Date: 2025-03-29T05:58:49+00:00

Actual source code: ex1.c

      1: static char help[] = "Solves the time independent Bratu problem using pseudo-timestepping.";

      3: /* ------------------------------------------------------------------------

      5:     This code demonstrates how one may solve a nonlinear problem
      6:     with pseudo-timestepping. In this simple example, the pseudo-timestep
      7:     is the same for all grid points, i.e., this is equivalent to using
      8:     the backward Euler method with a variable timestep.

     10:     Note: This example does not require pseudo-timestepping since it
     11:     is an easy nonlinear problem, but it is included to demonstrate how
     12:     the pseudo-timestepping may be done.

     14:     See snes/tutorials/ex4.c[ex4f.F] and
     15:     snes/tutorials/ex5.c[ex5f.F] where the problem is described
     16:     and solved using Newton's method alone.

     18:   ----------------------------------------------------------------------------- */
     19: /*
     20:     Include "petscts.h" to use the PETSc timestepping routines. Note that
     21:     this file automatically includes "petscsys.h" and other lower-level
     22:     PETSc include files.
     23: */
     24: #include <petscts.h>

     26: /*
     27:   Create an application context to contain data needed by the
     28:   application-provided call-back routines, FormJacobian() and
     29:   FormFunction().
     30: */
     31: typedef struct {
     32:   PetscReal param; /* test problem parameter */
     33:   PetscInt  mx;    /* Discretization in x-direction */
     34:   PetscInt  my;    /* Discretization in y-direction */
     35: } AppCtx;

     37: /*
     38:    User-defined routines
     39: */
     40: extern PetscErrorCode FormJacobian(TS, PetscReal, Vec, Mat, Mat, void *), FormFunction(TS, PetscReal, Vec, Vec, void *), FormInitialGuess(Vec, AppCtx *);

     42: int main(int argc, char **argv)
     43: {
     44:   TS          ts;     /* timestepping context */
     45:   Vec         x, r;   /* solution, residual vectors */
     46:   Mat         J;      /* Jacobian matrix */
     47:   AppCtx      user;   /* user-defined work context */
     48:   PetscInt    its, N; /* iterations for convergence */
     49:   PetscReal   param_max = 6.81, param_min = 0., dt;
     50:   PetscReal   ftime;
     51:   PetscMPIInt size;

     53:   PetscFunctionBeginUser;
     54:   PetscCall(PetscInitialize(&argc, &argv, NULL, help));
     55:   PetscCallMPI(MPI_Comm_size(PETSC_COMM_WORLD, &size));
     56:   PetscCheck(size == 1, PETSC_COMM_WORLD, PETSC_ERR_WRONG_MPI_SIZE, "This is a uniprocessor example only");

     58:   user.mx    = 4;
     59:   user.my    = 4;
     60:   user.param = 6.0;

     62:   /*
     63:      Allow user to set the grid dimensions and nonlinearity parameter at run-time
     64:   */
     65:   PetscCall(PetscOptionsGetInt(NULL, NULL, "-mx", &user.mx, NULL));
     66:   PetscCall(PetscOptionsGetInt(NULL, NULL, "-my", &user.my, NULL));
     67:   N  = user.mx * user.my;
     68:   dt = .5 / PetscMax(user.mx, user.my);
     69:   PetscCall(PetscOptionsGetReal(NULL, NULL, "-param", &user.param, NULL));
     70:   PetscCheck(user.param < param_max && user.param >= param_min, PETSC_COMM_WORLD, PETSC_ERR_ARG_OUTOFRANGE, "Parameter is out of range");

     72:   /*
     73:       Create vectors to hold the solution and function value
     74:   */
     75:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, N, &x));
     76:   PetscCall(VecDuplicate(x, &r));

     78:   /*
     79:     Create matrix to hold Jacobian. Preallocate 5 nonzeros per row
     80:     in the sparse matrix. Note that this is not the optimal strategy; see
     81:     the Performance chapter of the users manual for information on
     82:     preallocating memory in sparse matrices.
     83:   */
     84:   PetscCall(MatCreateSeqAIJ(PETSC_COMM_SELF, N, N, 5, 0, &J));

     86:   /*
     87:      Create timestepper context
     88:   */
     89:   PetscCall(TSCreate(PETSC_COMM_WORLD, &ts));
     90:   PetscCall(TSSetProblemType(ts, TS_NONLINEAR));

     92:   /*
     93:      Tell the timestepper context where to compute solutions
     94:   */
     95:   PetscCall(TSSetSolution(ts, x));

     97:   /*
     98:      Provide the call-back for the nonlinear function we are
     99:      evaluating. Thus whenever the timestepping routines need the
    100:      function they will call this routine. Note the final argument
    101:      is the application context used by the call-back functions.
    102:   */
    103:   PetscCall(TSSetRHSFunction(ts, NULL, FormFunction, &user));

    105:   /*
    106:      Set the Jacobian matrix and the function used to compute
    107:      Jacobians.
    108:   */
    109:   PetscCall(TSSetRHSJacobian(ts, J, J, FormJacobian, &user));

    111:   /*
    112:        Form the initial guess for the problem
    113:   */
    114:   PetscCall(FormInitialGuess(x, &user));

    116:   /*
    117:        This indicates that we are using pseudo timestepping to
    118:      find a steady state solution to the nonlinear problem.
    119:   */
    120:   PetscCall(TSSetType(ts, TSPSEUDO));

    122:   /*
    123:        Set the initial time to start at (this is arbitrary for
    124:      steady state problems); and the initial timestep given above
    125:   */
    126:   PetscCall(TSSetTimeStep(ts, dt));

    128:   /*
    129:       Set a large number of timesteps and final duration time
    130:      to insure convergence to steady state.
    131:   */
    132:   PetscCall(TSSetMaxSteps(ts, 10000));
    133:   PetscCall(TSSetMaxTime(ts, 1e12));
    134:   PetscCall(TSSetExactFinalTime(ts, TS_EXACTFINALTIME_STEPOVER));

    136:   /*
    137:       Use the default strategy for increasing the timestep
    138:   */
    139:   PetscCall(TSPseudoSetTimeStep(ts, TSPseudoTimeStepDefault, 0));

    141:   /*
    142:       Set any additional options from the options database. This
    143:      includes all options for the nonlinear and linear solvers used
    144:      internally the timestepping routines.
    145:   */
    146:   PetscCall(TSSetFromOptions(ts));

    148:   PetscCall(TSSetUp(ts));

    150:   /*
    151:       Perform the solve. This is where the timestepping takes place.
    152:   */
    153:   PetscCall(TSSolve(ts, x));
    154:   PetscCall(TSGetSolveTime(ts, &ftime));

    156:   /*
    157:       Get the number of steps
    158:   */
    159:   PetscCall(TSGetStepNumber(ts, &its));

    161:   PetscCall(PetscPrintf(PETSC_COMM_WORLD, "Number of pseudo timesteps = %" PetscInt_FMT " final time %4.2e\n", its, (double)ftime));

    163:   /*
    164:      Free the data structures constructed above
    165:   */
    166:   PetscCall(VecDestroy(&x));
    167:   PetscCall(VecDestroy(&r));
    168:   PetscCall(MatDestroy(&J));
    169:   PetscCall(TSDestroy(&ts));
    170:   PetscCall(PetscFinalize());
    171:   return 0;
    172: }
    173: /* ------------------------------------------------------------------ */
    174: /*           Bratu (Solid Fuel Ignition) Test Problem                 */
    175: /* ------------------------------------------------------------------ */

    177: /* --------------------  Form initial approximation ----------------- */

    179: PetscErrorCode FormInitialGuess(Vec X, AppCtx *user)
    180: {
    181:   PetscInt     i, j, row, mx, my;
    182:   PetscReal    one = 1.0, lambda;
    183:   PetscReal    temp1, temp, hx, hy;
    184:   PetscScalar *x;

    186:   PetscFunctionBeginUser;
    187:   mx     = user->mx;
    188:   my     = user->my;
    189:   lambda = user->param;

    191:   hx = one / (PetscReal)(mx - 1);
    192:   hy = one / (PetscReal)(my - 1);

    194:   PetscCall(VecGetArray(X, &x));
    195:   temp1 = lambda / (lambda + one);
    196:   for (j = 0; j < my; j++) {
    197:     temp = (PetscReal)(PetscMin(j, my - j - 1)) * hy;
    198:     for (i = 0; i < mx; i++) {
    199:       row = i + j * mx;
    200:       if (i == 0 || j == 0 || i == mx - 1 || j == my - 1) {
    201:         x[row] = 0.0;
    202:         continue;
    203:       }
    204:       x[row] = temp1 * PetscSqrtReal(PetscMin((PetscReal)(PetscMin(i, mx - i - 1)) * hx, temp));
    205:     }
    206:   }
    207:   PetscCall(VecRestoreArray(X, &x));
    208:   PetscFunctionReturn(PETSC_SUCCESS);
    209: }
    210: /* --------------------  Evaluate Function F(x) --------------------- */

    212: PetscErrorCode FormFunction(TS ts, PetscReal t, Vec X, Vec F, void *ptr)
    213: {
    214:   AppCtx            *user = (AppCtx *)ptr;
    215:   PetscInt           i, j, row, mx, my;
    216:   PetscReal          two = 2.0, one = 1.0, lambda;
    217:   PetscReal          hx, hy, hxdhy, hydhx;
    218:   PetscScalar        ut, ub, ul, ur, u, uxx, uyy, sc, *f;
    219:   const PetscScalar *x;

    221:   PetscFunctionBeginUser;
    222:   mx     = user->mx;
    223:   my     = user->my;
    224:   lambda = user->param;

    226:   hx    = one / (PetscReal)(mx - 1);
    227:   hy    = one / (PetscReal)(my - 1);
    228:   sc    = hx * hy;
    229:   hxdhy = hx / hy;
    230:   hydhx = hy / hx;

    232:   PetscCall(VecGetArrayRead(X, &x));
    233:   PetscCall(VecGetArray(F, &f));
    234:   for (j = 0; j < my; j++) {
    235:     for (i = 0; i < mx; i++) {
    236:       row = i + j * mx;
    237:       if (i == 0 || j == 0 || i == mx - 1 || j == my - 1) {
    238:         f[row] = x[row];
    239:         continue;
    240:       }
    241:       u      = x[row];
    242:       ub     = x[row - mx];
    243:       ul     = x[row - 1];
    244:       ut     = x[row + mx];
    245:       ur     = x[row + 1];
    246:       uxx    = (-ur + two * u - ul) * hydhx;
    247:       uyy    = (-ut + two * u - ub) * hxdhy;
    248:       f[row] = -uxx + -uyy + sc * lambda * PetscExpScalar(u);
    249:     }
    250:   }
    251:   PetscCall(VecRestoreArrayRead(X, &x));
    252:   PetscCall(VecRestoreArray(F, &f));
    253:   PetscFunctionReturn(PETSC_SUCCESS);
    254: }
    255: /* --------------------  Evaluate Jacobian F'(x) -------------------- */

    257: /*
    258:    Calculate the Jacobian matrix J(X,t).

    260:    Note: We put the Jacobian in the preconditioner storage B instead of J. This
    261:    way we can give the -snes_mf_operator flag to check our work. This replaces
    262:    J with a finite difference approximation, using our analytic Jacobian B for
    263:    the preconditioner.
    264: */
    265: PetscErrorCode FormJacobian(TS ts, PetscReal t, Vec X, Mat J, Mat B, void *ptr)
    266: {
    267:   AppCtx            *user = (AppCtx *)ptr;
    268:   PetscInt           i, j, row, mx, my, col[5];
    269:   PetscScalar        two = 2.0, one = 1.0, lambda, v[5], sc;
    270:   const PetscScalar *x;
    271:   PetscReal          hx, hy, hxdhy, hydhx;

    273:   PetscFunctionBeginUser;
    274:   mx     = user->mx;
    275:   my     = user->my;
    276:   lambda = user->param;

    278:   hx    = 1.0 / (PetscReal)(mx - 1);
    279:   hy    = 1.0 / (PetscReal)(my - 1);
    280:   sc    = hx * hy;
    281:   hxdhy = hx / hy;
    282:   hydhx = hy / hx;

    284:   PetscCall(VecGetArrayRead(X, &x));
    285:   for (j = 0; j < my; j++) {
    286:     for (i = 0; i < mx; i++) {
    287:       row = i + j * mx;
    288:       if (i == 0 || j == 0 || i == mx - 1 || j == my - 1) {
    289:         PetscCall(MatSetValues(B, 1, &row, 1, &row, &one, INSERT_VALUES));
    290:         continue;
    291:       }
    292:       v[0]   = hxdhy;
    293:       col[0] = row - mx;
    294:       v[1]   = hydhx;
    295:       col[1] = row - 1;
    296:       v[2]   = -two * (hydhx + hxdhy) + sc * lambda * PetscExpScalar(x[row]);
    297:       col[2] = row;
    298:       v[3]   = hydhx;
    299:       col[3] = row + 1;
    300:       v[4]   = hxdhy;
    301:       col[4] = row + mx;
    302:       PetscCall(MatSetValues(B, 1, &row, 5, col, v, INSERT_VALUES));
    303:     }
    304:   }
    305:   PetscCall(VecRestoreArrayRead(X, &x));
    306:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
    307:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
    308:   if (J != B) {
    309:     PetscCall(MatAssemblyBegin(J, MAT_FINAL_ASSEMBLY));
    310:     PetscCall(MatAssemblyEnd(J, MAT_FINAL_ASSEMBLY));
    311:   }
    312:   PetscFunctionReturn(PETSC_SUCCESS);
    313: }

    315: /*TEST

    317:     test:
    318:       args: -ksp_gmres_cgs_refinement_type refine_always -snes_type newtonls -ts_monitor_pseudo -snes_atol 1.e-7 -ts_pseudo_frtol 1.e-5 -ts_view draw:tikz:fig.tex

    320:     test:
    321:       suffix: 2
    322:       args: -ts_monitor_pseudo -ts_pseudo_frtol 1.e-5

    324: TEST*/