Actual source code: ex1.c
petsc-3.10.5 2019-03-28
2: static char help[] ="Solves the time independent Bratu problem using pseudo-timestepping.";
4: /*
5: Concepts: TS^pseudo-timestepping
6: Concepts: pseudo-timestepping
7: Concepts: TS^nonlinear problems
8: Processors: 1
10: */
12: /* ------------------------------------------------------------------------
14: This code demonstrates how one may solve a nonlinear problem
15: with pseudo-timestepping. In this simple example, the pseudo-timestep
16: is the same for all grid points, i.e., this is equivalent to using
17: the backward Euler method with a variable timestep.
19: Note: This example does not require pseudo-timestepping since it
20: is an easy nonlinear problem, but it is included to demonstrate how
21: the pseudo-timestepping may be done.
23: See snes/examples/tutorials/ex4.c[ex4f.F] and
24: snes/examples/tutorials/ex5.c[ex5f.F] where the problem is described
25: and solved using Newton's method alone.
27: ----------------------------------------------------------------------------- */
28: /*
29: Include "petscts.h" to use the PETSc timestepping routines. Note that
30: this file automatically includes "petscsys.h" and other lower-level
31: PETSc include files.
32: */
33: #include <petscts.h>
35: /*
36: Create an application context to contain data needed by the
37: application-provided call-back routines, FormJacobian() and
38: FormFunction().
39: */
40: typedef struct {
41: PetscReal param; /* test problem parameter */
42: PetscInt mx; /* Discretization in x-direction */
43: PetscInt my; /* Discretization in y-direction */
44: } AppCtx;
46: /*
47: User-defined routines
48: */
49: extern PetscErrorCode FormJacobian(TS,PetscReal,Vec,Mat,Mat,void*), FormFunction(TS,PetscReal,Vec,Vec,void*), FormInitialGuess(Vec,AppCtx*);
51: int main(int argc,char **argv)
52: {
53: TS ts; /* timestepping context */
54: Vec x,r; /* solution, residual vectors */
55: Mat J; /* Jacobian matrix */
56: AppCtx user; /* user-defined work context */
57: PetscInt its,N; /* iterations for convergence */
59: PetscReal param_max = 6.81,param_min = 0.,dt;
60: PetscReal ftime;
61: PetscMPIInt size;
63: PetscInitialize(&argc,&argv,NULL,help);if (ierr) return ierr;
64: MPI_Comm_size(PETSC_COMM_WORLD,&size);
65: if (size != 1) SETERRQ(PETSC_COMM_WORLD,PETSC_ERR_SUP,"This is a uniprocessor example only");
67: user.mx = 4;
68: user.my = 4;
69: user.param = 6.0;
71: /*
72: Allow user to set the grid dimensions and nonlinearity parameter at run-time
73: */
74: PetscOptionsGetInt(NULL,NULL,"-mx",&user.mx,NULL);
75: PetscOptionsGetInt(NULL,NULL,"-my",&user.my,NULL);
76: N = user.mx*user.my;
77: dt = .5/PetscMax(user.mx,user.my);
78: PetscOptionsGetReal(NULL,NULL,"-param",&user.param,NULL);
79: if (user.param >= param_max || user.param <= param_min) SETERRQ(PETSC_COMM_SELF,1,"Parameter is out of range");
81: /*
82: Create vectors to hold the solution and function value
83: */
84: VecCreateSeq(PETSC_COMM_SELF,N,&x);
85: VecDuplicate(x,&r);
87: /*
88: Create matrix to hold Jacobian. Preallocate 5 nonzeros per row
89: in the sparse matrix. Note that this is not the optimal strategy; see
90: the Performance chapter of the users manual for information on
91: preallocating memory in sparse matrices.
92: */
93: MatCreateSeqAIJ(PETSC_COMM_SELF,N,N,5,0,&J);
95: /*
96: Create timestepper context
97: */
98: TSCreate(PETSC_COMM_WORLD,&ts);
99: TSSetProblemType(ts,TS_NONLINEAR);
101: /*
102: Tell the timestepper context where to compute solutions
103: */
104: TSSetSolution(ts,x);
106: /*
107: Provide the call-back for the nonlinear function we are
108: evaluating. Thus whenever the timestepping routines need the
109: function they will call this routine. Note the final argument
110: is the application context used by the call-back functions.
111: */
112: TSSetRHSFunction(ts,NULL,FormFunction,&user);
114: /*
115: Set the Jacobian matrix and the function used to compute
116: Jacobians.
117: */
118: TSSetRHSJacobian(ts,J,J,FormJacobian,&user);
120: /*
121: Form the initial guess for the problem
122: */
123: FormInitialGuess(x,&user);
125: /*
126: This indicates that we are using pseudo timestepping to
127: find a steady state solution to the nonlinear problem.
128: */
129: TSSetType(ts,TSPSEUDO);
131: /*
132: Set the initial time to start at (this is arbitrary for
133: steady state problems); and the initial timestep given above
134: */
135: TSSetTimeStep(ts,dt);
137: /*
138: Set a large number of timesteps and final duration time
139: to insure convergence to steady state.
140: */
141: TSSetMaxSteps(ts,10000);
142: TSSetMaxTime(ts,1e12);
143: TSSetExactFinalTime(ts,TS_EXACTFINALTIME_STEPOVER);
145: /*
146: Use the default strategy for increasing the timestep
147: */
148: TSPseudoSetTimeStep(ts,TSPseudoTimeStepDefault,0);
150: /*
151: Set any additional options from the options database. This
152: includes all options for the nonlinear and linear solvers used
153: internally the timestepping routines.
154: */
155: TSSetFromOptions(ts);
157: TSSetUp(ts);
159: /*
160: Perform the solve. This is where the timestepping takes place.
161: */
162: TSSolve(ts,x);
163: TSGetSolveTime(ts,&ftime);
165: /*
166: Get the number of steps
167: */
168: TSGetStepNumber(ts,&its);
170: PetscPrintf(PETSC_COMM_WORLD,"Number of pseudo timesteps = %D final time %4.2e\n",its,(double)ftime);
172: /*
173: Free the data structures constructed above
174: */
175: VecDestroy(&x);
176: VecDestroy(&r);
177: MatDestroy(&J);
178: TSDestroy(&ts);
179: PetscFinalize();
180: return ierr;
181: }
182: /* ------------------------------------------------------------------ */
183: /* Bratu (Solid Fuel Ignition) Test Problem */
184: /* ------------------------------------------------------------------ */
186: /* -------------------- Form initial approximation ----------------- */
188: PetscErrorCode FormInitialGuess(Vec X,AppCtx *user)
189: {
190: PetscInt i,j,row,mx,my;
192: PetscReal one = 1.0,lambda;
193: PetscReal temp1,temp,hx,hy;
194: PetscScalar *x;
196: mx = user->mx;
197: my = user->my;
198: lambda = user->param;
200: hx = one / (PetscReal)(mx-1);
201: hy = one / (PetscReal)(my-1);
203: VecGetArray(X,&x);
204: temp1 = lambda/(lambda + one);
205: for (j=0; j<my; j++) {
206: temp = (PetscReal)(PetscMin(j,my-j-1))*hy;
207: for (i=0; i<mx; i++) {
208: row = i + j*mx;
209: if (i == 0 || j == 0 || i == mx-1 || j == my-1) {
210: x[row] = 0.0;
211: continue;
212: }
213: x[row] = temp1*PetscSqrtReal(PetscMin((PetscReal)(PetscMin(i,mx-i-1))*hx,temp));
214: }
215: }
216: VecRestoreArray(X,&x);
217: return 0;
218: }
219: /* -------------------- Evaluate Function F(x) --------------------- */
221: PetscErrorCode FormFunction(TS ts,PetscReal t,Vec X,Vec F,void *ptr)
222: {
223: AppCtx *user = (AppCtx*)ptr;
224: PetscErrorCode ierr;
225: PetscInt i,j,row,mx,my;
226: PetscReal two = 2.0,one = 1.0,lambda;
227: PetscReal hx,hy,hxdhy,hydhx;
228: PetscScalar ut,ub,ul,ur,u,uxx,uyy,sc,*f;
229: const PetscScalar *x;
231: mx = user->mx;
232: my = user->my;
233: lambda = user->param;
235: hx = one / (PetscReal)(mx-1);
236: hy = one / (PetscReal)(my-1);
237: sc = hx*hy;
238: hxdhy = hx/hy;
239: hydhx = hy/hx;
241: VecGetArrayRead(X,&x);
242: VecGetArray(F,&f);
243: for (j=0; j<my; j++) {
244: for (i=0; i<mx; i++) {
245: row = i + j*mx;
246: if (i == 0 || j == 0 || i == mx-1 || j == my-1) {
247: f[row] = x[row];
248: continue;
249: }
250: u = x[row];
251: ub = x[row - mx];
252: ul = x[row - 1];
253: ut = x[row + mx];
254: ur = x[row + 1];
255: uxx = (-ur + two*u - ul)*hydhx;
256: uyy = (-ut + two*u - ub)*hxdhy;
257: f[row] = -uxx + -uyy + sc*lambda*PetscExpScalar(u);
258: }
259: }
260: VecRestoreArrayRead(X,&x);
261: VecRestoreArray(F,&f);
262: return 0;
263: }
264: /* -------------------- Evaluate Jacobian F'(x) -------------------- */
266: /*
267: Calculate the Jacobian matrix J(X,t).
269: Note: We put the Jacobian in the preconditioner storage B instead of J. This
270: way we can give the -snes_mf_operator flag to check our work. This replaces
271: J with a finite difference approximation, using our analytic Jacobian B for
272: the preconditioner.
273: */
274: PetscErrorCode FormJacobian(TS ts,PetscReal t,Vec X,Mat J,Mat B,void *ptr)
275: {
276: AppCtx *user = (AppCtx*)ptr;
277: PetscInt i,j,row,mx,my,col[5];
278: PetscErrorCode ierr;
279: PetscScalar two = 2.0,one = 1.0,lambda,v[5],sc;
280: const PetscScalar *x;
281: PetscReal hx,hy,hxdhy,hydhx;
284: mx = user->mx;
285: my = user->my;
286: lambda = user->param;
288: hx = 1.0 / (PetscReal)(mx-1);
289: hy = 1.0 / (PetscReal)(my-1);
290: sc = hx*hy;
291: hxdhy = hx/hy;
292: hydhx = hy/hx;
294: VecGetArrayRead(X,&x);
295: for (j=0; j<my; j++) {
296: for (i=0; i<mx; i++) {
297: row = i + j*mx;
298: if (i == 0 || j == 0 || i == mx-1 || j == my-1) {
299: MatSetValues(B,1,&row,1,&row,&one,INSERT_VALUES);
300: continue;
301: }
302: v[0] = hxdhy; col[0] = row - mx;
303: v[1] = hydhx; col[1] = row - 1;
304: v[2] = -two*(hydhx + hxdhy) + sc*lambda*PetscExpScalar(x[row]); col[2] = row;
305: v[3] = hydhx; col[3] = row + 1;
306: v[4] = hxdhy; col[4] = row + mx;
307: MatSetValues(B,1,&row,5,col,v,INSERT_VALUES);
308: }
309: }
310: VecRestoreArrayRead(X,&x);
311: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
312: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
313: if (J != B) {
314: MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
315: MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
316: }
317: return 0;
318: }
321: /*TEST
323: test:
324: 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
326: test:
327: suffix: 2
328: args: -ts_monitor_pseudo -ts_pseudo_frtol 1.e-5
330: TEST*/