15SteadyStateSystem::SteadyStateSystem()
17 m_state = make_shared<vector<double>>();
18 m_newt = make_unique<MultiNewton>(1);
21SteadyStateSystem::~SteadyStateSystem()
29 m_state->assign(x.begin(), x.end());
51 debuglog(
"\nAttempt Newton solution of steady-state problem.", loglevel);
62 writelog(
"\nSteady Jacobian factorization failed:"
82 debuglog(
"\nNewton steady-state solve failed.\n", loglevel);
87 writelog(
"\nAttempt {} timesteps.", nsteps);
97 writelog(
"\nFinal timestep info: dt= {:<10.4g} log(ss)= {:<10.4g}\n", dt,
106 dt = std::min(dt,
m_tmax);
111SteadyStateSystem::TimeStepGrowthStrategy
112SteadyStateSystem::parseTimeStepGrowthStrategy(
const string& strategy)
114 if (strategy ==
"fixed-growth") {
115 return TimeStepGrowthStrategy::fixed;
116 }
else if (strategy ==
"steady-norm") {
117 return TimeStepGrowthStrategy::steadyNorm;
118 }
else if (strategy ==
"transient-residual") {
119 return TimeStepGrowthStrategy::transientResidual;
120 }
else if (strategy ==
"residual-ratio") {
121 return TimeStepGrowthStrategy::residualRatio;
122 }
else if (strategy ==
"newton-iterations") {
123 return TimeStepGrowthStrategy::newtonIterations;
125 throw CanteraError(
"SteadyStateSystem::setTimeStepGrowthStrategy",
126 "Unknown time step growth strategy '{}'; must be one of "
127 "'fixed-growth', 'steady-norm', 'transient-residual', "
128 "'residual-ratio', or 'newton-iterations'.", strategy);
131string SteadyStateSystem::timeStepGrowthStrategyName(TimeStepGrowthStrategy strategy)
134 case TimeStepGrowthStrategy::fixed:
135 return "fixed-growth";
136 case TimeStepGrowthStrategy::steadyNorm:
137 return "steady-norm";
138 case TimeStepGrowthStrategy::transientResidual:
139 return "transient-residual";
140 case TimeStepGrowthStrategy::residualRatio:
141 return "residual-ratio";
142 case TimeStepGrowthStrategy::newtonIterations:
143 return "newton-iterations";
145 throw CanteraError(
"SteadyStateSystem::timeStepGrowthStrategyName",
146 "Unknown time step growth strategy.");
160 span<const double> x_after)
170 case TimeStepGrowthStrategy::fixed:
172 case TimeStepGrowthStrategy::steadyNorm: {
175 return (ss_after < ss_before) ? growth : 1.0;
177 case TimeStepGrowthStrategy::transientResidual: {
180 return (ts_after < ts_before) ? growth : 1.0;
182 case TimeStepGrowthStrategy::residualRatio: {
185 if (!(ts_after > 0.0) || !(ts_before > ts_after)) {
188 const double exponent = 0.2;
189 double ratio = ts_before / ts_after;
190 double factor = std::pow(ratio, exponent);
191 return std::min(growth, std::max(1.0, factor));
193 case TimeStepGrowthStrategy::newtonIterations: {
194 const int max_iters_for_growth = 3;
195 return (
newton().lastIterations() <= max_iters_for_growth) ? growth : 1.0;
198 throw CanteraError(
"SteadyStateSystem::calculateTimeStepGrowthFactor",
203 span<double> r,
int loglevel)
209 int successiveFailures = 0;
213 writelog(
"\n============================");
214 writelog(
"\n{:<5s} {:<11s} {:<7s}\n",
"step",
"dt (s)",
"log(ss)");
215 writelog(
"============================");
220 writelog(
"\n{:<5d} {:<6.4e} {:>7.4f}", n, dt, log10(ss));
221 }
else if (loglevel > 1) {
223 writelog(
"\nTimestep ({}) dt= {:<11.4e} log(ss)= {:<7.4f}", n, dt, log10(ss));
229 int j0 =
m_jac->nEvals();
232 int status =
newton().
solve(x, r, *
this, loglevel);
238 writelog(
"\nTimestep ({}) succeeded", n);
240 successiveFailures = 0;
243 if (
m_jac->nEvals() == j0) {
246 copy(r.begin(), r.end(), x.begin());
250 dt = std::min(dt,
m_tmax);
253 "Took maximum number of timesteps allowed ({}) without "
259 successiveFailures++;
262 }
else if (loglevel > 1) {
263 writelog(
"\nTimestep ({}) failed", n);
265 if (successiveFailures > 2) {
266 debuglog(
"--> Resetting negative species concentrations", loglevel);
268 successiveFailures = 0;
270 debuglog(
"--> Reducing timestep", loglevel);
274 "Time integration failed. Minimum timestep ({}) reached.",
m_tmin);
283 writelog(
"\n{:<5d} {:<6.4e} {:>7.4f}", n, dt, log10(ss));
284 writelog(
"\n============================");
285 }
else if (loglevel > 1) {
287 writelog(
"\nTimestep ({}) dt= {:<11.4e} log10(ss)= {:<7.4f}\n", n, dt, log10(ss));
299 for (
size_t i = 0; i <
m_size; i++) {
300 ss = std::max(fabs(r[i]),ss);
309 for (
size_t i = 0; i <
m_size; i++) {
310 ts = std::max(fabs(r[i]), ts);
318 m_steps.assign(tsteps.begin(), tsteps.end());
329 "Jacobian evaluator must be instantiated before calling resize()");
348 double rdt_save =
m_rdt;
357 double rdt_old =
m_rdt;
Base class for exceptions thrown by Cantera classes.
virtual string getMessage() const
Method overridden by derived classes to format the error message.
virtual string getMethod() const
Get the name of the method that threw the exception.
virtual double eval(double t) const
Evaluate the function.
Newton iterator for multi-domain, one-dimensional problems.
void setOptions(int maxJacAge=5)
Set options.
int solve(span< const double > x0, span< double > x1, SteadyStateSystem &r, int loglevel)
Find the solution to F(x) = 0 by damped Newton iteration.
int m_nsteps
Number of time steps taken in the current call to solve()
double calculateTimeStepGrowthFactor(span< const double > x_before, span< const double > x_after)
Determine the timestep growth factor after a successful step.
size_t m_size
Solution vector size
int m_nsteps_max
Maximum number of timesteps allowed per call to solve()
virtual void resize()
Call to set the size of internal data structures after first defining the system or if the problem si...
double timeStep(int nsteps, double dt, span< double > x, span< double > r, int loglevel)
Take time steps using Backward Euler.
virtual void resetBadValues(span< double > x)
Reset values such as negative species concentrations.
vector< double > m_xnew
Work array used to hold the residual or the new solution.
unique_ptr< MultiNewton > m_newt
Newton iterator.
void getState(span< double > x) const
Get the converged steady-state solution after calling solve().
size_t size() const
Total solution vector length;.
virtual void initTimeInteg(double dt, span< const double > x)
Prepare for time stepping beginning with solution x and timestep dt.
double tsnorm(span< const double > x, span< double > r)
Transient max norm (infinity norm) of the residual evaluated using solution x and the current timeste...
void evalSSJacobian(span< const double > x)
Evaluate the steady-state Jacobian, accessible via linearSolver()
size_t bandwidth() const
Jacobian bandwidth.
double m_rdt
Reciprocal of time step.
shared_ptr< SystemJacobian > m_jac
Jacobian evaluator.
void setInitialGuess(span< const double > x)
Set the initial guess. Should be called before solve().
shared_ptr< vector< double > > m_state
Solution vector.
void setTimeStepGrowthStrategy(const string &strategy)
Set the strategy used to grow the timestep after a successful step that reuses the current Jacobian.
virtual void eval(span< const double > x, span< double > r, double rdt=-1.0, int count=1)=0
Evaluate the residual function.
vector< int > m_mask
Transient mask.
void solve(int loglevel=0)
Solve the steady-state problem, taking internal timesteps as necessary until the Newton solver can co...
double ssnorm(span< const double > x, span< double > r)
Steady-state max norm (infinity norm) of the residual evaluated using solution x.
string timeStepGrowthStrategy() const
Get the configured timestep growth strategy.
void setTimeStep(double stepsize, span< const int > tsteps)
Set the number of time steps to try when the steady Newton solver is unsuccessful.
vector< int > m_steps
Array of number of steps to take after each unsuccessful steady-state solve before re-attempting the ...
TimeStepGrowthStrategy m_tstep_growth_strategy
Selected strategy for successful-step growth.
double m_tfactor
Factor time step is multiplied by if time stepping fails ( < 1 )
bool m_jac_ok
If true, Jacobian is current.
double m_tstep
Initial timestep.
int m_ts_jac_age
Maximum age of the Jacobian in time-stepping mode.
void setJacAge(int ss_age, int ts_age=-1)
Set the maximum number of steps that can be taken using the same Jacobian before it must be re-evalua...
virtual void writeDebugInfo(const string &header_suffix, const string &message, int loglevel, int attempt_counter)
Write solver debugging based on the specified log level.
double m_tmin
Minimum timestep size.
double m_tmax
Maximum timestep size.
int m_ss_jac_age
Maximum age of the Jacobian in steady-state mode.
virtual void evalJacobian(span< const double > x0)=0
Evaluates the Jacobian at x0 using finite differences.
virtual void setSteadyMode()
Prepare to solve the steady-state problem.
virtual void clearDebugFile()
Delete debug output file that may be created by writeDebugInfo() when solving with high loglevel.
int m_attempt_counter
Counter used to manage the number of states stored in the debug log file generated by writeDebugInfo(...
void setLinearSolver(shared_ptr< SystemJacobian > solver)
Set the linear solver used to hold the Jacobian matrix and solve linear systems as part of each Newto...
int m_max_history
Constant that determines the maximum number of states stored in the debug log file generated by write...
Func1 * m_time_step_callback
User-supplied function called after each successful timestep.
vector< double > m_xlast_ts
State vector after the last successful set of time steps.
vector< double > m_work1
Work arrays used during Jacobian evaluation.
MultiNewton & newton()
Return a reference to the Newton iterator.
double m_tstep_growth
Growth factor for successful steps that reuse the Jacobian.
Error thrown when time stepping cannot proceed and the steady-state solver should be given a chance t...
void debuglog(const string &msg, int loglevel)
Write a message to the log only if loglevel > 0.
void writelog(const string &fmt, const Args &... args)
Write a formatted message to the screen.
Namespace for the Cantera kernel.
const double Tiny
Small number to compare differences of mole fractions against.