Différences

Ci-dessous, les différences entre deux révisions de la page.

--- sunfluidh:hypredata_poissonsolver_namelist [2017/09/25 13:41] – [SolverName] yann
+++ sunfluidh:hypredata_poissonsolver_namelist [2017/09/27 16:32] (Version actuelle) – [SolverName] yann
@@ Ligne 18: / Ligne 18: @@
    &HypreData_PoissonSolver  SolverName= "PFMG" ,
                              PreconditionerName= "NONE" ,
-                             RelaxationMethodName= "SOR-RedBlack-Sym",
+                             RelaxationMethodName= "SOR-RedBlack-Nonsym",
                              Convergence_Criterion = 1.D-08  ,
                              Number_Iteration= 10 ,
@@ Ligne 37: / Ligne 37: @@
    * Type: Character string
    * Selection of numerical methods for solving the Poisson's equation( PFMG or SMG are recommanded).
-       * __SMG__           : Parallel semicoarsening multigrid solver for the linear systems arising from finite difference, finite volume, or finite element discretizations of the diffusion equation. SMG is a particularly robust method. The algorithm semicoarsens in the z-direction and uses plane smoothing. The xy plane-solves are e↵ected by one V-cycle of the 2D SMG algorithm, which semicoarsens in the y-direction and uses line smoothing.
+       * __SMG__           : Parallel semicoarsening multigrid solver for the linear systems arising from finite difference, finite volume, or finite element discretizations of the diffusion equation. SMG is a particularly robust method. The algorithm semicoarsens in the z-direction and uses plane smoothing. The xy plane-solves are effected by one V-cycle of the 2D SMG algorithm, which semicoarsens in the y-direction and uses line smoothing.
-       * __PFMG__              : PFMG is a parallel semicoarsening multigrid solver similar to SMG. The main difference between the two methods is in the smoother: PFMG uses simple pointwise smoothing. As a result, PFMG is not as robust as SMG, but is much more e cient per V-cycle.
+       * __PFMG__              : PFMG is a parallel semicoarsening multigrid solver similar to SMG. The main difference between the two methods is in the smoother: PFMG uses simple pointwise smoothing. As a result, PFMG is not as robust as SMG, but is much more efficient per V-cycle.
 ==== SolverName ====
    * Type: Character string
@@ Ligne 51: / Ligne 51: @@
    * Set the solver for the relaxation procedure
        * __Jacobi__ : Jacobi's method
-       * __Weighted-Jacobi : Weighted Jacobi's method
+       * __Weighted-Jacobi__ : Weighted Jacobi's method
        * __SOR-Redblack-sym__ : SOR method for symmetrical matrix
        * __SOR-Redblack-Nonsym__ : SOR method for non symmetrical matrix
@@ Ligne 70: / Ligne 70: @@
    * Type : real value
    * the computation is stopped if the residu is lower than the criterion chosen.
+==== Matrix_Symmetrization ====
+    * Type Boolean value (default value= .false.). __** Not for the release SUNFLUIDH_EDU**__.
+    * The matrix is symmetric (set to .false. if any doubt exits)
 ==== Off_Set_Poisson_Source_term ====
-   * Type : Boolean value. __** Not for the release SUNFLUIDH_EDU**__.
+   * Type : Boolean value (default value= .false.). __** Not for the release SUNFLUIDH_EDU**__.
    * The source term is shifted from its averaged value such as its integral value over the domain is zero. This could facilitate the convergence in some particuliar cases. this option must be used with caution. For expert users only.