- bundles frequently used 'gather/scatter' patterns more consistently.
- combineAllGather -> combineGather + broadcast
- listCombineAllGather -> listCombineGather + broadcast
- mapCombineAllGather -> mapCombineGather + broadcast
- allGatherList -> gatherList + scatterList
- reduce -> gather + broadcast (ie, allreduce)
- The allGatherList currently wraps gatherList/scatterList, but may be
replaced with a different algorithm in the future.
STYLE: PstreamCombineReduceOps.H is mostly unneeded now
- controlPointsDefinition is now controled by a class with
runTimeSelection.
- Added a new controlPointsDefinition option that translates, rotates
and scales a given box. The required entries have the same meaning as
in the Paraview 'Transform' filter, facilitating the transition between the
visual placement of control boxes (e.g. in Paraview) and their setup
in the code.
- Improved performance during the parameterization, sensitivity
computation and grid displacement phases by re-using already computed
basis functions.
The if(Pstream::master()) clause in NURBS3DVolume::writeCpsInDict() was
causing the fileName of the regIOobject not to be allocated in all
processors, giving problems when masterUncollatedFileOperation::masterOp
was called by collatedFileOperation::writeObject for the mkDirOp.
- with '&&' conditions, often better to check for non-null autoPtr
first (it is cheap)
- check as bool instead of valid() method for cleaner code, especially
when the wrapped item itself has a valid/empty or good.
Also when handling multiple checks.
Now
if (ptr && ptr->valid())
if (ptr1 || ptr2)
instead
if (ptr.valid() && ptr->valid())
if (ptr1.valid() || ptr2.valid())
- previously introduced `getOrDefault` as a dictionary _get_ method,
now complete the transition and use it everywhere instead of
`lookupOrDefault`. This avoids mixed usage of the two methods that
are identical in behaviour, makes for shorter names, and promotes
the distinction between "lookup" access (ie, return a token stream,
locate and return an entry) and "get" access (ie, the above with
conversion to concrete types such as scalar, label etc).
When more than one volumetric B-Splines control boxes are present, the
sensitivity constituents corresponding to the non-active design
variables were not bounded(zeroed) correctly. The resultant
sensitivities, used in the optimization, were bounded correctly, so this
was more a bug pertaining to the output file of the sensitivities rather
than a functional one.
- Failed due to double*Matrix<float> multiplication.
Style changes
- use SquareMatrix with Identity on construction
- use Zero in constructors
- remove trailing space and semi-colons
The adjoint library is enhanced with new functionality enabling
automated shape optimisation loops. A parameterisation scheme based on
volumetric B-Splines is introduced, the control points of which act as
the design variables in the optimisation loop [1, 2]. The control
points of the volumetric B-Splines boxes can be defined in either
Cartesian or cylindrical coordinates.
The entire loop (solution of the flow and adjoint equations, computation
of sensitivity derivatives, update of the design variables and mesh) is
run within adjointOptimisationFoam. A number of methods to update the
design variables are implemented, including popular Quasi-Newton methods
like BFGS and methods capable of handling constraints like loop using
the SQP or constraint projection.
The software was developed by PCOpt/NTUA and FOSS GP, with contributions from
Dr. Evangelos Papoutsis-Kiachagias,
Konstantinos Gkaragounis,
Professor Kyriakos Giannakoglou,
Andy Heather
[1] E.M. Papoutsis-Kiachagias, N. Magoulas, J. Mueller, C. Othmer,
K.C. Giannakoglou: 'Noise Reduction in Car Aerodynamics using a
Surrogate Objective Function and the Continuous Adjoint Method with
Wall Functions', Computers & Fluids, 122:223-232, 2015
[2] E. M. Papoutsis-Kiachagias, V. G. Asouti, K. C. Giannakoglou,
K. Gkagkas, S. Shimokawa, E. Itakura: ‘Multi-point aerodynamic shape
optimization of cars based on continuous adjoint’, Structural and
Multidisciplinary Optimization, 59(2):675–694, 2019
