Optimisation of a U-Bend Using a Cad-Based Adjoint Method With Differentiated Cad Kernel

Auriemma, Salvatore; Banović, Mladen; Mykhaskiv, Orest; Legrand, Hervé; Müeller, Jens-Dominik; Verstraete, Tom; Walther, Andrea

doi:10.7712/100016.2089.10065

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…This term is calculated using the algorithmically differentiated version of OCCT. 1,8 The total sensitivity of (3) is then used in an optimisation loop.…”

Section: Iia Discrete Adjoint Equationmentioning

confidence: 99%

CAD-based shape optimisation of the NASA CRM wing-body intersection using differentiated CAD-kernel

Mykhaskiv

Mohanamuraly

Müller

et al. 2017

35th AIAA Applied Aerodynamics Conference

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Numerical shape optimisation is becoming an essential tool in aeronautic design. Highfidelity optimisation with many design variables will require gradient-based optimisation, adjoint methods can compute gradients very efficiently. CAD models are used on every stage of engineering product development from design to manufacturing. Ideally, the CAD system is kept inside the optimisation loop to maintain a consistent CAD model during the optimisation, as often practised in gradient-free optimisation. However, typical commercial CAD systems do not offer derivative computation, and standard CAD parametrisations may not define a suitable design space for the optimisation. In this work, we use the automatically differentiated (AD) version of Open Cascade Technology (OCCT) CAD-kernel, which robustly provides accurate derivatives with respect to CAD parameters. The kernel is used to handle implicit CAD parametrisations, e.g. move control points of NURBS surfaces, automatically re-parametrise these surfaces, compute intersections between them and perform corresponding mesh morphing. We apply the differentiated CAD tool and parametrisation to high-fidelity aerodynamic shape optimisation of the NASA CRM wing-body intersection and exercise the CAD method for the pylon-nacelle modification.

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“…This term is calculated using the algorithmically differentiated version of OCCT. 1,8 The total sensitivity of (3) is then used in an optimisation loop.…”

Section: Iia Discrete Adjoint Equationmentioning

confidence: 99%

CAD-based shape optimisation of the NASA CRM wing-body intersection using differentiated CAD-kernel

Mykhaskiv

Mohanamuraly

Müller

et al. 2017

35th AIAA Applied Aerodynamics Conference

Self Cite

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“…The second term (CAD sensitivity) represents the derivative of the surface grid points X S with respect to the CAD model design parameters. This part is calculated in the automatically dierentiated version of OCCT [1]. The dierentiated OCCT provides the derivatives for almost every possible CAD parametrisation and geometrical manipulation.…”

Section: Differentiated Occt and Adjoint Cfd Couplingmentioning

confidence: 99%

“…In [19] a small in-house CAD kernel was automatically dierentiated and provides analytical derivatives for NURBS. In this paper we exploit recently dierentiated version of the OCCT kernel [1], [2]. Although dierentiation of a complete CAD-kernel is a non-trivial and time-consuming task, it allows us to get exact derivatives without numerical noise in any of CAD modelling algorithms.…”

Section: Introductionmentioning

confidence: 99%

NURBS-based and Parametric-based Shape Optimisation with Differentiated CAD Kernel

Mykhaskiv¹,

Müller²,

Mohanamuraly³

et al. 2017

Proceedings of CAD'17

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Numerical optimisation is becoming an essential industrial method in engineering design for shapes immersed in uids. High-delity optimisation requires ne design spaces with many design variables, which can only be tackled with gradient-based optimisation methods. CAD packages that are open-source or commercially available do not provide shape derivatives, but impose to compute them with expensive, inaccurate and nonrobust nite-dierences.The present work is the rst demonstration of obtaining exact shape derivatives with respect to CAD design parametrisation by applying algorithmic dierentiation to a complete CAD system, in this case the Open Cascade Technology (OCCT) CAD-kernel. The extension of OCCT to perform shape optimisation is shown by using parametric models based on explicit parametrisations of the CAD model and on implicit parametrisations based on the BRep (NURBS). In addition, we demonstrate the imposition of geometric constraints for both approaches, a salient part of industrial design, and an intuitive method of storing them in standard CAD format. The proposed method is applied to redesign TU Berlin Stator Testcase.

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“…Gradients can also be computed by applying automatic differentiation to the source code of the CAD system, which addresses issues of accuracy and will allow use of the efficient reverse mode [16]. This approach still requires to define a suitable CAD parametrisation.…”

Section: Introductionmentioning

confidence: 99%

Adjoint-Based Aerodynamic Optimisation of Wing Shape Using Non-uniform Rational B-Splines

Zhang

Jesudasan

Müller

2018

Computational Methods in Applied Sciences

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Numerical shape optimisation with adjoint CFD is applied using the NURBS based Parametrisation method with Continuity Constraints (NSPCC) for aerodynamically optimising three dimensional surfaces. The ONERA M6 wing is re-parametrised with NURBS surfaces including weight adjustments to represent the three dimensional wing accurately, resulting in fewer control points and smoother variation of curvature. The NSPCC CAD kernel is coupled with the inhouse flow and adjoint solver STAMPS and a gradient-based optimiser to minimise the drag of the ONERA M6 wing in transonic Euler flow conditions. Optimisation results are presented for the B-Spline and NURBS parametrisations.

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Optimisation of a U-Bend Using a Cad-Based Adjoint Method With Differentiated Cad Kernel

Cited by 9 publications

References 10 publications

CAD-based shape optimisation of the NASA CRM wing-body intersection using differentiated CAD-kernel

CAD-based shape optimisation of the NASA CRM wing-body intersection using differentiated CAD-kernel

NURBS-based and Parametric-based Shape Optimisation with Differentiated CAD Kernel

Adjoint-Based Aerodynamic Optimisation of Wing Shape Using Non-uniform Rational B-Splines

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