# Time integration¶

## Options¶

BOUT++ can be compiled with several different time-integration solvers , and at minimum should have Runge-Kutta (RK4) and PVODE (BDF/Adams) solvers available.

The solver library used is set using the `solver:type`

option, so
either in BOUT.inp:

```
[solver]
type = rk4 # Set the solver to use
```

or on the command line by adding `solver:type=pvode`

for example:

```
mpirun -np 4 ./2fluid solver:type=rk4
```

**NB**: Make sure there are no spaces around the “=” sign:
`solver:type =pvode`

won’t work (probably). Table Table 10 gives
a list of time integration solvers, along with any compile-time options
needed to make the solver available.

Name |
Description |
Compile options |
---|---|---|

euler |
Euler explicit method (example only) |
Always available |

rk4 |
Runge-Kutta 4th-order explicit method |
Always available |

rkgeneric |
Generic Runge Kutta explicit methods |
Always available |

rk3ssp |
3rd-order Strong Stability Preserving |
Always available |

splitrk |
Split RK3-SSP and RK-Legendre |
Always available |

pvode |
1998 PVODE with BDF method |
Always available |

cvode |
SUNDIALS CVODE. BDF and Adams methods |
–with-cvode |

ida |
SUNDIALS IDA. DAE solver |
–with-ida |

arkode |
SUNDIALS ARKODE IMEX solver |
–with-arkode |

petsc |
PETSc TS methods |
–with-petsc |

imexbdf2 |
IMEX-BDF2 scheme |
–with-petsc |

beuler / snes |
Backward Euler with SNES solvers |
–with-petsc |

Each solver can have its own settings which work in slightly different ways, but some common settings and which solvers they are used in are given in table Table 11.

Option |
Description |
Solvers used |
---|---|---|

atol |
Absolute tolerance |
rk4, pvode, cvode, ida, imexbdf2, beuler |

rtol |
Relative tolerance |
rk4, pvode, cvode, ida, imexbdf2, beuler |

mxstep |
Maximum internal steps per output step |
rk4, imexbdf2 |

max_timestep |
Maximum timestep |
rk4, cvode |

timestep |
Starting timestep |
rk4, euler, imexbdf2, beuler |

adaptive |
Adapt timestep? (Y/N) |
rk4, imexbdf2 |

use_precon |
Use a preconditioner? (Y/N) |
pvode, cvode, ida, imexbdf2 |

mudq, mldq |
BBD preconditioner settings |
pvode, cvode, ida |

mukeep, mlkeep |
||

maxl |
Maximum number of linear iterations |
cvode, imexbdf2 |

max_nonlinear_iterations |
Maximum number of nonlinear iterations |
cvode, imexbdf2, beuler |

use_jacobian |
Use user-supplied Jacobian? (Y/N) |
cvode |

adams_moulton |
Use Adams-Moulton method rather than BDF |
cvode |

diagnose |
Collect and print additional diagnostics |
cvode, imexbdf2, beuler |

The most commonly changed options are the absolute and relative solver
tolerances, `atol`

and `rtol`

which should be varied to check
convergence.

## CVODE¶

The most commonly used time integration solver is CVODE, or its older version PVODE. CVODE has several advantages over PVODE, including better support for preconditioning and diagnostics.

Enabling diagnostics output using `solver:diagnose=true`

will print a
set of outputs for each timestep similar to:

```
CVODE: nsteps 51, nfevals 69, nniters 65, npevals 126, nliters 79
-> Newton iterations per step: 1.274510e+00
-> Linear iterations per Newton iteration: 1.215385e+00
-> Preconditioner evaluations per Newton: 1.938462e+00
-> Last step size: 1.026792e+00, order: 5
-> Local error fails: 0, nonlinear convergence fails: 0
-> Stability limit order reductions: 0
1.000e+01 149 2.07e+01 78.3 0.0 10.0 0.9 10.8
```

When diagnosing slow performance, key quantities to look for are
nonlinear convergence failures, and the number of linear iterations per
Newton iteration. A large number of failures, and close to 5 linear
iterations per Newton iteration are a sign that the linear solver is not
converging quickly enough, and hitting the default limit of 5
iterations. This limit can be modified using the `solver:maxl`

setting. Giving it a large value e.g. `solver:maxl=1000`

will show how
many iterations are needed to solve the linear system. If the number of
iterations becomes large, this may be an indication that the system is
poorly conditioned, and a preconditioner might help improve performance.
See Preconditioning.

CVODE can set constraints to keep some quantities positive, non-negative,
negative or non-positive. These constraints can be activated by setting the
option `solver:apply_positivity_constraints=true`

, and then in the section
for a certain variable (e.g. `[n]`

), setting the option
`positivity_constraint`

to one of `positive`

, `non_negative`

,
`negative`

, or `non_positive`

.

## IMEX-BDF2¶

This is an IMplicit-EXplicit time integration solver, which allows the evolving function to be split into two parts: one which has relatively long timescales and can be integrated using explicit methods, and a part which has short timescales and must be integrated implicitly. The order of accuracy is variable (up to 4th-order currently), and an adaptive timestep can be used.

To use the IMEX-BDF2 solver, set the solver type to `imexbdf2`

,
e.g. on the command-line add `solver:type=imexbdf2`

or in the
options file:

```
[solver]
type = imexbdf2
```

The order of the method is set to 2 by default, but can be increased up to a maximum of 4:

```
[solver]
type = imexbdf2
maxOrder = 3
```

This is a multistep method, so the state from previous steps are used
to construct the next one. This means that at the start, when there
are no previous steps, the order is limited to 1 (backwards Euler
method). Similarly, the second step is limited to order 2, and so
on. At the moment the order is not adapted, so just increases until
reaching `maxOrder`

.

At each step the explicit (non-stiff) part of the function is called, and combined with previous timestep values. The implicit part of the function is then solved using PETSc’s SNES, which consists of a nonlinear solver (usually modified Newton iteration), each iteration of which requires a linear solve (usually GMRES). Settings which affect this implicit part of the solve are:

Option |
Default |
Description |
---|---|---|

atol |
1e-16 |
Absolute tolerance on SNES solver |

rtol |
1e-10 |
Relative tolerance on SNES solver |

max_nonlinear_it |
5 |
Maximum number of nonlinear iterations If adaptive timestepping is used then failure will cause timestep reduction |

maxl |
20 |
Maximum number of linear iterations If adaptive, failure will cause timestep reduction |

predictor |
1 |
Starting guess for the nonlinear solve Specifies order of extrapolating polynomial |

use_precon |
false |
Use user-supplied preconditioner? |

matrix_free |
true |
Use Jacobian-free methods? If false, calculates the Jacobian matrix using finite difference |

use_coloring |
true |
If not matrix free, use coloring to speed up calculation of the Jacobian |

Note that the SNES tolerances `atol`

and `rtol`

are set very conservatively by default. More reasonable
values might be 1e-10 and 1e-5, but this must be explicitly asked for in the input options.

The predictor extrapolates from previous timesteps to get a starting estimate for the value at the next timestep. This estimate is then used to initialise the SNES nonlinear solve. The value is the order of the extrapolating polynomial, so 1 (the default) is a linear extrapolation from the last two steps, 0 is the same as the last step. A value of -1 uses the explicit update to the state as the starting guess, i.e. assuming that the implicit part of the problem is small. This is usually not a good guess.

To diagnose what is happening in the time integration, for example to see why it is
failing to converge or why timesteps are small, there are two settings which can be
set to `true`

to enable:

`diagnose`

outputs a summary at each output time, similar to CVODE. This contains information like the last timestep, average number of iterations and number of convergence failures.`verbose`

prints information at every internal step, with more information on the values used to modify timesteps, and the reasons for solver failures.

By default adaptive timestepping is turned on, using several factors to modify the timestep:

If the nonlinear solver (SNES) fails to converge, either because it diverges or exceeds the iteration limits

`max_nonlinear_its`

or`maxl`

. Reduces the timestep by 2 and tries again, giving up after 10 failures.Every

`nadapt`

internal timesteps (default 4), the error is checked by taking the timestep twice: Once with the current order of accuracy, and once with one order of accuracy lower. The difference between the solutions is then used to estimate the timestep required to achieve the required tolerances. If this is much larger or smaller than the current timestep, then the timestep is modified.The timestep is kept within user-specified maximum and minimum ranges.

The options which control this behaviour are:

Option |
Default |
Description |
---|---|---|

adaptive |
true |
Turns on adaptive timestepping |

timestep |
output timestep |
If adaptive sets the starting timestep. If not adaptive, timestep fixed at this value |

dtMin |
1e-10 |
Minimum timestep |

dtMax |
output timestep |
Maximum timestep |

mxstep |
1e5 |
Maximum number of internal steps between outputs |

nadapt |
4 |
How often is error checked and timestep adjusted? |

adaptRtol |
1e-3 |
Target relative tolerance for adaptive timestep |

scaleCushDown |
1.0 |
Timestep scale factor below which the timestep is modified. By default the timestep is always reduced |

scaleCushUp |
1.5 |
Minimum timestep scale factor based on adaptRtol above which the timestep will be modified. Currently the timestep increase is limited to 25% |

## Split-RK¶

The `splitrk`

solver type uses Strang splitting to combine two
explicit Runge Kutta schemes:

2nd order Runge-Kutta-Legendre method for the diffusion (parabolic) part. These schemes use multiple stages to increase stability, rather than accuracy; this is always 2nd order, but the stable timestep for diffusion problems increases as the square of the number of stages. The number of stages is an input option, and can be arbitrarily large.

3rd order SSP-RK3 scheme for the advection (hyperbolic) part http://www.cscamm.umd.edu/tadmor/pub/linear-stability/Gottlieb-Shu-Tadmor.SIREV-01.pdf

Each timestep consists of

A half timestep of the diffusion part

A full timestep of the advection part

A half timestep of the diffusion part

Options to control the behaviour of the solver are:

Option |
Default |
Description |
---|---|---|

timestep |
output timestep |
If adaptive sets the starting timestep. If not adaptive, timestep fixed at this value |

nstages |
10 |
Number of stages in RKL step. Must be > 1 |

diagnose |
false |
Print diagnostic information |

And the adaptive timestepping options:

Option |
Default |
Description |
---|---|---|

adaptive |
true |
Turn on adaptive timestepping |

atol |
1e-10 |
Absolute tolerance |

rtol |
1e-5 |
Relative tolerance |

max_timestep |
output timestep |
Maximum internal timestep |

max_timestep_change |
2 |
Maximum factor by which the timestep by which the time step can be changed at each step |

mxstep |
1000 |
Maximum number of internal steps before output |

adapt_period |
1 |
Number of internal steps between tolerance checks |

## Backward Euler - SNES¶

The `beuler`

or `snes`

solver type (either name can be used) is
intended mainly for solving steady-state problems, so integrates in
time using a stable but low accuracy method (Backward Euler). It uses
PETSc’s SNES solvers to solve the nonlinear system at each timestep,
and adjusts the internal timestep to keep the number of SNES
iterations within a given range.

Option |
Default |
Description |
---|---|---|

snes_type |
newtonls |
PETSc SNES nonlinear solver (try anderson, qn) |

ksp_type |
gmres |
PETSc KSP linear solver |

pc_type |
ilu / bjacobi |
PETSc PC preconditioner |

max_nonlinear_iterations |
20 |
If exceeded, solve restarts with timestep / 2 |

maxl |
20 |
Maximum number of linear iterations |

atol |
1e-12 |
Absolute tolerance of SNES solve |

rtol |
1e-5 |
Relative tolerance of SNES solve |

upper_its |
80% max |
If exceeded, next timestep reduced by 10% |

lower_its |
50% max |
If under this, next timestep increased by 10% |

timestep |
1 |
Initial timestep |

predictor |
true |
Use linear predictor? |

matrix_free |
false |
Use matrix free Jacobian-vector product? |

use_coloring |
true |
If |

lag_jacobian |
50 |
Re-use the Jacobian for successive inner solves |

kspsetinitialguessnonzero |
false |
If true, Use previous solution as KSP initial |

use_precon |
false |
Use user-supplied preconditioner? If false, the default PETSc preconditioner is used |

diagnose |
false |
Print diagnostic information every iteration |

The predictor is linear extrapolation from the last two timesteps. It seems to be
effective, but can be disabled by setting `predictor = false`

.

The default `newtonls`

SNES type can be very effective if combined
with Jacobian coloring: The coloring enables the Jacobian to be
calculated relatively efficiently; once a Jacobian matrix has been
calculated, effective preconditioners can be used to speed up
convergence. It is important to note that the coloring assumes a star
stencil and so won’t work for every problem: It assumes that each
evolving quantity is coupled to all other evolving quantities on the
same grid cell, and on all the neighbouring grid cells. If the RHS
function includes Fourier transforms, or matrix inversions
(e.g. potential solves) then these will introduce longer-range
coupling and the Jacobian calculation will give spurious
results. Generally the method will then fail to converge. Two
solutions are to a) switch to matrix-free (`matrix_free=true`

), or b)
solve the matrix inversion as a constraint.

The SNES type
can be set through PETSc command-line options, or in the BOUT++
options as setting `snes_type`

. Good choices for unpreconditioned
problems where the Jacobian is not available (`matrix_free=true`

) seem to be anderson
and qn
(quasinewton).

Preconditioner types:

On one processor the ILU solver is typically very effective, and is usually the default

The Hypre package can be installed with PETSc and used as a preconditioner. One of the options available in Hypre is the Euler parallel ILU solver. Enable with command-line args

`-pc_type hypre -pc_hypre_type euclid -pc_hypre_euclid_levels k`

where`k`

is the level (1-8 typically).

## ODE integration¶

The `Solver`

class can be used to solve systems of ODEs inside a physics
model: Multiple Solver objects can exist besides the main one used for
time integration. Example code is in `examples/test-integrate`

.

To use this feature, systems of ODEs must be represented by a class
derived from `PhysicsModel`

.

```
class MyFunction : public PhysicsModel {
public:
int init(bool restarting) {
// Initialise ODE
// Add variables to solver as usual
solver->add(result, "result");
...
}
int rhs(BoutReal time) {
// Specify derivatives of fields as usual
ddt(result) = ...
}
private:
Field3D result;
};
```

To solve this ODE, create a new `Solver`

object:

```
Solver* ode = Solver::create(Options::getRoot()->getSection("ode"));
```

This will look in the section `[ode]`

in the options file.
**Important:** To prevent this solver overwriting the main restart files
with its own restart files, either disable restart files:

```
[ode]
enablerestart = false
```

or specify a different directory to put the restart files:

```
[ode]
restartdir = ode # Restart files ode/BOUT.restart.0.nc, ...
```

Create a model object, and pass it to the solver:

```
MyFunction* model = new MyFunction();
ode->setModel(model);
```

Finally tell the solver to perform the integration:

```
ode->solve(5, 0.1);
```

The first argument is the number of steps to take, and the second is the size of each step. These can also be specified in the options, so calling

```
ode->solve();
```

will cause ode to look in the input for `nout`

and `timestep`

options:

```
[ode]
nout = 5
timestep = 0.1
```

Finally, delete the model and solver when finished:

```
delete model;
delete solver;
```

**Note:** If an ODE needs to be solved multiple times, at the moment it
is recommended to delete the solver, and create a new one each time.

## Preconditioning¶

At every time step, an implicit scheme such as BDF has to solve a non-linear problem to find the next solution. This is usually done using Newton’s method, each step of which involves solving a linear (matrix) problem. For \(N\) evolving variables is an \(N\times N\) matrix and so can be very large. By default matrix-free methods are used, in which the Jacobian \(\mathcal{J}\) is approximated by finite differences (see next subsection), and so this matrix never needs to be explicitly calculated. Finding a solution to this matrix can still be difficult, particularly as \(\delta t\) gets large compared with some time-scales in the system (i.e. a stiff problem).

A preconditioner is a function which quickly finds an approximate solution to this matrix, speeding up convergence to a solution. A preconditioner does not need to include all the terms in the problem being solved, as the preconditioner only affects the convergence rate and not the final solution. A good preconditioner can therefore concentrate on solving the parts of the problem with the fastest time-scales.

A simple example 1 is a coupled wave equation, solved in the
`test-precon`

example code:

First, calculate the Jacobian of this set of equations by taking partial derivatives of the time-derivatives with respect to each of the evolving variables

In this case \(\frac{\partial u}{\partial t}\) doesn’t depend on \(u\) nor \(\frac{\partial v}{\partial t}\) on \(v\), so the diagonal is empty. Since the equations are linear, the Jacobian doesn’t depend on \(u\) or \(v\) and so

In general for non-linear functions \(\mathcal{J}\) gives the change in time-derivatives in response to changes in the state variables \(u\) and \(v\).

In implicit time stepping, the preconditioner needs to solve an equation

where \(\mathcal{I}\) is the identity matrix, and \(\gamma\) depends on the time step and method (e.g. \(\gamma = \delta t\) for backwards Euler method). For the simple wave equation problem, this is

This matrix can be block inverted using Schur factorisation 2

where \({\mathbf{P}}_{Schur} = {\mathbf{D}} - {\mathbf{L}}{\mathbf{E}}^{-1}{\mathbf{U}}\) Using this, the wave problem becomes:

The preconditioner is implemented by defining a function of the form

```
int precon(BoutReal t, BoutReal gamma, BoutReal delta) {
...
}
```

which takes as input the current time, the \(\gamma\) factor
appearing above, and \(\delta\) which is only important for
constrained problems (not discussed here… yet). The current state of
the system is stored in the state variables (here `u`

and `v`

),
whilst the vector to be preconditioned is stored in the time derivatives
(here `ddt(u)`

and `ddt(v)`

). At the end of the preconditioner the
result should be in the time derivatives. A preconditioner which is just
the identity matrix and so does nothing is therefore:

```
int precon(BoutReal t, BoutReal gamma, BoutReal delta) {
}
```

To implement the preconditioner in equation (2), first apply the rightmost matrix to the given vector:

```
int precon(BoutReal t, BoutReal gamma, BoutReal delta) {
mesh->communicate(ddt(u));
//ddt(u) = ddt(u);
ddt(v) = gamma*Grad_par(ddt(u)) + ddt(v);
```

note that since the preconditioner is linear, it doesn’t depend on
\(u\) or \(v\). As in the RHS function, since we are taking a
differential of `ddt(u)`

, it first needs to be communicated to
exchange guard cell values.

The second matrix

doesn’t alter \(u\), but solves a parabolic equation in the
parallel direction. There is a solver class to do this called
`InvertPar`

which solves the equation \((A + B\partial_{||}^2)x =
b\) where \(A\) and \(B\) are `Field2D`

or constants 3. In
`PhysicsModel::init()`

we create one of these solvers:

```
InvertPar *inv; // Parallel inversion class
int init(bool restarting) {
...
inv = InvertPar::Create();
inv->setCoefA(1.0);
...
}
```

In the preconditioner we then use this solver to update \(v\):

```
inv->setCoefB(-SQ(gamma));
ddt(v) = inv->solve(ddt(v));
```

which solves \(ddt(v) \rightarrow (1 - \gamma^2\partial_{||}^2)^{-1} ddt(v)\). The final matrix just updates \(u\) using this new solution for \(v\)

```
mesh->communicate(ddt(v));
ddt(u) = ddt(u) + gamma*Grad_par(ddt(v));
```

Finally, boundary conditions need to be imposed, which should be consistent with the conditions used in the RHS:

```
ddt(u).applyBoundary("dirichlet");
ddt(v).applyBoundary("dirichlet");
```

To use the preconditioner, pass the function to the solver in
`PhysicsModel::init()`

:

```
int init(bool restarting) {
solver->setPrecon(precon);
...
}
```

then in the `BOUT.inp`

settings file switch on the preconditioner

```
[solver]
type = cvode # Need CVODE or PETSc
use_precon = true # Use preconditioner
rightprec = false # Use Right preconditioner (default left)
```

## Jacobian function¶

## DAE constraint equations¶

Using the IDA or IMEX-BDF2 solvers, BOUT++ can solve Differential
Algebraic Equations (DAEs), in which algebraic constraints are used for
some variables. Examples of how this is used are in the
`examples/constraints`

subdirectory.

First the variable to be constrained is added to the solver, in a similar way to time integrated variables. For example

```
Field3D phi;
...
solver->constraint(phi, ddt(phi), "phi");
```

The first argument is the variable to be solved for (constrained). The
second argument is the field to contain the residual (error). In this
example the time derivative field `ddt(phi)`

is used, but it could
be another `Field3D`

variable. The solver will attempt to
find a solution to the first argument (`phi`

here) such that the
second argument (`ddt(phi)`

) is zero to within tolerances.

In the RHS function the residual should be calculated. In this example
(`examples/constraints/drift-wave-constraint`

) we have:

```
ddt(phi) = Delp2(phi) - Vort;
```

so the time integration solver includes the algebraic constraint
`Delp2(phi) = Vort`

i.e. (\(\nabla_\perp^2\phi = \omega\)).

## IMEX-BDF2¶

This is an implicit-explicit multistep method, which uses the PETSc
library for the SNES nonlinear solver. To use this solver, BOUT++ must
have been configured with PETSc support, and the solver type set to
`imexbdf2`

```
[solver]
type = imexbdf2
```

For examples of using IMEX-BDF2, see the `examples/IMEX/`

subdirectory, in particular the `diffusion-nl`

, `drift-wave`

and
`drift-wave-constrain`

examples.

The time step is currently fixed (not adaptive), and defaults to the
output timestep. To set a smaller internal timestep, the
`solver:timestep`

option can be set. If the timestep is too large,
then the explicit part of the problem may become unstable, or the
implicit part may fail to converge.

The implicit part of the problem can be solved matrix-free, in which case the Jacobian-vector product is approximated using finite differences. This is currently the default, and can be set on the command-line using the options:

```
solver:matrix_free=true -snes_mf
```

Note the `-snes_mf`

flag which is passed to PETSc. When using a matrix
free solver, the Jacobian is not calculated and so the amount of memory
used is minimal. However, since the Jacobian is not known, many standard
preconditioning methods cannot be used, and so in many cases a custom
preconditioner is needed to obtain good convergence.

An experimental feature uses PETSc’s ability to calculate the Jacobian using finite differences. This can then speed up the linear solve, and allows more options for preconditioning. To enable this option:

```
solver:matrix_free=false
```

There are two ways to calculate the Jacobian: A brute force method which is set up by this call to PETSc which is generally very slow, and a “coloring” scheme which can be quite fast and is the default. Coloring uses knowledge of where the non-zero values are in the Jacobian, to work out which rows can be calculated simultaneously. The coloring code in IMEX-BDF2 currently assumes that every field is coupled to every other field in a star pattern: one cell on each side, a 7 point stencil for 3D fields. If this is not the case for your problem, then the solver may not converge.

The brute force method can be useful for comparing the Jacobian structure, so to turn off coloring:

```
solver:use_coloring=false
```

Using MatView calls, or the `-mat_view`

PETSc options, the non-zero
structure of the Jacobian can be plotted or printed.

## Monitoring the simulation output¶

Monitoring of the solution can be done at two levels: output monitoring,
and timestep monitoring. Output monitoring occurs only when data is
written to file, whereas timestep monitoring is every timestep and so
(usually) much more frequent. Examples of both are in
`examples/monitor`

and `examples/monitor-newapi`

.

**Output monitoring**: At every output timestep the solver calls a
monitor method of the BoutMonitor class, which writes the output dump file,
calculates and prints timing information and estimated time remaining. If you
want to run additional code or write data to a different file, you can
implement the outputMonitor method of PhysicsModel:

```
int outputMonitor(BoutReal simtime, int iter, int nout)
```

The first input is the current simulation time, the second is the output number, and the last is the total number of outputs requested. This method is called by a monitor object PhysicsModel::modelMonitor, which writes the restart files at the same time. You can change the frequency at which the monitor is called by calling, in PhysicsModel::init:

```
modelMonitor.setTimestep(new_timestep)
```

where `new_timestep`

is a BoutReal which is either `timestep*n`

or
`timestep/n`

for an integer `n`

. Note that this will change the frequency
of writing restarts as well as of calling `outputMonitor()`

.

You can also add custom monitor object(s) for more flexibility.

You can call your output monitor class whatever you like, but it must be a
subclass of Monitor and provide the method `call`

which takes 4 inputs and
returns an int:

```
class MyOutputMonitor : public Monitor {
int call(Solver *solver, BoutReal simtime, int iter, int NOUT) {
...
}
};
```

The first input is the solver object, the second is the current
simulation time, the third is the output number, and the last is the
total number of outputs requested. To get the solver to call this
function every output time, define a `MyOutputMonitor`

object as a member of your
PhysicsModel:

```
MyOutputMonitor my_output_monitor;
```

and put in your `PhysicsModel::init()`

code:

```
solver->addMonitor(&my_output_monitor);
```

Note that the solver only stores a pointer to the `Monitor`

, so you must make sure
the object is persistent, e.g. a member of a `PhysicsModel`

class, not a local
variable in a constructor. If you want to later remove a monitor, you can do so with:

```
solver->removeMonitor(&my_output_monitor);
```

A simple example using this monitor is:

```
class MyOutputMonitor: public Monitor{
public:
MyOutputMonitor(BoutReal timestep=-1):Monitor(timestep){};
int call(Solver *solver, BoutReal simtime, int iter, int NOUT) override;
};
int MyOutputMonitor::call(Solver *solver, BoutReal simtime, int iter, int NOUT) {
output.write("Output monitor, time = %e, step %d of %d\n",
simtime, iter, NOUT);
return 0;
}
MyOutputMonitor my_monitor;
int init(bool restarting) {
solver->addMonitor(&my_monitor);
}
```

See the monitor example (`examples/monitor`

) for full code.

**Timestep monitoring**: This uses functions instead of objects. First define a
monitor function:

```
int my_timestep_monitor(Solver *solver, BoutReal simtime, BoutReal lastdt) {
...
}
```

where `simtime`

will again contain the current simulation time, and
`lastdt`

the last timestep taken. Add this function to the solver:

```
solver->addTimestepMonitor(my_timestep_monitor);
```

Timestep monitoring is disabled by default, unlike output monitoring. To enable timestep monitoring, set in the options file (BOUT.inp):

```
[solver]
monitor_timestep = true
```

or put on the command line `solver:monitor_timestep=true`

. When this
is enabled, it will change how solvers like CVODE and PVODE (the default
solvers) are used. Rather than being run in NORMAL mode, they will
instead be run in SINGLE_STEP mode (see the SUNDIALS notes
here:https://computation.llnl.gov/casc/sundials/support/notes.html).
This may in some cases be less efficient.

## Implementation internals¶

The solver is the interface between BOUT++ and the time-integration
code such as SUNDIALS. All solvers implement the `Solver`

class interface (see `src/solver/generic_solver.hxx`

).

First all the fields which are to be evolved need to be added to the solver. These are always done in pairs, the first specifying the field, and the second the time-derivative:

```
void add(Field2D &v, Field2D &F_v, const char* name);
```

This is normally called in the `PhysicsModel::init()`

initialisation routine.
Some solvers (e.g. IDA) can support constraints, which need to be added
in the same way as evolving fields:

```
bool constraints();
void constraint(Field2D &v, Field2D &C_v, const char* name);
```

The `constraints()`

function tests whether or not the current solver
supports constraints. The format of `constraint(...)`

is the same as
`add`

, except that now the solver will attempt to make `C_v`

zero.
If `constraint`

is called when the solver doesn’t support them then an
error should occur.

If the physics model implements a preconditioner or Jacobian-vector multiplication routine, these can be passed to the solver during initialisation:

```
typedef int (*PhysicsPrecon)(BoutReal t, BoutReal gamma, BoutReal delta);
void setPrecon(PhysicsPrecon f); // Specify a preconditioner
typedef int (*Jacobian)(BoutReal t);
void setJacobian(Jacobian j); // Specify a Jacobian
```

If the solver doesn’t support these functions then the calls will just be ignored.

Once the problem to be solved has been specified, the solver can be initialised using:

```
int init();
```

which returns an error code (0 on success). This is currently called in bout++.cxx:

```
if (solver.init()) {
output.write("Failed to initialise solver. Aborting\n");
return(1);
}
```

which passes the (physics module) RHS function `PhysicsModel::rhs()`

to the
solver along with the number and size of the output steps.

```
typedef int (*MonitorFunc)(BoutReal simtime, int iter, int NOUT);
int run(MonitorFunc f);
```

- 1
Taken from a talk by L.Chacon available here https://bout2011.llnl.gov/pdf/talks/Chacon_bout2011.pdf

- 2
See paper https://arxiv.org/abs/1209.2054 for an application to 2-fluid equations

- 3
This

`InvertPar`

class can handle cases with closed field-lines and twist-shift boundary conditions for tokamak simulations