Function solvefi.m
Tutorial main page runPolymer.m runLabPolymer.m equationOWPolymer.m computeMobilities.m setupControls.m setupSystem.m

Single step non-linear solver

Compute the next state of the system for given previous state state0 and time step dt.

The discretized residual equation are assembled in the function equationOWPolymer.m.

function [state, convergence, its] = solvefi(state0, dt, bc, system, varargin)
   opt = struct('verbose', false);
   opt = merge_options(opt, varargin{:});

   fluid = system.fluid;

   meta = struct('converged'  , false                       , ...
                 'stopped'    , false                       , ...
                 'relax'      , 1                           , ...
                 'stagnate'   , false                       , ...
                 'iteration'  , 0                           , ...
                 'res_history', []                          );

   timer = tic;

   converged = false;
   state = state0;

   if mrstVerbose
      fprintf('%13s%-26s%-36s\n', '', 'CNV (oil, water)', 'MB (oil, water)');

The function equationOWPolymer.m assembles the equation and returns the residuals and the Jacobians.

   equation = @(state) equationOWPolymer(state0, state, dt, bc, system);

We start with the Newton iterations

   while ~ (converged || meta.stopped),
      % Save iteration number in meta info
      meta.iteration = meta.iteration + 1;

We assemble the equations. The variable eqs is an Automatic Differentiation (AD) variable

      eqs = equation(state);

We call a standard linear solve to compute the Newton step dx.

      dx = SolveEqsADI(eqs, []);

We update state.

      state      = updateState(state, dx, system);

We compute the residual values by calling getResiduals. This function detects oscillation and stagnation

      [meta, residuals] = getResiduals(meta, eqs, system, false);

We test for convergence. Here we use a very stringent test given by a max norm

      if mrstVerbose
         fprintf('Newton iteration %d, max residual: %5g\n', meta.iteration, ...
      converged = all(residuals <= system.nonlinear.tol);

      if meta.stagnate,
         warning('newt:stagnate', 'Non-linear solver stagnated...')

      if meta.iteration > system.nonlinear.maxIterations
         meta.stopped = true;

   if meta.stopped,
      warning('newt:maxit', ...
              ['Non-linear solver did not converge, stopped ', ...
               'by max iterations...']);
      convergence = false;
      its = meta.iteration;
      convergence = true;

   dispif(mrstVerbose, 'Completed %d iterations in %1.2f s\n', ...
          meta.iteration, toc(timer));

We define the updateState function which, given an increment, returns the updated state.

function state = updateState(state, dx, system)
   fluid = system.fluid;

   dp = dx{1};
   ds = dx{2};
   dc = dx{3};

   state.pressure = state.pressure + dp;

We cap the saturations and concentrations values.

   state.s(:, 1) = max(0, min((state.s(:, 1) + ds), 1));
   state.s(:, 2) = 1 - state.s(:, 1);
   state.c = max(0, min((state.c + dc), fluid.cmax));
   state.cmax = max(state.c, state.cmax);

Published November 6, 2014