EulerImplicitSolver
This component belongs to the category of integration schemes or ODE Solver. This scheme builds the system following an implicit scheme: forces are considered based on the state information at the next time step , unknown at the current time step.
Looking at continuum mechanics, the linear system arises from the dynamic equation. This dynamic is written as follows but other physics (like heat transfer) result in a similar equation:
where is the degrees of freedom, the mass matrix and a function of (and possibly its derivatives) acting on our system. In the case of the EulerImplicitSolver, this equation can be written:
by using a Taylor expansion, we get:
since we have: , then:
Finally, gathering the unknown (depending on ) in the left hand side, we have:
We can notice the appearance of the stiffness matrix : . The stiffness matrix is a symetric matrix, can either be linear or nonlinear regarding .
The computation of the right hand side is done by the ForceFields. Just like in the explicit case (see EulerExplicitSolver), the explicit contribution is implemented in the same function addForce()
. The second part is computed by the function addDForce()
.
It is important to note that, depending on the choice of LinearSolver (direct or iterative), the API functions called to build the left hand side system matrix will not be the same:

if a direct solver is used, the mass is computed in the
addMToMatrix()
and the stiffness part is computed in the functionaddKToMatrix()
in ForceFields 
if an iterative solver is used, the mass is iteratively multiplied by the unknown within the
addMDx()
, as the stiffness part within the functionaddDForce()
in ForceFields.
Considering viscosity
As you might have notice, the Taylor expansion detailed above does not take into account a possible dependency of the force on the velocity. By considering it, the effect of velocity will result in a viscosity effect through the damping matrix .
Let’s apply the Taylor expansion taking into account the velocity and we get:
Depending on the choice of LinearSolver (direct or iterative), the API functions called to build the damping matrix on the left hand side will not be the same:

if a direct solver is used, the damping matrix is computed in the
addBToMatrix()
in ForceFields 
if an iterative solver is used, the damping is iteratively multiplied by the unknown within the
addDForce()
just as the stiffness partin the functionaddDForce()
in ForceFields.
Sequence diagram
Data
The data trapezoidalScheme modifies the EulerImplicitSolver scheme and implements the trapezoidal rule:
This results in the following linear system:
The use of the trapezoidal rule is known to increase robustness and stability to the time integration due to the order 2 in time of this trapezoidal scheme.
The option is given to the user to hadd numerical Rayleigh damping using the data rayleighStiffness and rayleighMass. The description of the meaning and effect of these Rayleigh damping coefficients is given in ODESolver.
The data firstOrder enables to use the EulerImplicitSolver at the order 1, which means that only the first derivative of the DOFs (state) x appears in the equation. Higher derivatives are absent. This option is for instance well suited for heat diffusion equation using only the first derivative of the temperature field:
.
Usage
The EulerImplicitSolver requires:
 a LinearSolver to solve the linear system
 and a MechanicalObject to store the state vectors.
Example
This component is used as follows in XML format:
<EulerImplicitSolver name="ODEsolver" rayleighStiffness="0.1" rayleighMass="0.1" />
or using Python:
node.createObject('EulerImplicitSolver', name='ODEsolver', rayleighStiffness='0.1' rayleighMass='0.1')
An example scene involving a StaticSolver is available in examples/Components/solver/EulerImplicitSolver.scn
Last modified: 21 August 2019