Using Parametric Effectiveness for Efficient CAD-Based Adjoint Optimization

Agarwal, D. D.; Kapellos, Christos; Robinson, Trevor; Armstrong, Cecil

doi:10.14733/cadaps.2019.703-719

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…PE is the ratio of the maximum objective function improvement that can be achieved using the parameterization in hands to the one that could be achieved if surface nodal points were free to move independently; both are subjected to the constraint of the same root-mean-squared boundary displacement. PE can be computed at the cost of a single adjoint run [20]. Herein, we refrain from presenting the definition of PE; this can be found in [19] and [20].…”

Section: B-rep Deformation Driven By Handlesmentioning

confidence: 99%

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RBF-based morphing of B-Rep models for use in aerodynamic shape optimization

Gagliardi

Giannakoglou

2019

Advances in Engineering Software

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This paper presents a shape parameterization method based on morphing that acts directly on CAD-compatible Boundary-Representations (B-Rep), effectively integrated into aerodynamic shape optimization. The proposed technique requires the definition of a small number of "handles", which are strategically placed around or on the B-Rep shapes to be optimized. Displacement vectors associated with these handles are used as design variables in the optimization method. Using Radial Basis Functions (RBF) as an interpolation method, these displacements are transferred from the handles to the Non-Uniform Rational Basis-Splines (NURBS) control points of the B-Rep model; this approach offers the advantage that the updated surface remains in CAD format and is thus exportable to a STEP file. The proposed method combines two successive deformation steps. Each deformation is controlled by an independent set of handles, increasing the flexibility of the morphing action. A strategy for updating the CFD surface grid to the already changed B-Rep models enables the seamless inclusion of the proposed method into any optimization loop, avoiding any into-the-loop dependence on grid generation packages. The surface grid is updated by computing new nodal coordinates based on the updated NURBS parametric coordinates; these are computed according to changes in the parametric domain of trimmed surfaces by an RBF-based interpolation. The displacement of the surface nodes is then used to displace the CFD volume grid. The tool is differentiated and integrated into gradient-based optimization loops using the adjoint method to compute the gradient of the objective and constraint functions with respect to the surface nodal positions. The performance of the proposed method is assessed in four aerodynamic shape optimization problems, concerning a 2D airfoil, a duct, an aircraft model and a compressor stationary blading. An assessment of the proposed method based on parametric effectiveness, in the 2D case, is also presented.

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Section: B-rep Deformation Driven By Handlesmentioning

confidence: 99%

“…PE can be computed at the cost of a single adjoint run [20]. Herein, we refrain from presenting the definition of PE; this can be found in [19] and [20].…”

Section: B-rep Deformation Driven By Handlesmentioning

confidence: 99%

RBF-based morphing of B-Rep models for use in aerodynamic shape optimization

Gagliardi

Giannakoglou

2019

Advances in Engineering Software

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“…In previous work by the authors [9,[14][15][16], CAD system APIs were used to access the parameters defined within a commercial CAD system, CATIA V5, and they were used within a gradient-based optimisation framework. However, the analysis mesh was regenerated at each optimisation step, limiting the applicability of the optimisation framework.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Shape Optimisation Using Parametric CAD and Discrete Adjoint

2022

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This paper presents an optimisation framework based on an open-source CAD system and CFD solver. In this work, the high-fidelity flow solutions and surface sensitivities are obtained using the primal and discrete adjoint formulations of SU2. This paper shows the direct use of CAD models for optimisation by developing a CAD system application programming interface and creating a link between the CAD-MESH-CFD analysis. A methodology to obtain geometric sensitivities is introduced, enabling the calculation of accurate gradients with respect to CAD variables and the deformation of the analysis mesh during the optimisation process. This methodology guarantees that the new surface mesh lies exactly on the CAD geometry. The optimisation framework is applied to a rectangular wing and a three section high-lift aerofoil configuration derived from the NASA CRM-HL configuration. Both geometries are created using FreeCAD. The performance objectives are to decrease the drag while constraining the lift to be above a desired value. The twist distribution of the wing was parameterised within the CAD system, allowing the minimisation of the induced drag by obtaining a nearly elliptical lift distribution. For the high-lift configuration, the position and rotation of the flap and slat were parameterised with respect to the original section; the final optimised positions yield a drag reduction of approximately 16.5%. These results show that the CAD parameterisation can be reliably used to obtain efficient optimums while operating directly on the CAD geometries.

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Imposing C₀ and C₁ continuity constraints during CAD‐based adjoint optimization

Damigos

2021

Numerical Methods in Fluids

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This article presents a method for performing adjoint-based aerodynamic shape optimization by manipulating the standard CAD geometry of the shape to be optimized. A standard CAD file gives access to the boundary representation (BRep) of the shape and consequently its boundary surfaces which are usually trimmed patches. This is a sensible choice as the open format of such files is a requirement for the computation of the shape derivatives. The method addresses the continuity issues that emerge during the update of the shape by imposing up to C 1 constraints between different CAD patches. A parameterization scheme based on NURBS surfaces is then defined, in which the aforementioned constraints are inherently satisfied. The proposed method is firstly demonstrated in a simple geometric case and then in the full-scale optimization of industrial-like cases such as the S-section of a cooling duct and the tail surface of a passenger car.

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Using Parametric Effectiveness for Efficient CAD-Based Adjoint Optimization

Cited by 5 publications

References 27 publications

RBF-based morphing of B-Rep models for use in aerodynamic shape optimization

RBF-based morphing of B-Rep models for use in aerodynamic shape optimization

Aerodynamic Shape Optimisation Using Parametric CAD and Discrete Adjoint

Imposing C₀ and C₁ continuity constraints during CAD‐based adjoint optimization

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Using Parametric Effectiveness for Efficient CAD-Based Adjoint Optimization

Cited by 5 publications

References 27 publications

RBF-based morphing of B-Rep models for use in aerodynamic shape optimization

RBF-based morphing of B-Rep models for use in aerodynamic shape optimization

Aerodynamic Shape Optimisation Using Parametric CAD and Discrete Adjoint

Imposing C0 and C1 continuity constraints during CAD‐based adjoint optimization

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Imposing C₀ and C₁ continuity constraints during CAD‐based adjoint optimization