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Parts of the adjoint optimisation library were re-designed to generalise the way sensitivity derivatives (SDs) are computed and to allow easier extension to primal problems other than the ones governed by incompressible flows. In specific: - the adjoint solver now holds virtual functions returning the part of SDs that depends only on the primal and the adjoint fields. - a new class named designVariables was introduced which, apart from defining the design variables of the optimisation problem and providing hooks for updating them in an optimisation loop, provides the part of the SDs that affects directly the flow residuals (e.g. geometric variations in shape optimisation, derivatives of source terms in topology optimisation, etc). The final assembly of the SDs happens here, with the updated sensitivity class acting as an intermediate. With the new structure, when the primal problem changes (for instance, passive scalars are included), the same design variables and sensitivity classes can be re-used for all physics, with additional contributions to the SDs being limited (and contained) to the new adjoint solver to be implemented. The old code structure would require new SD classes for each additional primal problem. As a side-effect, setting up a case has arguably become a bit easier and more intuitive. Additional changes include: --------------------------- - Changes in the formulation and computation of shape sensitivity derivatives using the E-SI approach. The latter is now derived directly from the FI approach, with proper discretization for the terms and boundary conditions that emerge from applying the Gauss divergence theorem used to transition from FI to E-SI. When E-SI and FI are based on the same Laplace grid displacement model, they are now numerically equivalent (the previous formulation proved the theoretical equivalence of the two approaches but numerical results could differ, depending on the case). - Sensitivity maps at faces are now computed based (and are deriving from) sensitivity maps at points, with a constistent point-to-face interpolation (requires the differentiation of volPointInterpolation). - The objective class now allocates only the member pointers that correspond to the non-zero derivatives of the objective w.r.t. the flow and geometric quantities, leading to a reduced memory footprint. Additionally, contributions from volume-based objectives to the adjoint equations have been re-worked, removing the need for objectiveManager to be virtual. - In constrained optimisation, an adjoint solver needs to be present for each constraint function. For geometric constraints though, no adjoint equations need to solved. This is now accounted for through the null adjoint solver and the geometric objectives which do not allocate adjoint fields for this kind of constraints, reducing memory requirements and file clutter. - Refactoring of the updateMethod to collaborate with the new designVariables. Additionally, all updateMethods can now read and write restart data in binary, facilitating exact continuation. Furthermore, code shared by various quasi-Newton methods (BFGS, DBFGS, LBFGS, SR1) has been organised in the namesake class. Over and above, an SQP variant capable of tackling inequality constraints has been added (ISQP, with I indicating that the QP problem in the presence of inequality constraints is solved through an interior point method). Inequality constraints can be one-sided (constraint < upper-value) or double-sided (lower-value < constraint < upper-value). - Bounds can now be defined for the design variables. For volumetricBSplines in specific, these can be computed as the mid-points of the control points and their neighbouring ones. This usually leads to better-defined optimisation problems and reduces the chances of an invalid mesh during optimisation. - Convergence criteria can now be defined for the optimisation loop which will stop if the relative objective function reduction over the last objective value is lower than a given threshold and constraints are satisfied within a give tolerance. If no criteria are defined, the optimisation will run for the max. given number of cycles provided in controlDict. - Added a new grid displacement method based on the p-Laplacian equation, which seems to outperform other PDE-based approaches. TUT: updated the shape optimisation tutorials and added a new one showcasing the use of double-sided constraints, ISQP, applying no-overlapping constraints to volumetric B-Splines control points and defining convergence criteria for the optimisation loop.
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