[petsc-users] Dirichlet boundary condition for a nonlinear system

[Barry Smith]

  So you could solve a sequence of problems where you move the boundary condition from being “easy” to the final value you want as a kind of continuation method?

    Hmm, wonder how you would do that cleanly in PETSc.

  Barry

On Feb 18, 2014, at 10:31 PM, Sanjay Govindjee <s_g at berkeley.edu> wrote:

> One alternate to this is that on the first step the initial value of X_B is not
> taken as the know value but rather some prior known value.  Then for the first
> iteration Y_B turns out to be the known increment; in subsequent iterations it
> is take as 0.  This can sometimes be helpful with convergence in tough problems.
> 
> -sg
> 
> 
> On 2/18/14 5:56 PM, Barry Smith wrote:
>> On Feb 18, 2014, at 5:39 PM, Fande Kong <fd.kong at siat.ac.cn> wrote:
>> 
>>> Hi all,
>>> 
>>> I am just trying to solve a nonlinear system resulted from discretizating a hyperelasticity problem by finite element method. When I solve a linear PDE, I never put boundary solution either in a solution vector or a matrix, but instead, I put boundary condition to the right hand size (load).
>>     You adjust the right hand side to have zero as the boundary conditions. This can be written as
>> 
>>       (A_II   A_IB ) ( X_I )       (F_I)
>>       (A_BI  A_BB)(X_B)   =   (F_B)
>> 
>>     Which is equivalent to
>> 
>>       (A_I  A_B) (X_I)         (F_I) - (A_B)*(X_B)
>>                         (0)       =
>> 
>>       A_I X_I  = F_I - A_B*X_B
>> 
>>     In the nonlinear case you have
>> 
>>       F_I(X_I,X_B)    = ( 0 )
>>       F_B(X_I,X_B)      ( 0)
>> 
>>      where you know X_B  with Jacobian
>> 
>>        (J_II  J_IB)
>>        (J_BI J_BB)
>>         Newtons’ method on all variables gives
>> 
>>       (X_I)^{n+1}     =  (X_I)^{n}     +  (Y_I)
>>       (X_B)                  (X_B)              (Y_B)
>> 
>>     where   JY = F which written out in terms of I and B is
>> 
>>         (J_II  J_IB)   (Y_I)      =   F_I( X_I,X_B)
>>        (J_BI J_BB)   (Y_B)         F_B(X_I,X_B)
>> 
>>     Now since X_B is the solution on the boundary the updates on the boundary at zero so Y_B is zero so this system reduces to
>> 
>>         J_II   Y_I     = F_I(X_I,X_B) so Newton reduces to just the interior with
>> 
>>     (X_I)^{n+1}     =  (X_I)^{n}     +  J_II^{-1} F_I(X_I,X_B)
>> 
>>     Another way to look at it is you are simply solving F_I(X_I,X_B) = 0 with given X_B so Newton’s method only uses the Jacobian of F_I with respect to X_I
>> 
>>    Barry
>> 
>> 
>> 
>>      
>> 
>>    
>> 
>>> How can I do a similar thing when solving a nonlinear system using a newton method?
>>> 
>>> Thanks,
>>> 
>>> Fande,
>