Using Parametric Effectiveness for Efficient CAD-Based Adjoint Optimization

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

doi:10.14733/cadconfp.2018.372-376

Cited by 2 publications

(7 citation statements)

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“…First, a contribution that employs a CAD-based parameterization is chosen and in particular the approach of Agarwal et al 41 which includes design velocities and a study of parametric effectiveness. The same boundary conditions are employed as in this work and this results in the same Reynolds number.…”

Section: Optimization Of the Mid-section Of A 3d Ductmentioning

confidence: 99%

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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Section: Optimization Of the Mid-section Of A 3d Ductmentioning

confidence: 99%

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

Damigos

2021

Numerical Methods in Fluids

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“…Two dimensional single-and multi-element airfoil configurations were optimized using a gradient-based Newton-Krylov algorithm and a CAD system, by parametrizing the airfoils with B-spline curves [73] and by using finite differences to compute the gradients. A CAD-based shape parameterization using CATIA V5 was used to perform a gradient-based optimization of an automotive car mirror for noise reduction [74]. In this case, the gradients were computed via the design velocity approach.…”

Section: Cad-basedmentioning

confidence: 99%

“…The so-called design velocities are geometric sensitivities that represent the displacement normal to the geometry boundary of a point on the boundary due to a perturbation of a design parameter in the CAD model. Typically, a positive design velocity represents an outward movement of the boundary, whilst a negative is an inward movement [74]. The approach to compute the design velocities is based on a finite difference between the CAD's model shape before and after a perturbation of a CAD design parameter.…”

Section: Design Velocity Approachmentioning

confidence: 99%

“…In an industrial design workflow, very often the CAD models use a very large number of design variables to construct the geometry. For example, in [107] the parametric CAD model of a car mirror used 2925 feature parameters and the CPU time required to update the CAD geometry was nearly 3 hours, making the optimization of such geometry to be computationally expensive. Another downside of exploring rich design spaces with large number of CAD design variables is the convergence of the optimizer.…”

Section: Adaptive Shape Parameterizationmentioning

confidence: 99%

“…Using the constraint on boundary movement, authors in [106] were able to penalise the parameters which moved a larger part of boundary in the low sensitivity regions. Later in [107], authors showed that using this measure of P E it was possible to localize the boundary movement in the areas of high sensitivities.…”

Section: Parametric Effectivenessmentioning

confidence: 99%

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Efficient CAD based adjoint optimization of turbomachinery using adaptive shape parameterization

Sanchez Torreguitart

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The present thesis incorporates the CAD model into an adjoint-based optimization loop and uses it for the shape optimization of a 2D transonic turbine blade mid-section (profile). This is demonstrated by performing a single and multipoint optimization of the LS89 turbine, originally designed at the VKI. Substantial aerodynamic improvements are reported for both design point and off-design conditions.The case is deeply analysed from the flow analysis point of view. The present thesis is a step forward in three main aspects. First, the way the CAD model (for turbomachinery applications) is used within the shape optimization loop.To include the CAD model into the optimization loop, the CAD kernel and the grid generator (multiblock structured) are differentiated using the Algorithmic Differentiation (AD) tool ADOL-C. The advantage of including the CAD model in the design system is that assembly or manufacturing constraints can be imposed on the shape, allowing the optimized model or component to be manufactured. Second, a new definition of the parametric effectiveness indicator is proposed, based on the ability of a set of CAD-based design variables to produce a shape change using the adjoint sensitivities. An interesting thing is that parametric effectiveness considers the design variables can be non-orthogonal to each other and it can be applied to any type of constrained or unconstrained problems. If, in the beginning of the optimization, the parametric effectiveness is high, it is expected to reach a final solution with increased performance. Third, a new adaptive shape parameterization strategy is adopted, which is assisted by the above parametric effectiveness indicator in order to explore the design space more efficiently. The parametric effectiveness, which rates the quality of a CAD based parameterization for optimization, is used in a novel multilevel shape refinement procedure to: (1) introduce the minimum amount of design variables required to modify the shape in the direction the adjoint sensitivities dictate; (2) to create the best parameterization to be used during the optimization. By using the proposed methods and tools, not only the optimal geometry is defined by the CAD, which is the industry adopted standard for the design of components, but also, the designer avoids the use of either too few (slow improvements from cycle to cycle) or too many (increase the computational burden) design variables. The proposed methodology results to be an effective strategy to explore rich design spaces, to improve convergence rate, robustness and final solution of the adjoint-based optimization. Aquesta tesi incorpora el model de CAD en un procés iteratiu d'optimització basat en el mètode adjunt i l'utilitza per a l'optimització de la secció d'una turbina transónica 2D (perfil). Això es demostra realitzant una optimització de punt únic i multipunt de la turbina LS89, originalment dissenyada en el VKI. Es reporten millores aerodinàmiques substancials tant per al punt de disseny com per les condicions fora del disseny. El cas s'analitza en profunditat des del punt de vista aerodinàmic. Aquesta tesi representa un avanç en tres aspectes principals. Primer, la forma en què es fa servir el model CAD (per a aplicacions de turbomàquines) dins el procés d'optimització. Per incloure el model CAD en el bucle d'optimització, s'apliquen tècniques de diferenciació algorítmica (l'eina ADOL-C) al kernel del CAD i el generador de la malla (estructurada i multibloc). L'avantatge d'incloure el model CAD en el sistema de disseny és que es poden imposar restriccions de fabricació a la geometria, i això permet que el disseny ja optimitzat es pugui fabricar. En segon lloc, es proposa una nova definició de l'indicador d'efectivitat paramètrica, basat en la capacitat de produir el canvi en la geometria que dicta el mètode adjunt mitjançant l'ús de les variables de disseny que defineixen el model CAD. Cal destacar que l'efectivitat paramètrica considera que les variables de disseny poden ser no ortogonals entre si i es pot aplicar a qualsevol tipus de problemes restringits o no restringits. Si, al començament de l'optimització, l'efectivitat paramètrica és alta, s'espera que l'optimització arribi a una solució final amb major rendiment. En tercer lloc, s'adopta una nova estratègia de parametrització adaptativa, que és assistida per l'indicador d'efectivitat paramètrica anterior per explorar l'espai de disseny de manera més eficient. L'efectivitat paramètrica, que classifica la qualitat d'una parametrització basada en CAD per a l'optimització, s'utilitza en un nou procediment de refinament multinivell per: (1) introduir la quantitat mínima de variables de disseny requerides per modificar la geometria en la direcció que dicten les sensibilitats del mètode adjunt; (2) per crear la millor parametrització que s'utilitzarà durant l'optimització. En utilitzar els mètodes i eines proposats, no només la geometria òptima està definida en el model CAD, que és l'estàndard adoptat per la indústria per al disseny de components, sinó que també el dissenyador evita l'ús de molt poques (millores lentes de cicle a cicle) o massa variables de disseny (augmenten la càrrega computacional). La metodologia proposada resulta ser una estratègia efectiva per explorar espais de disseny enriquits, millora la taxa de convergència, la solidesa i la solució final de l'optimització basada en el mètode adjunt.

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Using Parametric Effectiveness for Efficient CAD-Based Adjoint Optimization

Cited by 2 publications

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

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

Efficient CAD based adjoint optimization of turbomachinery using adaptive shape parameterization

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Using Parametric Effectiveness for Efficient CAD-Based Adjoint Optimization

Cited by 2 publications

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

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

Efficient CAD based adjoint optimization of turbomachinery using adaptive shape parameterization

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

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