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// $Id: time_dependent.h 30036 2013-07-18 16:55:32Z maier $
//
// Copyright (C) 1999 - 2013 by the deal.II authors
//
// This file is part of the deal.II library.
//
// The deal.II library is free software; you can use it, redistribute
// it, and/or modify it under the terms of the GNU Lesser General
// Public License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
// The full text of the license can be found in the file LICENSE at
// the top level of the deal.II distribution.
//
// ---------------------------------------------------------------------
#ifndef __deal2__time_dependent_h
#define __deal2__time_dependent_h
/*---------------------------- time-dependent.h ---------------------------*/
#include <deal.II/base/config.h>
#include <deal.II/base/exceptions.h>
#include <deal.II/base/subscriptor.h>
#include <deal.II/base/smartpointer.h>
#include <vector>
#include <utility>
DEAL_II_NAMESPACE_OPEN
// forward declarations
class TimeStepBase;
template <typename number> class Vector;
template <int dim, int spacedim> class Triangulation;
/**
* This class provides an abstract interface to time dependent problems in that
* it addresses some of the most annoying aspects of this class of problems:
* data management. These problems frequently need large amounts of computer
* ressources, most notably computing time, main memory and disk space.
* Main memory reduction is often the most pressing need, methods to implement
* it are almost always quite messy, though, quickly leading to code that
* stores and reloads data at places scattered all over the program, and
* which becomes unmaintainable sometimes. The present class tries to offer
* a more structured interface, albeit simple, which emerged in my mind after
* messing with my wave equation simulation for several months.
*
* The design of this class is mostly tailored for the solution of time
* dependent partial differential equations where the computational
* meshes may differ between each two timesteps and where the computations
* on each time step take a rather long time compared with the overhead
* of this class. Since no reference to the class of problems is made within
* this class, it is not restricted to PDEs, though, and it seems likely that
* a solver for large ordinary matrix differential equations may successfully
* use the same setup and therefore this class.
*
*
* <h3>Overview</h3>
*
* The general structure of a time dependent problem solver using a timestepping
* scheme is about the following: we have a collection of time step objects
* on which we solve our problem subsequently. In order to do so, we need
* knowledge of the data on zero or several previous timesteps (when using single
* or multiple step methods, that is) and maybe also some data of time steps
* ahead (for example the computational grid on these). Dependening on the
* problem in question, a second loop over all timesteps may be done solving
* a dual problem, where the loop may run forward (one dual problem for each
* time step) or backward (using a global dual problem). Within one of these
* loops or using a separate loop, error estimators may be computed and the
* grids may be refined. Each of these loops are initiated by a call preparing
* each timestep object for the next loop, before actually starting the loop
* itself.
*
* We will denote a complete set of all these loops with the term "sweep".
* Since this library is mostly about adaptive methods, it is likely that the
* last loop within a sweep will generate refined meshes and that we will
* perform another sweep on these refined meshes. A total run will therefore
* often be a sequence of several sweeps. The global setup therefore looks
* like this:
* @verbatim
* for sweep=0 to n_sweeps-1
* {
* for i=0 to n_timesteps-1
* initialize timestep i for this sweep, e.g. for setting up
* data structures, creating temporary files, etc.
*
* for i=0 to n_timesteps-1
* prepare timestep i for loop 0
* for i=0 to n_timesteps-1
* perform loop 0 on timestep i (e.g. solve primal problem)
*
* for i=0 to n_timesteps-1
* prepare timestep i for loop 1
* for i=0 to n_timesteps-1
* perform loop 1 on timestep i (e.g. solve dual problem)
*
* for i=0 to n_timesteps-1
* prepare timestep i for loop 2
* for i=0 to n_timesteps-1
* perform loop 2 on timestep i (e.g. compute error information)
*
* ...
*
* for i=0 to n_timesteps-1
* notify timestep i of the end of the sweep, e.g. for cleanups,
* deletion of temporary files, etc.
* }
* @endverbatim
* The user may specify that a loop shall run forward or backward (the latter
* being needed for the solution of global dual problems, for example).
*
* Going from the global overview to a more local viewpoint, we note that when
* a loop visits one timestep (e.g. to solve the primal or dual problem, or
* to compute error information), we need information on this, one or more
* previous time steps and zero or more timesteps in the future. However,
* often it is not needed to know all information from these timesteps and
* it is often a computational requirement to delete data at the first
* possible time when it is no more needed. Likewise, data should be reloaded
* at the latest time possible.
*
* In order to facilitate these principles, the concept of waking up and
* letting sleep a time step object was developed. Assume we have a time
* stepping scheme which needs to look ahead one time step and needs the
* data of the last two time steps, the following pseudocode described
* what the centeral loop function of this class will do when we move
* from timestep @p n-1 to timestep @p n:
* @verbatim
* wake up timestep n+1 with signal 1
* wake up timestep n with signal 0
* do computation on timestep n
* let timestep n sleep with signal 0
* let timestep n-1 sleep with signal 1
* let timestep n-2 sleep with signal 2
*
* move from n to n+1
* @endverbatim
* The signal number here denotes the distance of the timestep being sent
* the signal to the timestep where computations are done on. The calls to
* the @p wake_up and @p sleep functions with signal 0 could in principle
* be absorbed into the function doing the computation; we use these
* redundant signals, however, in order to separate computations and data
* management from each other, allowing to put all stuff around grid
* management, data reload and storage into one set of functions and
* computations into another.
*
* In the example above, possible actions might be: timestep <tt>n+1</tt> rebuilds
* the computational grid (there is a specialized class which can do this
* for you); timestep @p n builds matrices sets solution vectors to the right
* size, maybe using an initial guess; then it does the computations; then
* it deletes the matrices since they are not needed by subsequent timesteps;
* timestep @p n-1 deletes those data vectors which are only needed by one
* timestep ahead; timestep @p n-2 deletes the remaining vectors and deletes
* the computational grid, somewhere storing information how to rebuild it
* eventually.
*
* From the given sketch above, it is clear that each time step object sees
* the following sequence of events:
* @verbatim
* wake up with signal 1
* wake up signal 0
* do computation
* sleep with signal 0
* sleep with signal 1
* sleep with signal 2
* @endverbatim
* This pattern is repeated for each loop in each sweep.
*
* For the different loops within each sweep, the numbers of timesteps
* to look ahead (i.e. the maximum signal number to the @p wake_up function)
* and the look-behind (i.e. the maximum signal number to the @p sleep
* function) can be chosen separately. For example, it is usually only
* needed to look one time step behind when computing error estimation
* (in some cases, it may vene be possible to not look ahead or back
* at all, in which case only signals zero will be sent), while one
* needs a look back of at least one for a timestepping method.
*
* Finally, a note on the direction of look-ahead and look-back is in
* place: look-ahead always refers to the direction the loop is running
* in, i.e. for loops running forward, @p wake_up is called for timestep
* objects with a greater time value than the one previously computed on,
* while @p sleep is called for timesteps with a lower time. If the loop
* runs in the opposite direction, e.g. when solving a global dual
* problem, this order is reversed.
*
*
* <h3>Implementation</h3>
*
* The main loop of a program using this class will usually look like
* the following one, taken modified from an application program that
* isn't distributed as part of the library:
* @code
* template <int dim>
* void TimeDependent_Wave<dim>::run_sweep (const unsigned int sweep_no)
* {
* start_sweep (sweep_no);
*
* solve_primal_problem ();
*
* if (compute_dual_problem)
* solve_dual_problem ();
*
* postprocess ();
*
* if (sweep_no != number_of_sweeps-1)
* refine_grids ();
*
* write_statistics ();
*
* end_sweep ();
* };
*
*
*
* template <int dim>
* void WaveProblem<dim>::run ()
* {
* for (unsigned int sweep=0; sweep<number_of_sweeps; ++sweep)
* timestep_manager.run_sweep (sweep);
* };
* @endcode
* Here, @p timestep_manager is an object of type TimeDependent_Wave(), which
* is a class derived from TimeDependent. @p start_sweep,
* @p solve_primal_problem, @p solve_dual_problem, @p postprocess and @p end_sweep
* are functions inherited from this class. They all do a loop over all
* timesteps within this object and call the respective function on each of
* these objects. For example, here are two of the functions as they are
* implemented by the library:
* @code
* void TimeDependent::start_sweep (const unsigned int s)
* {
* sweep_no = s;
*
* // reset the number each
* // time step has, since some time
* // steps might have been added since
* // the last time we visited them
* //
* // also set the sweep we will
* // process in the sequel
* for (unsigned int step=0; step<timesteps.size(); ++step)
* {
* timesteps[step]->set_timestep_no (step);
* timesteps[step]->set_sweep_no (sweep_no);
* };
*
* for (unsigned int step=0; step<timesteps.size(); ++step)
* timesteps[step]->start_sweep ();
* };
*
*
* void
* TimeDependent::solve_primal_problem ()
* {
* do_loop (mem_fun(&TimeStepBase::init_for_primal_problem),
* mem_fun(&TimeStepBase::solve_primal_problem),
* timestepping_data_primal,
* forward);
* };
* @endcode
* The latter function shows rather clear how most of the loops are
* invoked (@p solve_primal_problem, @p solve_dual_problem, @p postprocess,
* @p refine_grids and @p write_statistics all have this form, where the
* latter two give functions of the derived timestep class, rather than
* from the base class). The function TimeStepBase::init_for_primal_problem
* and the respective ones for the other operations defined by that class
* are only used to store the type of operation which the loop presently
* performed will do.
*
* As can be seen, most of the work is done by the @p do_loop function of
* this class, which takes the addresses of two functions which are used
* to initialize all timestep objects for the loop and to actually perform
* some action. The next parameter gives some information on the look-ahead
* and look-back and the last one denotes in which direction the loop is
* to be run.
*
* Using function pointers through the @p mem_fun functions provided by
* the <tt>C++</tt> standard library, it is possible to do neat tricks, like
* the following, also taken from the wave program, in this case from
* the function @p refine_grids:
* @code
* ...
* compute the thresholds for refinement
* ...
*
* do_loop (mem_fun (&TimeStepBase_Tria<dim>::init_for_refinement),
* bind2nd (mem_fun1 (&TimeStepBase_Wave<dim>::refine_grid),
* TimeStepBase_Tria<dim>::RefinementData (top_threshold,
* bottom_threshold)),
* TimeDependent::TimeSteppingData (0,1),
* TimeDependent::forward);
* @endcode
* TimeStepBase_Wave::refine_grid is a function taking an argument, unlike
* all the other functions used above within the loops. However, in this special
* case the parameter was the same for all timesteps and known before the loop
* was started, so we fixed it and made a function object which to the outside
* world does not take parameters.
*
* Since it is the central function of this class, we finally present a
* stripped down version of the @p do_loop method, which is shown in order
* to provide a better understanding of the internals of this class. For
* brevity we have omitted the parts that deal with backward running loops
* as well as the checks whether wake-up and sleep operations act on timesteps
* outside <tt>0..n_timesteps-1</tt>.
* @code
* template <typename InitFunctionObject, typename LoopFunctionObject>
* void TimeDependent::do_loop (InitFunctionObject init_function,
* LoopFunctionObject loop_function,
* const TimeSteppingData ×tepping_data,
* const Direction direction)
* {
* // initialize the time steps for
* // a round of this loop
* for (unsigned int step=0; step<n_timesteps; ++step)
* init_function (static_cast<typename InitFunctionObject::argument_type>
* (timesteps[step]));
*
* // wake up the first few time levels
* for (int step=-timestepping_data.look_ahead; step<0; ++step)
* for (int look_ahead=0; look_ahead<=timestepping_data.look_ahead; ++look_ahead)
* timesteps[step+look_ahead]->wake_up(look_ahead);
*
*
* for (unsigned int step=0; step<n_timesteps; ++step)
* {
* // first thing: wake up the
* // timesteps ahead as necessary
* for (unsigned int look_ahead=0;
* look_ahead<=timestepping_data.look_ahead; ++look_ahead)
* timesteps[step+look_ahead]->wake_up(look_ahead);
*
*
* // actually do the work
* loop_function (static_cast<typename LoopFunctionObject::argument_type>
* (timesteps[step]));
*
* // let the timesteps behind sleep
* for (unsigned int look_back=0; look_back<=timestepping_data.look_back; ++look_back)
* timesteps[step-look_back]->sleep(look_back);
* };
*
* // make the last few timesteps sleep
* for (int step=n_timesteps; n_timesteps+timestepping_data.look_back; ++step)
* for (int look_back=0; look_back<=timestepping_data.look_back; ++look_back)
* timesteps[step-look_back]->sleep(look_back);
* };
* @endcode
*
*
* @author Wolfgang Bangerth, 1999
*/
class TimeDependent
{
public:
/**
* Structure holding the two basic
* entities that control a loop over
* all time steps: how many time steps
* ahead of the present one we shall
* start waking up timestep objects
* and how many timesteps behind
* we shall call their @p sleep
* method.
*/
struct TimeSteppingData
{
/**
* Constructor; see the different
* fields for a description of the
* meaning of the parameters.
*/
TimeSteppingData (const unsigned int look_ahead,
const unsigned int look_back);
/**
* This denotes the number of timesteps
* the timestepping algorithm needs to
* look ahead. Usually, this number
* will be zero, since algorithms
* looking ahead can't act as
* timestepping schemes since they
* can't compute their data from knowledge
* of the past only and are therefore
* global in time.
*
* However, it may be necessary to look
* ahead in other circumstances, when
* not wanting to access the data of the
* next time step(s), but for example
* to know the next grid, the solution
* of a dual problem on the next
* time level, etc.
*
* Note that for a dual problem walking
* back in time, "looking ahead" means
* looking towards smaller time values.
*
* The value of this number determines,
* how many time steps ahead the
* time step manager start to call
* the @p wake_up function for each
* time step.
*/
const unsigned int look_ahead;
/**
* This is the opposite variable to the
* above one. It denotes the number of
* time steps behind the present one
* for which we need to keep all data
* in order to do the computations on
* the present time level.
*
* For one step schemes (e.g. the
* Euler schemes, or the Crank-Nicolson
* scheme), this value will be one.
*
* The value of this number
* determines, how many time
* steps after having done
* computations on a tim level
* the time step manager will
* call the @p sleep function for
* each time step.
*/
const unsigned int look_back;
};
/**
* Enum offering the different directions
* in which a loop executed by
* @p do_loop may be run.
*/
enum Direction
{
forward, backward
};
/**
* Constructor.
*/
TimeDependent (const TimeSteppingData &data_primal,
const TimeSteppingData &data_dual,
const TimeSteppingData &data_postprocess);
/**
* Destructor. This will delete the
* objects pointed to by the pointers
* given to the <tt>insert_*</tt> and
* @p add_timestep functions, i.e.
* it will delete the objects doing
* the computations on each time step.
*/
virtual ~TimeDependent ();
/**
* Add a timestep at any position. The
* position is a pointer to an existing
* time step object, or a null pointer
* denoting the end of the timestep
* sequence. If @p position is non-null,
* the new time step will be inserted
* before the respective element.
*
* Note that by giving an object
* to this function, the
* TimeDependent object assumes
* ownership of the object; it will
* therefore also take care of
* deletion of the objects its manages.
*
* There is another function,
* @p add_timestep, which inserts a
* time step at the end of the list.
*
* Note that this function does not
* change the timestep numbers stored
* within the other timestep objects,
* nor does it set the timestep number
* of this new timestep. This is only
* done upon calling the @p start_sweep
* function. In not changing the timestep
* numbers, it is simpler to operate
* on a space-time triangulation since
* one can always use the timestep numbers
* that were used in the previous sweep.
*/
void insert_timestep (const TimeStepBase *position,
TimeStepBase *new_timestep);
/**
* Just like @p insert_timestep, but
* insert at the end.
*
* This mechanism usually will result
* in a set-up loop like this
* @code
* for (i=0; i<N; ++i)
* manager.add_timestep(new MyTimeStep());
* @endcode
*/
void add_timestep (TimeStepBase *new_timestep);
/**
* Delete a timestep. This is only
* necessary to call, if you want to
* delete it between two sweeps; at
* the end of the lifetime of this object,
* care is taken automatically of
* deletion of the time step objects.
* Deletion of the object by the
* destructor is done through this
* function also.
*
* Note that this function does
* not change the timestep
* numbers stored within the
* other timestep objects. This
* is only done upon calling the
* @p start_sweep function. In not
* changing the timestep numbers,
* it is simpler to operate on a
* space-time triangulation since
* one can always use the
* timestep numbers that were
* used in the previous sweep.
*/
void delete_timestep (const unsigned int position);
/**
* Solve the primal problem; uses the
* functions @p init_for_primal_problem
* and @p solve_primal_problem of the
* TimeStepBase class through the
* @p do_loop function of this class.
*
* Look ahead and look back are
* determined by the @p timestepping_data_primal
* object given to the constructor.
*/
void solve_primal_problem ();
/**
* Solve the dual problem; uses the
* functions @p init_for_dual_problem
* and @p solve_dual_problem of the
* TimeStepBase class through the
* @p do_loop function of this class.
*
* Look ahead and look back are
* determined by the @p timestepping_data_dual
* object given to the constructor.
*/
void solve_dual_problem ();
/**
* Do a postprocessing round; uses the
* functions @p init_for_postprocessing
* and @p postprocess of the
* TimeStepBase class through the
* @p do_loop function of this class.
*
* Look ahead and look back are
* determined by the @p timestepping_data_postprocess
* object given to the constructor.
*/
void postprocess ();
/**
* Do a loop over all timesteps, call the
* @p init_function at the beginning and
* the @p loop_function of each time step.
* The @p timestepping_data determine how
* many timesteps in front and behind
* the present one the @p wake_up and
* @p sleep functions are called.
*
* To see how this function work, note that
* the function @p solve_primal_problem only
* consists of a call to
* <tt>do_loop (mem_fun(&TimeStepBase::init_for_primal_problem),
* mem_fun(&TimeStepBase::solve_primal_problem),
* timestepping_data_primal, forward);</tt>.
*
* Note also, that the given class from which
* the two functions are taken needs not
* necessarily be TimeStepBase, but it
* could also be a derived class, that is
* @p static_castable from a TimeStepBase.
* The function may be a virtual function
* (even a pure one) of that class, which
* should help if the actual class where it
* is implemented is one which is derived
* through virtual base classes and thus
* unreachable by @p static_cast from the
* TimeStepBase class.
*
* Instead of using the above form, you can
* equally well use
* <tt>bind2nd(mem_fun1(&X::unary_function), arg)</tt>
* which lets the @p do_loop
* function call the given function with
* the specified parameter. Note that you
* need to bind the second parameter since
* the first one implicitly contains
* the object which the function is to
* be called for.
*/
template <typename InitFunctionObject, typename LoopFunctionObject>
void do_loop (InitFunctionObject init_function,
LoopFunctionObject loop_function,
const TimeSteppingData ×tepping_data,
const Direction direction);
/**
* Initialize the objects for the next
* sweep. This function specifically does
* the following: assign each time
* level the number it presently has
* within the array (which may change,
* if time levels are inserted or
* deleted) and transmit the number of
* the present sweep to these objects.
*
* It also calls the @p start_sweep
* function of each time step object,
* after the numbers above are set.
*
* This function is virtual, so you
* may overload it. You should, however
* not forget to call this function as
* well from your overwritten version,
* at best at the beginning of your
* function since this is some kind of
* "constructor-like" function, which
* should be called bottom-up.
*
* The default implementation of this
* function calls @p start_sweep on all
* time step objects.
*/
virtual void start_sweep (const unsigned int sweep_no);
/**
* Analogon to the above
* function, calling @p end_sweep
* of each time step object. The
* same applies with respect to
* the @p virtualness of this
* function as for the previous
* one.
*
* @note This function does not
* guarantee that @p end_sweep is
* called for successive time
* steps successively, rather the order of
* time step objects for which the
* function is called is
* arbitrary. You should
* therefore not assume that that
* function has been called for
* previous time steps
* already. If in multithread
* mode, the @p end_sweep function
* of several time steps may be
* called at once, so you should
* use synchronization
* mechanisms if your program
* requires so.
*/
virtual void end_sweep ();
/**
* @deprecated Use the function without an argument.
* @arg n_threads This argument is ignored.
*/
virtual void end_sweep (const unsigned int n_threads) DEAL_II_DEPRECATED;
/**
* Determine an estimate for the
* memory consumption (in bytes)
* of this object.
*/
std::size_t memory_consumption () const;
/**
* Exception.
*/
DeclException0 (ExcInvalidPosition);
protected:
/**
* Vector holding pointers to the time
* level objects. This is the main data
* this object operates on. Note that
* this object takes possession of the
* objects pointed to by the pointers
* in this collection.
*/
std::vector<SmartPointer<TimeStepBase,TimeDependent> > timesteps;
/**
* Number of the present sweep. This is
* reset by the @p start_sweep function
* called at the outset of each sweep.
*/
unsigned int sweep_no;
/**
* Some flags telling the
* @p solve_primal_problem function what to
* do. See the documentation of this struct
* for more information.
*/
const TimeSteppingData timestepping_data_primal;
/**
* Some flags telling the
* @p solve_dual_problem function what to
* do. See the documentation of this struct
* for more information.
*/
const TimeSteppingData timestepping_data_dual;
/**
* Some flags telling the
* @p postprocess function what to
* do. See the documentation of this struct
* for more information.
*/
const TimeSteppingData timestepping_data_postprocess;
private:
/**
* Do the work of <tt>end_sweep()</tt>
* for some timesteps only. This
* is useful in multithread mode.
*/
void end_sweep (const unsigned int begin_timestep,
const unsigned int end_timestep);
};
/**
* Base class for a time step in time dependent problems. This class provides
* barely more than the basic framework, defining the necessary virtual
* functions (namely @p sleep and @p wake_up), the interface to previous
* and following grids, and some functions to be called before a new loop
* over all time steps is started.
*
* @author Wolfgang Bangerth, 1999
*/
class TimeStepBase : public Subscriptor
{
public:
/**
* Enum denoting the type of problem
* which will have to be solved next.
*/
enum SolutionState
{
primal_problem = 0x0,
dual_problem = 0x1,
postprocess = 0x2
};
/**
* Constructor. Does nothing here apart
* from setting the time.
*/
TimeStepBase (const double time);
/**
* Destructor. At present, this does
* nothing.
*/
virtual ~TimeStepBase ();
/**
* Reconstruct all the data that is
* needed for this time level to work.
* This function serves to reget all
* the variables and data structures
* to work again after they have been
* send to sleep some time before, or
* at the first time we visit this time
* level. In particular, it is used
* to reconstruct the triangulation,
* degree of freedom handlers, to reload
* data vectors in case they have been
* stored to disk, etc.
*
* The actual implementation of
* this function does nothing.
*
* Since this is an important task, you
* should call this function from your
* own function, should you choose to
* overload it in your own class (which
* likely is the case), preferably at
* the beginning so that your function
* can take effect of the triangulation
* already existing.
*/
virtual void wake_up (const unsigned int);
/**
* This is the opposite function
* to @p wake_up. It is used to
* delete data or save it to disk
* after they are no more needed
* for the present sweep. Typical
* kinds of data for this are
* data vectors, degree of
* freedom handlers,
* triangulation objects,
* etc. which occupy large
* amounts of memory and may
* therefore be externalized.
*
* By default, this function does
* nothing.
*/
virtual void sleep (const unsigned int);
/**
* This function is called each time
* before a new sweep is started. You
* may want to set up some fields needed
* in the course of the computations,
* and so on. You should take good care,
* however, not to install large objects,
* which should be deferred until the
* @p wake_up function is called.
*
* A typical action of this function
* would be sorting out names of
* temporary files needed in the
* process of solving, etc.
*
* At the time this function is called,
* the values of @p timestep_no, @p sweep_no
* and the pointer to the previous and
* next time step object already have
* their correct value.
*
* The default implementation of this
* function does nothing.
*/
virtual void start_sweep ();
/**
* This is the analogon to the above
* function, but it is called at the
* end of a sweep. You will usually want
* to do clean-ups in this function,
* such as deleting temporary files
* and the like.
*/
virtual void end_sweep ();
/**
* Before the primal problem is
* solved on each time level, this
* function is called (i.e. before the
* solution takes place on the first
* time level). By default, this function
* sets the @p next_action variable of
* this class. You may overload this
* function, but you should call this
* function within your own one.
*/
virtual void init_for_primal_problem ();
/**
* Same as above, but called before
* a round of dual problem solves.
*/
virtual void init_for_dual_problem ();
/**
* Same as above, but called before
* a round of postprocessing steps.
*/
virtual void init_for_postprocessing ();
/**
* This function is called by the
* manager object when solving the
* primal problem on this time level
* is needed. It is called after
* the @p wake_up function was
* called and before the @p sleep
* function will be called. There
* is no default implementation for
* obvious reasons, so you have
* to overload this function.
*/
virtual void solve_primal_problem () = 0;
/**
* This function is called by the
* manager object when solving the
* dual problem on this time level
* is needed. It is called after
* the @p wake_up function was
* called and before the @p sleep
* function will be called. There
* is a default implementation
* doing plain nothing since some
* problems may not need solving a
* dual problem. However, it
* will abort the program when
* being called anyway, since then you
* should really overload the function.
*/
virtual void solve_dual_problem ();
/**
* This function is called by the
* manager object when postprocessing
* this time level
* is needed. It is called after
* the @p wake_up function was
* called and before the @p sleep
* function will be called. There
* is a default implementation
* doing plain nothing since some
* problems may not need doing a
* postprocess step, e.g. if everything
* was already done when solving the
* primal problem. However, it
* will abort the program when
* being called anyway, since then you
* should really overload the function.
*/
virtual void postprocess_timestep ();
/**
* Return the time value of this time
* step.
*/
double get_time () const;
/**
* Return the number of this time
* step. Note that this number may vary
* between different sweeps, if timesteps
* are added or deleted.
*/
unsigned int get_timestep_no () const;
/**
* Compute the time difference to the
* last time step. If this timestep is
* the first one, this function will
* result in an exception. Though this
* behaviour seems a bit drastic, it
* is appropriate in most cases since
* if there is no previous time step
* you will need special treatment
* anyway and this way no invalid
* value is returned which could lead
* to wrong but unnoticed results of
* your computation. (The only sensible
* value to return in that case would
* not be zero, since valid computation
* can be done with that, but would
* be a denormalized value such as @p NaN.
* However, there is not much difference
* in finding that the results of a
* computation are all denormalized values
* or in getting an exception; in the
* latter case you at least get the exact
* place where your problem lies.)
*/
double get_backward_timestep () const;
/**
* Return the time difference to the next
* time step. With regard to the case
* that there is no next time step,
* the same applies as for the function
* above.
*/
double get_forward_timestep () const;
/**
* Determine an estimate for the
* memory consumption (in bytes)
* of this object.
*
* You will want to overload
* this function in derived
* classes to compute the
* amount memory used by the
* derived class, and add the
* result of this function to
* your result.
*/
virtual std::size_t memory_consumption () const;
/**
* Exception
*/
DeclException0 (ExcCantComputeTimestep);
protected:
/**
* Pointer to the previous time step object
* in the list.
*/
const TimeStepBase *previous_timestep;
/**
* Pointer to the next time step object
* in the list.
*/
const TimeStepBase *next_timestep;
/**
* Number of the sweep we are presently
* in. This number is reset by the time
* level manager before a sweep is
* started.
*/
unsigned int sweep_no;
/**
* Number of the time step, counted from
* zero onwards. This number is reset at
* the start of each sweep by the time
* level manager, since some time steps
* may have been inserted or deleted
* after the previous sweep.
*/
unsigned int timestep_no;
/**
* Discrete time this level operates on.
*/
const double time;
/**
* Variable storing whether the
* solution of a primal or a dual
* problem is actual, or any of
* the other actions
* specified. This variable is
* set by the <tt>init_for_*</tt>
* functions.
*/
unsigned int next_action;
private:
/**
* Reset the pointer to the previous time
* step; shall only be called by the
* time level manager object.
*
* This function is called at the set-up
* of the manager object and whenever
* a timestep is inserted or deleted.
*/
void set_previous_timestep (const TimeStepBase *previous);
/**
* Reset the pointer to the next time
* step; shall only be called by the
* time level manager object.
*
* This function is called at the set-up
* of the manager object and whenever
* a timestep is inserted or deleted.
*/
void set_next_timestep (const TimeStepBase *next);
/**
* Set the number this time step
* has in the list of timesteps.
* This function is called by the
* time step management object at
* the beginning of each sweep, to
* update information which may have
* changed due to addition or deleltion
* of time levels.
*/
void set_timestep_no (const unsigned int step_no);
/**
* Set the number of the sweep we are
* presently in. This function is
* called by the time level management
* object at start-up time of each
* sweep.
*/
void set_sweep_no (const unsigned int sweep_no);
/**
* Copy constructor. I can see no reason
* why someone might want to use it, so
* I don't provide it. Since this class
* has pointer members, making it private
* prevents the compiler to provide it's
* own, incorrect one if anyone chose to
* copy such an object.
*/
TimeStepBase (const TimeStepBase &);
/**
* Copy operator. I can see no reason
* why someone might want to use it, so
* I don't provide it. Since this class
* has pointer members, making it private
* prevents the compiler to provide it's
* own, incorrect one if anyone chose to
* copy such an object.
*/
TimeStepBase &operator = (const TimeStepBase &);
// make the manager object a friend
friend class TimeDependent;
};
/**
* Namespace in which some classes are declared that encapsulate flags
* for the TimeStepBase_Tria() class. These used to be local data
* types of that class, but some compilers choked on some aspects, so
* we put them into a namespace of their own.
*
* @author Wolfgang Bangerth, 2001
*/
namespace TimeStepBase_Tria_Flags
{
/**
* This structure is used to tell the TimeStepBase_Tria() class how grids should
* be handled. It has flags defining the moments where grids shall be
* re-made and when they may be deleted. Also, one variable states whether
* grids should be kept in memory or should be deleted between to uses to
* save memory.
*/
template <int dim>
struct Flags
{
/**
* Default constructor; yields
* an exception, so is not
* really usable.
*/
Flags ();
/**
* Constructor; see the different
* fields for a description of the
* meaning of the parameters.
*/
Flags (const bool delete_and_rebuild_tria,
const unsigned int wakeup_level_to_build_grid,
const unsigned int sleep_level_to_delete_grid);
/**
* This flag determines whether
* the @p sleep and
* @p wake_up functions shall
* delete and rebuild the
* triangulation. While for
* small problems, this is not
* necessary, for large
* problems it is indispensable
* to save memory. The reason
* for this is that there may
* be several hundred time
* levels in memory, each with
* its own triangulation, which
* may require large amounts if
* there are many cells on
* each. Having a total of
* 100.000.000 cells on all
* time levels taken together
* is not uncommon, which makes
* this flag understandable.
*/
const bool delete_and_rebuild_tria;
/**
* This number denotes the
* parameter to the @p wake_up
* function at which it shall
* rebuild the grid. Obviously,
* it shall be less than or
* equal to the @p look_ahead
* number passed to the time
* step management object; if
* it is equal, then the grid
* is rebuilt the first time
* the @p wake_up function is
* called. If
* @p delete_and_rebuild_tria
* is @p false, this number
* has no meaning.
*/
const unsigned int wakeup_level_to_build_grid;
/**
* This is the opposite flag to
* the one above: it determines
* at which call to * @p sleep
* the grid shall be deleted.
*/
const unsigned int sleep_level_to_delete_grid;
/**
* Exception
*/
DeclException1 (ExcInvalidParameter,
int,
<< "The parameter " << arg1 << " has an invalid value.");
};
/**
* This structure is used to tell the TimeStepBase_Tria() class how grids should
* be refined. Before we explain all the different variables, fist some terminology:
* <ul>
* <li> Correction: after having flagged some cells of the triangulation for
* following some given criterion, we may want to change the number of flagged
* cells on this grid according to another criterion that the number of cells
* may be only a certain fraction more or less then the number of cells on
* the previous grid. This change of refinement flags will be called
* "correction" in the sequel.
* <li> Adaption: in order to make the change between one grid and the next not
* to large, we may want to flag some additional cells on one of the two
* grids such that there are not too grave differences. This process will
* be called "adaption".
* </ul>
*
*
* <h3>Description of flags</h3>
*
* <ul>
* <li> @p max_refinement_level: Cut the refinement of cells at a given level.
* This flag does not influence the flagging of cells, so not more cells
* on the coarser levels are flagged than usual. Rather, the flags are all
* set, but when it comes to the actual refinement, the maximum refinement
* level is truncated.
*
* This option is only really useful when you want to compare global
* refinement with adaptive refinement when you don't want the latter
* to refine more than the global refinement.
*
* <li> @p first_sweep_with_correction: When using cell number correction
* as defined above, it may be worth while to start with this only in
* later sweeps, not already in the first one. If this variable is
* zero, then start with the first sweep, else with a higher one. The
* rationale for only starting later is that we do not want to block the
* development of grids at the beginning and only impose restrictions in
* the sweeps where we start to be interested in the actual results of
* the computations.
*
* <li> @p min_cells_for_correction: If we want a more free process of
* grid development, we may want to impose less rules for grids with few
* cells also. This variable sets a lower bound for the cell number of
* grids where corrections are to be performed.
*
* <li> @p cell_number_corridor_top: Fraction of the number of cells by
* which the number of cells of one grid may be higher than that on the
* previous grid. Common values are 10 per cent (i.e. 0.1). The naming
* of the variable results from the goal to define a target corridor
* for the number of cells after refinement has taken place.
*
* <li> @p cell_number_corridor_bottom: Fraction of the number of cells by
* which the number of cells of one grid may be lower than that on the
* previous grid. Common values are 5 per cent (i.e. 0.05). Usually this
* number will be smaller than @p cell_number_corridor_top since an
* increase of the number of cells is not harmful (though it increases
* the numerical amount of work needed to solve the problem) while a
* sharp decrease may reduce the accuracy of the final result even if
* the time steps computed before the decrease were computed to high
* accuracy.
*
* Note however, that if you compute the dual problem as well, then the time
* direction is reversed, so the two values defining the cell number
* corridor should be about equal.
*
* <li> @p correction_relaxations: This is a list of pairs of number with the
* following meaning: just as for @p min_cells_for_correction, it may be
* worth while to reduce the requirements upon grids if the have few cells.
* The present variable stores a list of cell numbers along with some values
* which tell us that the cell number corridor should be enlarged by a
* certain factor. For example, if this list was <tt>((100 5) (200 3) (500 2))</tt>,
* this would mean that for grids with a cell number below 100, the
* <tt>cell_number_corridor_*</tt> variables are to be multiplied by 5 before they
* are applied, for cell numbers below 200 they are to be multiplied by 3,
* and so on.
*
* @p correction_relaxations is actually a vector of such list. Each entry
* in this vector denotes the relaxation rules for one sweep. The last
* entry defines the relaxation rules for all following sweeps. This
* scheme is adopted to allow for stricter corrections in later sweeps
* while the relaxations may be more generous in the first sweeps.
*
* There is a static variable @p default_correction_relaxations which you
* can use as a default value. It is an empty list and thus defines no
* relaxations.
*
* <li> @p cell_number_correction_steps: Usually, if you want the number of
* cells to be corrected, the target corridor for the cell number is computed
* and some additional cells are flagged or flags are removed. But since
* the cell number resulting after flagging and deflagging can not be
* easily computed, it will usually not be within the corridor. We therefore
* need to iteratively get to our goal. Usually, three or four iterations are
* needed, but using this variable, you can reduce the allowed number of
* iterations; breaking the loop after two iterations yields good results
* regularly. Setting the variable to zero will result in no correction
* steps at all.
*
* <li> @p mirror_flags_to_previous_grid: If a cell on the present grid is
* flagged for refinement, also flag the corresponding cell on the previous
* grid. This is useful if, for example, error indicators are computed for
* space-time cells, but are stored for the second grid only. Now, since the
* first grid has the same contributions to the indicators as the second, it
* may be useful to flag both if necessary. This is done if the present
* variable is set.
*
* <li> @p adapt_grids: adapt the present grid to the previous one in the sense
* defined above. What is actually done here is the following: if going from
* the previous to the present grid would result in double refinement or
* double coarsening of some cells, then we try to flag these cells for
* refinement or coarsening such as to avoid the double step. Obviously, more
* than double refinement of coarsening is also caught.
*
* Grid adaption can try to avoid such changes between two grids, but it can
* never promise that they don't occur. This is because the next grid may
* change the present one, but then again there may be jumps in refinement
* level between the present and the previous one; this could only be avoided
* by looping iteratively through all grids, back and forth, until nothing
* changes anymore, which is obviously impossible if there are many time steps
* with very large grids.
* </ul>
*/
template <int dim>
struct RefinementFlags
{
/**
* Typedef of a data type
* describing some relaxations
* of the correction process.
* See the general description
* of this class for more
* information.
*/
typedef std::vector<std::vector<std::pair<unsigned int, double> > > CorrectionRelaxations;
/**
* Default values for the relaxations:
* no relaxations.
*/
static CorrectionRelaxations default_correction_relaxations;
/**
* Constructor. The default
* values are chosen such that
* almost no restriction on the
* mesh refinement is imposed.
*/
RefinementFlags (const unsigned int max_refinement_level = 0,
const unsigned int first_sweep_with_correction = 0,
const unsigned int min_cells_for_correction = 0,
const double cell_number_corridor_top = (1<<dim),
const double cell_number_corridor_bottom = 1,
const CorrectionRelaxations &correction_relaxations = CorrectionRelaxations(),
const unsigned int cell_number_correction_steps = 0,
const bool mirror_flags_to_previous_grid = false,
const bool adapt_grids = false);
/**
* Maximum level of a cell in
* the triangulation of a time
* level. If it is set to zero,
* then no limit is imposed on
* the number of refinements a
* coarse grid cell may
* undergo. Usually, this field
* is used, if for some reason
* you want to limit refinement
* in an adaptive process, for
* example to avoid overly
* large numbers of cells or to
* compare with grids which
* have a certain number of
* refinements.
*/
const unsigned int max_refinement_level;
/**
* First sweep to perform cell
* number correction steps on;
* for sweeps before, cells are
* only flagged and no
* number-correction to
* previous grids is performed.
*/
const unsigned int first_sweep_with_correction;
/**
* Apply cell number correction
* with the previous time level
* only if there are more than
* this number of cells.
*/
const unsigned int min_cells_for_correction;
/**
* Fraction by which the number
* of cells on a time level may
* differ from the number on
* the previous time level
* (first: top deviation,
* second: bottom deviation).
*/
const double cell_number_corridor_top;
/**
* @ref cell_number_corridor_top
*/
const double cell_number_corridor_bottom;
/**
* List of relaxations to the
* correction step.
*/
const std::vector<std::vector<std::pair<unsigned int,double> > > correction_relaxations;
/**
* Number of iterations to be
* performed to adjust the
* number of cells on a time
* level to those on the
* previous one. Zero means: do
* no such iteration.
*/
const unsigned int cell_number_correction_steps;
/**
* Flag all cells which are
* flagged on this timestep for
* refinement on the previous
* one also. This is useful in
* case the error indicator was
* computed by integration over
* time-space cells, but are
* now associated to a grid on
* a discrete time level. Since
* the error contribution comes
* from both grids, however, it
* is appropriate to refine
* both grids.
*
* Since the previous grid does
* not mirror the flags to the
* one before it, this does not
* lead to an almost infinite
* growth of cell numbers. You
* should use this flag with
* cell number correction
* switched on only, however.
*
* Mirroring is done after cell
* number correction is done,
* but before grid adaption, so
* the cell number on this grid
* is not noticeably influenced
* by the cells flagged
* additionally on the previous
* grid.
*/
const bool mirror_flags_to_previous_grid;
/**
* Adapt this grid to the
* previous one.
*/
const bool adapt_grids;
/**
* Exception
*/
DeclException1 (ExcInvalidValue,
double,
<< "The following value does not fulfill the requirements: " << arg1);
};
/**
* Structure given to the actual refinement function, telling it which
* thresholds to take for coarsening and refinement. The actual refinement
* criteria are loaded by calling the virtual function
* @p get_tria_refinement_criteria.
*/
template <int dim>
struct RefinementData
{
/**
* Constructor
*/
RefinementData (const double refinement_threshold,
const double coarsening_threshold=0);
/**
* Threshold for refinement:
* cells having a larger value
* will be refined (at least in
* the first round; subsequent
* steps of the refinement
* process may flag other cells
* as well or remove the flag
* from cells with a criterion
* higher than this threshold).
*/
const double refinement_threshold;
/**
* Same threshold for
* coarsening: cells with a
* smaller threshold will be
* coarsened if possible.
*/
const double coarsening_threshold;
/**
* Exception
*/
DeclException1 (ExcInvalidValue,
double,
<< "The following value does not fulfill the requirements: " << arg1);
};
}
/**
* Specialisation of TimeStepBase which addresses some aspects of grid handling.
* In particular, this class is thought to make handling of grids available that
* are adaptively refined on each time step separately or with a loose coupling
* between time steps. It also takes care of deleting and rebuilding grids when
* memory resources are a point, through the @p sleep and @p wake_up functions
* declared in the base class.
*
* In addition to that, it offers functions which do some rather hairy refinement
* rules for time dependent problems, trying to avoid too much change in the grids
* between subsequent time levels, while also trying to retain the freedom of
* refining each grid separately. There are lots of flags and numbers controlling
* this function, which might drastically change the behaviour of the function -- see
* the description of the flags for further information.
*
* @author Wolfgang Bangerth, 1999; large parts taken from the wave program, by Wolfgang Bangerth 1998
*/
template <int dim>
class TimeStepBase_Tria : public TimeStepBase
{
public:
/**
* Typedef the data types of the
* TimeStepBase_Tria_Flags()
* namespace into local scope.
*/
typedef typename TimeStepBase_Tria_Flags::Flags<dim> Flags;
typedef typename TimeStepBase_Tria_Flags::RefinementFlags<dim> RefinementFlags;
typedef typename TimeStepBase_Tria_Flags::RefinementData<dim> RefinementData;
/**
* Extension of the enum in the base
* class denoting the next action to be
* done.
*/
enum SolutionState
{
grid_refinement = 0x1000
};
/**
* Default constructor. Does nothing but
* throws an exception. We need to have
* such a constructor in order to satisfy
* the needs of derived classes, which
* take this class as a virtual base class
* and don't need to call this constructor
* of they are not terminal classes. The
* compiler would like to know a
* constructor to call anyway since it
* can't know that the class is not
* terminal.
*/
TimeStepBase_Tria ();
/**
* Constructor. Takes a coarse
* grid from which the grids on this
* time level will be derived and
* some flags steering the behaviour
* of this object.
*
* The ownership of the coarse grid
* stays with the creator of this
* object. However, it is locked
* from destruction to guarantee
* the lifetime of the coarse grid
* is longer than it is needed by
* this object.
*
* You need to give the general flags
* structure to this function since it
* is needed anyway; the refinement
* flags can be omitted if you do
* not intend to call the refinement
* function of this class.
*/
TimeStepBase_Tria (const double time,
const Triangulation<dim, dim> &coarse_grid,
const Flags &flags,
const RefinementFlags &refinement_flags = RefinementFlags());
/**
* Destructor. At present, this does
* not more than releasing the lock on
* the coarse grid triangulation given
* to the constructor.
*/
virtual ~TimeStepBase_Tria ();
/**
* Reconstruct all the data that is
* needed for this time level to work.
* This function serves to reget all
* the variables and data structures
* to work again after they have been
* send to sleep some time before, or
* at the first time we visit this time
* level. In particular, it is used
* to reconstruct the triangulation,
* degree of freedom handlers, to reload
* data vectors in case they have been
* stored to disk, etc. By default,
* this function rebuilds the triangulation
* if the respective flag has been set to
* destroy it in the @p sleep function.
* It does so also the first time we
* hit this function and @p wakeup_level
* equals <tt>flags.wakeup_level_to_build_grid</tt>,
* independently of the value of the
* mentioned flag. (Actually, it does so
* whenever the triangulation pointer
* equals the Null pointer and the
* value of @p wakeup_level is right.)
*
* Since this is an important task, you
* should call this function from your
* own function, should you choose to
* overload it in your own class (which
* likely is the case), preferably at
* the beginning so that your function
* can take effect of the triangulation
* already existing.
*/
virtual void wake_up (const unsigned int wakeup_level);
/**
* This is the opposite function
* to @p wake_up. It is used to
* delete data or save it to disk
* after they are no more needed
* for the present sweep. Typical
* kinds of data for this are
* data vectors, degree of
* freedom handlers,
* triangulation objects,
* etc. which occupy large
* amounts of memory and may
* therefore be externalized.
*
* By default, if the user specified so
* in the flags for this object, the
* triangulation is deleted and the
* refinement history saved such that
* the respective @p wake_up function can
* rebuild it. You should therefore call
* this function from your overloaded
* version, preferably at the end so
* that your function can use the
* triangulation as long as ou need it.
*/
virtual void sleep (const unsigned int);
/**
* Do the refinement according to the
* flags passed to the constructor of this
* object and the data passed to this
* function. For a description of the
* working of this function refer to the
* general documentation of this class.
*
* In fact, this function does not
* actually refine or coarsen the
* triangulation, but only sets the
* respective flags. This is done because
* usually you will not need the grid
* immediately afterwards but only
* in the next sweep, so it suffices
* to store the flags and rebuild it
* the next time we need it. Also, it
* may be that the next time step
* would like to add or delete some
* flags, so we have to wait anyway
* with the use of this grid.
*/
void refine_grid (const RefinementData data);
/**
* Respective init function for the
* refinement loop; does nothing in
* the default implementation, apart from
* setting @p next_action to
* @p grid_refinement but can
* be overloaded.
*/
virtual void init_for_refinement ();
/**
* Virtual function that should fill
* the vector with the refinement
* criteria for the present triangulation.
* It is used within the @p refine_grid
* function to get the criteria for
* the present time step, since they
* can't be passed through its
* argument when using the loop of
* the time step management object.
*/
virtual void get_tria_refinement_criteria (Vector<float> &criteria) const = 0;
/**
* The refinement
* flags of the triangulation are stored
* in a local variable thus allowing
* a restoration. The coarsening flags
* are also stored.
*/
void save_refine_flags ();
/**
* Determine an estimate for the
* memory consumption (in bytes)
* of this object.
*
* You will want to overload
* this function in derived
* classes to compute the
* amount memory used by the
* derived class, and add the
* result of this function to
* your result.
*/
virtual std::size_t memory_consumption () const;
/**
* Exception
*/
DeclException0 (ExcGridNotDeleted);
protected:
/**
* Triangulation used at this
* time level. Since this is
* something that every time
* stepping scheme needs to have,
* we can safely put it into the
* base class. Note that the
* triangulation is frequently
* deleted and rebuilt by the
* functions @p sleep and
* @p wake_up to save memory, if
* such a behaviour is specified
* in the @p flags structure.
*/
SmartPointer<Triangulation<dim, dim>,TimeStepBase_Tria<dim> > tria;
/**
* Pointer to a grid which is to
* be used as the coarse grid for
* this time level. This pointer
* is set through the
* constructor; ownership remains
* with the owner of this
* management object.
*/
SmartPointer<const Triangulation<dim, dim>,TimeStepBase_Tria<dim> > coarse_grid;
/**
* Some flags about how this time level
* shall behave. See the documentation
* of this struct to find out more about
* that.
*/
const Flags flags;
/**
* Flags controlling the refinement
* process; see the documentation of the
* respective structure for more
* information.
*/
const RefinementFlags refinement_flags;
private:
/**
* Vectors holding the refinement and
* coarsening flags of the different
* sweeps on this time level. The
* vectors therefore hold the history
* of the grid.
*/
std::vector<std::vector<bool> > refine_flags;
/**
* @ref refine_flags
*/
std::vector<std::vector<bool> > coarsen_flags;
/**
* Restore the grid according to the saved
* data. For this, the coarse grid is
* copied and the grid is stepwise
* rebuilt using the saved flags.
*/
void restore_grid ();
};
/*----------------------------- template functions ------------------------------*/
template <typename InitFunctionObject, typename LoopFunctionObject>
void TimeDependent::do_loop (InitFunctionObject init_function,
LoopFunctionObject loop_function,
const TimeSteppingData ×tepping_data,
const Direction direction)
{
// the following functions looks quite
// disrupted due to the recurring switches
// for forward and backward running loops.
//
// I chose to switch at every place where
// it is needed, since it is so easy
// to overlook something when you change
// some code at one place when it needs
// to be changed at a second place, here
// for the other direction, also.
const unsigned int n_timesteps = timesteps.size();
// initialize the time steps for
// a round of this loop
for (unsigned int step=0; step<n_timesteps; ++step)
switch (direction)
{
case forward:
init_function (static_cast<typename InitFunctionObject::argument_type>
(&*timesteps[step]));
break;
case backward:
init_function (static_cast<typename InitFunctionObject::argument_type>
(&*timesteps[n_timesteps-step-1]));
break;
};
// wake up the first few time levels
for (int step=-timestepping_data.look_ahead; step<0; ++step)
for (int look_ahead=0;
look_ahead<=static_cast<int>(timestepping_data.look_ahead); ++look_ahead)
switch (direction)
{
case forward:
if (step+look_ahead >= 0)
timesteps[step+look_ahead]->wake_up(look_ahead);
break;
case backward:
if (n_timesteps-(step+look_ahead) < n_timesteps)
timesteps[n_timesteps-(step+look_ahead)]->wake_up(look_ahead);
break;
};
for (unsigned int step=0; step<n_timesteps; ++step)
{
// first thing: wake up the
// timesteps ahead as necessary
for (unsigned int look_ahead=0;
look_ahead<=timestepping_data.look_ahead; ++look_ahead)
switch (direction)
{
case forward:
if (step+look_ahead < n_timesteps)
timesteps[step+look_ahead]->wake_up(look_ahead);
break;
case backward:
if (n_timesteps > (step+look_ahead))
timesteps[n_timesteps-(step+look_ahead)-1]->wake_up(look_ahead);
break;
};
// actually do the work
switch (direction)
{
case forward:
loop_function (static_cast<typename LoopFunctionObject::argument_type>
(&*timesteps[step]));
break;
case backward:
loop_function (static_cast<typename LoopFunctionObject::argument_type>
(&*timesteps[n_timesteps-step-1]));
break;
};
// let the timesteps behind sleep
for (unsigned int look_back=0;
look_back<=timestepping_data.look_back; ++look_back)
switch (direction)
{
case forward:
if (step>=look_back)
timesteps[step-look_back]->sleep(look_back);
break;
case backward:
if (n_timesteps-(step-look_back) <= n_timesteps)
timesteps[n_timesteps-(step-look_back)-1]->sleep(look_back);
break;
};
};
// make the last few timesteps sleep
for (int step=n_timesteps;
step<static_cast<int>(n_timesteps+timestepping_data.look_back); ++step)
for (int look_back=0;
look_back<=static_cast<int>(timestepping_data.look_back); ++look_back)
switch (direction)
{
case forward:
if ((step-look_back >= 0)
&&
(step-look_back < static_cast<int>(n_timesteps)))
timesteps[step-look_back]->sleep(look_back);
break;
case backward:
if ((step-look_back >= 0)
&&
(step-look_back < static_cast<int>(n_timesteps)))
timesteps[n_timesteps-(step-look_back)-1]->sleep(look_back);
break;
};
}
DEAL_II_NAMESPACE_CLOSE
/*---------------------------- time-dependent.h ---------------------------*/
#endif
/*---------------------------- time-dependent.h ---------------------------*/
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