Old:
- Previous versions created k and epsilon fields by default, and
then processed omega and nuTilda fields if present.
- Depending on the choice of turbulence model, not all of these fields
would be used, and could lead to errors when running some utilities
due to erroneous values.
- If the omega field did not exist, it would be derived from the epsilon
field, and also inherit the epsilon boundary conditions (wall
functions)
New:
- This version will only update fields that already exist on file, i.e.
will not generate any new fields, and will preserve the boundary
conditions
inline Foam::vector Foam::septernion::transformPoint(const vector& v) const
{
return r().transform(v - t());
}
Now there is a 1:1 correspondence between septernion and
spatialTransform and a septernion constructor from spatialTransform
provided.
Additionally "septernion::transform" has been renamed
"septernion::transformPoint" to clarify that it transforms coordinate
points rather than displacements or other relative vectors.
'w' is now obtained from 'v' using the relation w = sqrt(1 - |sqr(v)|)
and 'v' is stored in the joint state field 'q' and integrated in the
usual manner but corrected using quaternion transformations.
Currently supported solvers: symplectic, Newmark, CrankNicolson
The symplectic solver should only be used if iteration over the forces
and body-motion is not required. Newmark and CrankNicolson both require
iteration to provide 2nd-order behavior.
See applications/test/rigidBodyDynamics/spring for an example of the
application of the Newmark solver.
This development is sponsored by Carnegie Wave Energy Ltd.
This is a more convenient way of maintaining the state or multiple
states (for higher-order integration), storing, retrieving and passing
between processors.
applications/test/rigidBodyDynamics/spring: Test of the linear spring with damper restraint
Damped simple harmonic motion of a weight on a spring is simulated and
the results compared with analytical solution
Test-spring
gnuplot spring.gnuplot
evince spring.eps
This development is sponsored by Carnegie Wave Energy Ltd.
e.g. (fvc::interpolate(HbyA) & mesh.Sf()) -> fvc::flux(HbyA)
This removes the need to create an intermediate face-vector field when
computing fluxes which is more efficient, reduces the peak storage and
improved cache coherency in addition to providing a simpler and cleaner
API.
//- Disallow default shallow-copy assignment
//
// Assignment of UList<T> may need to be either shallow (copy pointer)
// or deep (copy elements) depending on context or the particular type
// of list derived from UList and it is confusing and prone to error
// for the default assignment to be either. The solution is to
// disallow default assignment and provide separate 'shallowCopy' and
// 'deepCopy' member functions.
void operator=(const UList<T>&) = delete;
//- Copy the pointer held by the given UList.
inline void shallowCopy(const UList<T>&);
//- Copy elements of the given UList.
void deepCopy(const UList<T>&);
Contributed by Mattijs Janssens.
1. Any non-blocking data exchange needs to know in advance the sizes to
receive so it can size the buffer. For "halo" exchanges this is not
a problem since the sizes are known in advance but or all other data
exchanges these sizes need to be exchanged in advance.
This was previously done by having all processors send the sizes of data to
send to the master and send it back such that all processors
- had the same information
- all could work out who was sending what to where and hence what needed to
be received.
This is now changed such that we only send the size to the
destination processor (instead of to all as previously). This means
that
- the list of sizes to send is now of size nProcs v.s. nProcs*nProcs before
- we cut out the route to the master and back by using a native MPI
call
It causes a small change to the API of exchange and PstreamBuffers -
they now return the sizes of the local buffers only (a labelList) and
not the sizes of the buffers on all processors (labelListList)
2. Reversing the order of the way in which the sending is done when
scattering information from the master processor to the other
processors. This is done in a tree like fashion. Each processor has a
set of processors to receive from/ send to. When receiving it will
first receive from the processors with the least amount of
sub-processors (i.e. the ones which return first). When sending it
needs to do the opposite: start sending to the processor with the
most amount of sub-tree since this is the critical path.
