In the 'standard' and 'UaGradU' options for the ATC term of the adjoint
equations, there is an option to add 'aritificial dissipation', by
adding and subtracting a multiple of the adjoint convection term with
different discretizations. The implicit part was not multiplied with the
ATClimiter whereas the explicit one was, leading to mismatched
contributions in the areas affected by the ATClimiter, which could
affect the sensitivity derivatives.
- ATCstandard, ATCUaGradU:
the ATC is now added as a dimensioned field and not as an fvMatrix
to UaEqn. This get rid of many unnecessary allocations.
- ATCstandard:
gradU is cached within the class to avoid its re-computation in
every adjoint iteration of the steady state solver.
- Inlined a number of functions within the primal and adjoint solvers.
This probably has a negligible effect since they likely were inlined
by the compiler either way.
- The momentum diffusivity at the boundary, used by the adjoint boundary
conditions, was computed for the entire field and, then, only the
boundary field of each adjoint boundary condition was used. If many
outlet boundaries exist, the entire nuEff field would be computed as
many times as the number of boundaries, leading to an unnecessary
computational overhead.
- Outlet boundary conditions (both pressure and velocity) use the local
patch gradient to compute their fluxes. This patch gradient requires
the computation of the adjacent cell gradient, which is done on the
fly, on a per patch basis. To compute this patch adjacent gradient
however, the field under the grad sign is interpolated on the entire
mesh. If many outlets exist, this leads to a huge computational
overhead. Solved by caching the interpolated field to the database and
re-using it, in a way similar to the caching of gradient fields (see
fvc::grad).
WIP: functions returning references to primal and adjoint boundary
fields within boundaryAdjointContributions seem to have a non-negligible
overhead for cases with many patches. No easy work-around here since
these are virtual and cannot be inlined.
WIP: introduced the code structure for caching the contributions to
the adjoint boundary conditions that depend only on the primal fields
and reusing. The process needs to be completed and evaluated, to make
sure that the extra code complexity is justified by gains in
performance.
- 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).
- 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
A set of libraries and executables creating a workflow for performing
gradient-based optimisation loops. The main executable (adjointOptimisationFoam)
solves the flow (primal) equations, followed by the adjoint equations and,
eventually, the computation of sensitivity derivatives.
Current functionality supports the solution of the adjoint equations for
incompressible turbulent flows, including the adjoint to the Spalart-Allmaras
turbulence model and the adjoint to the nutUSpaldingWallFunction, [1], [2].
Sensitivity derivatives are computed with respect to the normal displacement of
boundary wall nodes/faces (the so-called sensitivity maps) following the
Enhanced Surface Integrals (E-SI) formulation, [3].
The software was developed by PCOpt/NTUA and FOSS GP, with contributions from
Dr. Evangelos Papoutsis-Kiachagias,
Konstantinos Gkaragounis,
Professor Kyriakos Giannakoglou,
Andy Heather
and contributions in earlier version from
Dr. Ioannis Kavvadias,
Dr. Alexandros Zymaris,
Dr. Dimitrios Papadimitriou
[1] A.S. Zymaris, D.I. Papadimitriou, K.C. Giannakoglou, and C. Othmer.
Continuous adjoint approach to the Spalart-Allmaras turbulence model for
incompressible flows. Computers & Fluids, 38(8):1528–1538, 2009.
[2] E.M. Papoutsis-Kiachagias and K.C. Giannakoglou. Continuous adjoint methods
for turbulent flows, applied to shape and topology optimization: Industrial
applications. 23(2):255–299, 2016.
[3] I.S. Kavvadias, E.M. Papoutsis-Kiachagias, and K.C. Giannakoglou. On the
proper treatment of grid sensitivities in continuous adjoint methods for shape
optimization. Journal of Computational Physics, 301:1–18, 2015.
Integration into the official OpenFOAM release by OpenCFD
