2)} and M{(Qp)_i=k_{ij}p_{,j}} for the permeability M{k_{ij}}. If C{v_ref} or C{p_ref} is not present zero is assumed.
148
149          The iteration is terminated if for the current approximation C{p}, flux C{v=(I+D^*D)^{-1}(D^*f-g-Qp)} and their residual      def setAbsoluteTolerance(self,atol=0.):
150            """
151          M{r=Q^*(g-Qp-v)}          sets the absolute tolerance C{atol} used to terminate the solution process. The iteration is terminated if

the condition
152
153          M{<(Q^*Q)^{-1} r,r> <= ATOL}          M{|g-v-Qp| <= atol + rtol * min( max( |g-v|, |Qp| ), max( |v|, |g-Qp| ) ) }
154
155          holds. M{D} is the M{div} operator and M{(Qp)_i=k_{ij}p_{,j}} for the permeability M{k_{ij}}          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}}.
156
157          @param atol: absolute tolerance for the pressure          @param atol: absolute tolerance for the pressure
158          @type atol: non-negative C{float}          @type atol: non-negative C{float}
@param rtol: relative tolerance for the pressure
@type rtol: non-negative C{float}
@param p_ref: reference pressure. If not present zero is used. You may use physical arguments to set a resonable value for C{p_ref}, use the
L{getPressure} method or use  the value from a previous time step.
@type p_ref: scalar value on the domain (e.g. L{Data}).
@param v_ref: reference velocity.  If not present zero is used. You may use physical arguments to set a resonable value for C{v_ref}, use the
L{getFlux} method or use  the value from a previous time step.
@type v_ref: vector-valued on the domain (e.g. L{Data}).
@return: used absolute tolerance.
@rtype: positive C{float}
"""
g=self.__g
if not v_ref == None:
f1=util.integrate(util.length(util.interpolate(g-v_ref,Function(self.domain)))**2)
else:
f1=util.integrate(util.length(util.interpolate(g))**2)
if not p_ref == None:
f2=util.integrate(util.length(util.tensor_mult(self.__permeability,util.grad(p_ref)))**2)
else:
f2=0
self.__ATOL= atol + rtol * util.sqrt(max(f1,f2))
if self.__ATOL<=0:
raise ValueError,"Positive tolerance (=%e) is expected."%self.__ATOL
return self.__ATOL

def getTolerance(self):
159          """          """
160          returns the current tolerance.          if atol<0:
161                  raise ValueError,"Absolute tolerance needs to be non-negative."
162          @return: used absolute tolerance.          self.__atol=atol
163          @rtype: positive C{float}      def getAbsoluteTolerance(self):
164          """         """
165          if self.__ATOL==None:         returns the absolute tolerance
166             raise ValueError,"no tolerance is defined."
167          return self.__ATOL         @return: current absolute tolerance
168           @rtype: C{float}
169           """
170           return self.__atol
171
172        def setSubProblemTolerance(self,rtol=None):
173             """
174             Sets the relative tolerance to solve the subproblem(s). If C{rtol} is not present
175             C{self.getTolerance()**2} is used.
176
177             @param rtol: relative tolerence
178             @type rtol: positive C{float}
179             """
180             if rtol == None:
181                  if self.getTolerance()<=0.:
182                      raise ValueError,"A positive relative tolerance must be set."
183                  self.__sub_tol=max(util.EPSILON**(0.75),self.getTolerance()**2)
184             else:
185                 if rtol<=0:
186                     raise ValueError,"sub-problem tolerance must be positive."
187                 self.__sub_tol=max(util.EPSILON**(0.75),rtol)
188
189        def getSubProblemTolerance(self):
190             """
191             Returns the subproblem reduction factor.
192
193             @return: subproblem reduction factor
194             @rtype: C{float}
195             """
196             return self.__sub_tol
197
198      def solve(self,u0,p0, max_iter=100, verbose=False, show_details=False, sub_rtol=1.e-8):      def solve(self,u0,p0, max_iter=100, verbose=False, show_details=False, max_num_corrections=10):
199           """           """
200           solves the problem.           solves the problem.
201
202           The iteration is terminated if the residual norm is less then self.getTolerance().           The iteration is terminated if the residual norm is less then self.getTolerance().
203
204           @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.
205           @type u0: vector value on the domain (e.g. L{Data}).           @type u0: vector value on the domain (e.g. L{Data}).
206           @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.
207           @type p0: scalar value on the domain (e.g. L{Data}).           @type p0: scalar value on the domain (e.g. L{Data}).
@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}
208           @param verbose: if set some information on iteration progress are printed           @param verbose: if set some information on iteration progress are printed
209           @type verbose: C{bool}           @type verbose: C{bool}
210           @param show_details:  if set information on the subiteration process are printed.           @param show_details:  if set information on the subiteration process are printed.
211           @type show_details: C{bool}           @type show_details: C{bool}
212           @return: flux and pressure           @return: flux and pressure
213           @rtype: C{tuple} of L{Data}.           @rtype: C{tuple} of L{Data}.
214
215           @note: The problem is solved as a least squares form           @note: The problem is solved as a least squares form
216
217           M{(I+D^*D)u+Qp=D^*f+g}           M{(I+D^*D)u+Qp=D^*f+g}
218           M{Q^*u+Q^*Qp=Q^*g}           M{Q^*u+Q^*Qp=Q^*g}
219
220           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}}.
221           We eliminate the flux form the problem by setting           We eliminate the flux form the problem by setting
222
223           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
224
225           form the first equation. Inserted into the second equation we get           form the first equation. Inserted into the second equation we get
226
227           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
228
229           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
230           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.
231           """           """
232           self.verbose=verbose           self.verbose=verbose
233           self.show_details= show_details and self.verbose           self.show_details= show_details and self.verbose
234           self.__pde_v.setTolerance(sub_rtol)           rtol=self.getTolerance()
235           self.__pde_p.setTolerance(sub_rtol)           atol=self.getAbsoluteTolerance()
236           ATOL=self.getTolerance()           if self.verbose: print "DarcyFlux: initial sub tolerance = %e"%self.getSubProblemTolerance()
237           if self.verbose: print "DarcyFlux: absolute tolerance = %e"%ATOL
238           #########################################################################################################################           num_corrections=0
239           #           converged=False
240           #   we solve:           p=p0
241           #             norm_r=None
242           #      Q^*(I-(I+D^*D)^{-1})Q dp =  Q^* (g-u0-Qp0 - (I+D^*D)^{-1} ( D^*(f-Du0)+g-u0-Qp0) )           while not converged:
243           #                 v=self.getFlux(p, fixed_flux=u0, show_details=self.show_details)
244           #   residual is                 Qp=self.__Q(p)
245           #                 norm_v=self.__L2(v)
246           #    r=  Q^* (g-u0-Qp0 - (I+D^*D)^{-1} ( D^*(f-Du0)+g-u0-Qp0) - Q dp +(I+D^*D)^{-1})Q dp ) = Q^* (g - Qp - v)                 norm_Qp=self.__L2(Qp)
247           #                 if norm_v == 0.:
248           #        with v = (I+D^*D)^{-1} (D^*f+g-Qp) including BC                    if norm_Qp == 0.:
249           #                       return v,p
250           #    we use (g - Qp, v) to represent the residual. not that                    else:
251           #                      fac=norm_Qp
252           #    dr(dp)=( -Q(dp), dv) with dv = - (I+D^*D)^{-1} Q(dp)                 else:
253           #                    if norm_Qp == 0.:
254           #   while the initial residual is                      fac=norm_v
255           #                    else:
256           #      r0=( g - Qp0, v00) with v00=(I+D^*D)^{-1} (D^*f+g-Qp0) including BC                      fac=2./(1./norm_v+1./norm_Qp)
257           #                   ATOL=(atol+rtol*fac)
258           d0=self.__g-util.tensor_mult(self.__permeability,util.grad(p0))                 if self.verbose:
259           self.__pde_v.setValue(Y=d0, X=self.__f*util.kronecker(self.domain), r=u0)                      print "DarcyFlux: L2 norm of v = %e."%norm_v
260           v00=self.__pde_v.getSolution(verbose=show_details)                      print "DarcyFlux: L2 norm of k.grad(p) = %e."%norm_Qp
261           if self.verbose: print "DarcyFlux: range of initial flux = ",util.inf(v00), util.sup(v00)                      print "DarcyFlux: absolute tolerance ATOL = %e."%ATOL
262           self.__pde_v.setValue(r=Data())                 if norm_r == None or norm_r>ATOL:
263           # start CG                     if num_corrections>max_num_corrections:
264           r=ArithmeticTuple(d0, v00)                           raise ValueError,"maximum number of correction steps reached."
265           p,r=PCG(r,self.__Aprod_PCG,p0,self.__Msolve_PCG,self.__inner_PCG,atol=ATOL, rtol=0.,iter_max=max_iter, verbose=self.verbose)                     p,r, norm_r=PCG(self.__g-util.interpolate(v,Function(self.domain))-Qp,self.__Aprod,p,self.__Msolve_PCG,self.__inner_PCG,atol=0.1*ATOL, rtol=0.,iter_max=max_iter, verbose=self.verbose)
266           return r[1],p                     num_corrections+=1
267                   else:
268                       converged=True
269             return v,p
270    #
271    #
272    #               r_hat=g-util.interpolate(v,Function(self.domain))-Qp
273    #               #===========================================================================
274    #               norm_r_hat=self.__L2(r_hat)
275    #               norm_v=self.__L2(v)
276    #               norm_g=self.__L2(g)
277    #               norm_gv=self.__L2(g-v)
278    #               norm_Qp=self.__L2(Qp)
279    #               norm_gQp=self.__L2(g-Qp)
280    #               fac=min(max(norm_v,norm_gQp),max(norm_Qp,norm_gv))
281    #               fac=min(norm_v,norm_Qp,norm_gv)
282    #               norm_r_hat_PCG=util.sqrt(self.__inner_PCG(self.__Msolve_PCG(r_hat),r_hat))
283    #               print "norm_r_hat = ",norm_r_hat,norm_r_hat_PCG, norm_r_hat_PCG/norm_r_hat
284    #               if r!=None:
285    #                   print "diff = ",self.__L2(r-r_hat)/norm_r_hat
286    #                   sub_tol=min(rtol/self.__L2(r-r_hat)*norm_r_hat,1.)*self.getSubProblemTolerance()
287    #                   self.setSubProblemTolerance(sub_tol)
288    #                   print "subtol_new=",self.getSubProblemTolerance()
289    #               print "norm_v = ",norm_v
290    #               print "norm_gv = ",norm_gv
291    #               print "norm_Qp = ",norm_Qp
292    #               print "norm_gQp = ",norm_gQp
293    #               print "norm_g = ",norm_g
294    #               print "max(norm_v,norm_gQp)=",max(norm_v,norm_gQp)
295    #               print "max(norm_Qp,norm_gv)=",max(norm_Qp,norm_gv)
296    #               if fac == 0:
297    #                   if self.verbose: print "DarcyFlux: trivial case!"
298    #                   return v,p
299    #               #===============================================================================
300    #               # norm_v=util.sqrt(self.__inner_PCG(self.__Msolve_PCG(v),v))
301    #               # norm_Qp=self.__L2(Qp)
302    #               norm_r_hat=util.sqrt(self.__inner_PCG(self.__Msolve_PCG(r_hat),r_hat))
303    #               # print "**** norm_v, norm_Qp :",norm_v,norm_Qp
304    #
305    #               ATOL=(atol+rtol*2./(1./norm_v+1./norm_Qp))
306    #               if self.verbose:
307    #                   print "DarcyFlux: residual = %e"%norm_r_hat
308    #                   print "DarcyFlux: absolute tolerance ATOL = %e."%ATOL
309    #               if norm_r_hat <= ATOL:
310    #                   print "DarcyFlux: iteration finalized."
311    #                   converged=True
312    #               else:
313    #                   # p=GMRES(r_hat,self.__Aprod, p, self.__inner_GMRES, atol=ATOL, rtol=0., iter_max=max_iter, iter_restart=20, verbose=self.verbose,P_R=self.__Msolve_PCG)
314    #                   # p,r=PCG(r_hat,self.__Aprod,p,self.__Msolve_PCG,self.__inner_PCG,atol=ATOL*min(0.1,norm_r_hat_PCG/norm_r_hat), rtol=0.,iter_max=max_iter, verbose=self.verbose)
315    #                   p,r, norm_r=PCG(r_hat,self.__Aprod,p,self.__Msolve_PCG,self.__inner_PCG,atol=0.1*ATOL, rtol=0.,iter_max=max_iter, verbose=self.verbose)
316    #               print "norm_r =",norm_r
317    #         return v,p
318        def __L2(self,v):
319             return util.sqrt(util.integrate(util.length(util.interpolate(v,Function(self.domain)))**2))
320
321        def __Q(self,p):
322              return util.tensor_mult(self.__permeability,util.grad(p))
323
324      def __Aprod_PCG(self,dp):      def __Aprod(self,dp):
325              self.__pde_v.setTolerance(self.getSubProblemTolerance())
326            if self.show_details: print "DarcyFlux: Applying operator"            if self.show_details: print "DarcyFlux: Applying operator"
327            #  -dr(dp) = (Qdp,du) where du = (I+D^*D)^{-1} (Qdp)            Qdp=self.__Q(dp)
328            mQdp=util.tensor_mult(self.__permeability,util.grad(dp))            self.__pde_v.setValue(Y=-Qdp,X=Data(), r=Data())
self.__pde_v.setValue(Y=mQdp,X=Data(), r=Data())
329            du=self.__pde_v.getSolution(verbose=self.show_details)            du=self.__pde_v.getSolution(verbose=self.show_details)
330            return ArithmeticTuple(mQdp,du)            return Qdp+du
331        def __inner_GMRES(self,r,s):
332             return util.integrate(util.inner(r,s))
333
334      def __inner_PCG(self,p,r):      def __inner_PCG(self,p,r):
335           a=util.tensor_mult(self.__permeability,util.grad(p))           return util.integrate(util.inner(self.__Q(p), r))
f0=util.integrate(util.inner(a,r[0]))
f1=util.integrate(util.inner(a,r[1]))
# print "__inner_PCG:",f0,f1,"->",f0-f1
return f0-f1
336
337      def __Msolve_PCG(self,r):      def __Msolve_PCG(self,r):
338              self.__pde_p.setTolerance(self.getSubProblemTolerance())
339            if self.show_details: print "DarcyFlux: Applying preconditioner"            if self.show_details: print "DarcyFlux: Applying preconditioner"
340            self.__pde_p.setValue(X=util.transposed_tensor_mult(self.__permeability,r[0]-r[1]), r=Data())            self.__pde_p.setValue(X=util.transposed_tensor_mult(self.__permeability,r), Y=Data(), r=Data())
341            return self.__pde_p.getSolution(verbose=self.show_details)            return self.__pde_p.getSolution(verbose=self.show_details)
342
343
344        def getFlux(self,p=None, fixed_flux=Data(), show_details=False):
345            """
346            returns the flux for a given pressure C{p} where the flux is equal to C{fixed_flux}
347            on locations where C{location_of_fixed_flux} is positive (see L{setValue}).
348            Note that C{g} and C{f} are used, see L{setValue}.
349
350            @param p: pressure.
351            @type p: scalar value on the domain (e.g. L{Data}).
352            @param fixed_flux: flux on the locations of the domain marked be C{location_of_fixed_flux}.
353            @type fixed_flux: vector values on the domain (e.g. L{Data}).
354            @param tol: relative tolerance to be used.
355            @type tol: positive C{float}.
356            @return: flux
357            @rtype: L{Data}
358            @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}}
359                   for the permeability M{k_{ij}}
360            """
361            self.__pde_v.setTolerance(self.getSubProblemTolerance())
362            g=self.__g
363            f=self.__f
364            self.__pde_v.setValue(X=f*util.kronecker(self.domain), r=fixed_flux)
365            if p == None:
366               self.__pde_v.setValue(Y=g)
367            else:
368               self.__pde_v.setValue(Y=g-self.__Q(p))
369            return self.__pde_v.getSolution(verbose=show_details)
370
371  class StokesProblemCartesian(HomogeneousSaddlePointProblem):  class StokesProblemCartesian(HomogeneousSaddlePointProblem):
372       """       """
373       solves       solves
374
375            -(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}
376                  u_{i,i}=0                  u_{i,i}=0
# Line 333  class StokesProblemCartesian(Homogeneous Line 378  class StokesProblemCartesian(Homogeneous
378            u=0 where  fixed_u_mask>0            u=0 where  fixed_u_mask>0
379            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
380
381       if surface_stress is not given 0 is assumed.       if surface_stress is not given 0 is assumed.
382
383       typical usage:       typical usage:
384
# Line 357  class StokesProblemCartesian(Homogeneous Line 402  class StokesProblemCartesian(Homogeneous
402           self.__pde_u.setSymmetryOn()           self.__pde_u.setSymmetryOn()
403           # self.__pde_u.setSolverMethod(self.__pde_u.DIRECT)           # self.__pde_u.setSolverMethod(self.__pde_u.DIRECT)
404           # self.__pde_u.setSolverMethod(preconditioner=LinearPDE.RILU)           # self.__pde_u.setSolverMethod(preconditioner=LinearPDE.RILU)
405
406           self.__pde_prec=LinearPDE(domain)           self.__pde_prec=LinearPDE(domain)
407           self.__pde_prec.setReducedOrderOn()           self.__pde_prec.setReducedOrderOn()
408           # self.__pde_prec.setSolverMethod(self.__pde_prec.LUMPING)           # self.__pde_prec.setSolverMethod(self.__pde_prec.LUMPING)
# Line 378  class StokesProblemCartesian(Homogeneous Line 423  class StokesProblemCartesian(Homogeneous
423          @param stress: initial stress          @param stress: initial stress
424      @type stress: L{Tensor} object on L{FunctionSpace} L{Function} or similar      @type stress: L{Tensor} object on L{FunctionSpace} L{Function} or similar
425          @note: All values needs to be set.          @note: All values needs to be set.
426
427          """          """
428          self.eta=eta          self.eta=eta
429          A =self.__pde_u.createCoefficient("A")          A =self.__pde_u.createCoefficient("A")
430      self.__pde_u.setValue(A=Data())      self.__pde_u.setValue(A=Data())
431          for i in range(self.domain.getDim()):          for i in range(self.domain.getDim()):
432          for j in range(self.domain.getDim()):          for j in range(self.domain.getDim()):
433              A[i,j,j,i] += 1.              A[i,j,j,i] += 1.
434              A[i,j,i,j] += 1.              A[i,j,i,j] += 1.
435      self.__pde_prec.setValue(D=1/self.eta)      self.__pde_prec.setValue(D=1/self.eta)
436          self.__pde_u.setValue(A=A*self.eta,q=fixed_u_mask)          self.__pde_u.setValue(A=A*self.eta,q=fixed_u_mask)
437          self.__f=f          self.__f=f
438          self.__surface_stress=surface_stress          self.__surface_stress=surface_stress
439          self.__stress=stress          self.__stress=stress
440
#===============================================================================================================
441       def inner_pBv(self,p,v):       def inner_pBv(self,p,v):
442           """           """
443           returns inner product of element p and div(v)           returns inner product of element p and div(v)
# Line 433  class StokesProblemCartesian(Homogeneous Line 477  class StokesProblemCartesian(Homogeneous
477           return the value for v for a given p (overwrite)           return the value for v for a given p (overwrite)
478
479           @param p: a pressure           @param p: a pressure
480           @param v0: a initial guess for the value v to return.           @param v0: a initial guess for the value v to return.
481           @return: v given as M{v= A^{-1} (f-B^*p)}           @return: v given as M{v= A^{-1} (f-B^*p)}
482           """           """
483           self.__pde_u.setTolerance(self.getSubProblemTolerance())           self.__pde_u.setTolerance(self.getSubProblemTolerance())
# Line 448  class StokesProblemCartesian(Homogeneous Line 492  class StokesProblemCartesian(Homogeneous
492
493           raise NotImplementedError,"no v calculation implemented."           raise NotImplementedError,"no v calculation implemented."
494
495
496       def norm_Bv(self,v):       def norm_Bv(self,v):
497          """          """
498          Returns Bv (overwrite).          Returns Bv (overwrite).
# Line 463  class StokesProblemCartesian(Homogeneous Line 507  class StokesProblemCartesian(Homogeneous
507           Solves M{Av=B^*p} with accuracy L{self.getSubProblemTolerance()}           Solves M{Av=B^*p} with accuracy L{self.getSubProblemTolerance()}
508
509           @param p: a pressure increment           @param p: a pressure increment
510           @return: the solution of M{Av=B^*p}           @return: the solution of M{Av=B^*p}
511           @note: boundary conditions on v should be zero!           @note: boundary conditions on v should be zero!
512           """           """
513           self.__pde_u.setTolerance(self.getSubProblemTolerance())           self.__pde_u.setTolerance(self.getSubProblemTolerance())

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