ENH: changes reducing the peak memory consumption of shape sensitivities

The multiplier of grad(dxdb) is a volTensorField which, by itself, is
memory consuming. The function computing it though was sloppy in terms
of memory management, constituting the peak memory consumption during an
adjoint optimisation. Initial changes to remedy the problem include the
deallocation of some of the volTensorFields included in the computation
of grad(dxdb) once unneeded, the utilisation of volSymmTensorFields
instead of volTensorFields where possible and avoiding allocating some
unnecessary intermediate fields.

Actions to further reduce memory consumption:

- For historical reasons, the code computes/stores the transpose of
  grad(dxdb), which is then transposed when used in the computation of
  the FI or the ESI sensitivity derivatives.  This redundant
  transposition can be avoid, saving the allocation of an additional
  volTensorField, but the changes need to permeate a number of places in
  the code that contribute to grad(dxdb) (e.g.  ATC, adjoint turbulence
  models, adjoint MRF, etc).
- Allocation of unnecessary pointers in the objective class should be
  avoided.