Aerodynamic Shape Optimization of a Pipe Using the Adjoint Method

Helgason, Eysteinn; Krajnović, Siniša

doi:10.1115/imece2012-89396

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…These techniques are based on the so‐called shape derivative, which measures the sensitivity of a shape due to infinitesimal deformations, and the topological derivative, which measures the sensitivity of a geometry with respect to the insertion of an infinitesimally small hole, see, e.g., [14, 61] for shape calculus and [45] for topological sensitivity analysis. In recent years these techniques have been applied to many industrial problems, e.g., the shape design of polymer spin packs [27, 37–39], electric motors [20, 21], acoustic horns [5, 57], automobiles [18, 46, 48], aircrafts [41, 55, 56] or pipe systems [25, 28, 58]. To the best of our knowledge, the optimization of a microchannel cooling system by means of shape calculus has only been investigated in our earlier work [6], where we rigorously analyzed the theoretical aspects of this problem.…”

Section: Introductionmentioning

confidence: 99%

Model hierarchy for the shape optimization of a microchannel cooling system

2020

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We model a microchannel cooling system and consider the optimization of its shape by means of shape calculus. A three‐dimensional model covering all relevant physical effects and three reduced models are introduced. The latter are derived via a homogenization of the geometry in 3D and a transformation of the three‐dimensional models to two dimensions. A shape optimization problem based on the tracking of heat absorption by the cooler and the uniform distribution of the flow through the microchannels is formulated and adapted to all models. We present the corresponding shape derivatives and adjoint systems, which we derived with a material derivative free adjoint approach. To demonstrate the feasibility of the reduced models, the optimization problems are solved numerically with a gradient descent method. A comparison of the results shows that the reduced models perform similarly to the original one while using significantly less computational resources.

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Section: Introductionmentioning

confidence: 99%

Model hierarchy for the shape optimization of a microchannel cooling system

2020

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“…The success of any shape optimization methodology depends extensively on the type of parameterization technique employed [1]. One straightforward route, which gives the most flexible parametrization strategy, is to use the nodes of the computational mesh [2][3][4][5] as design variables. One major drawback for this parameterization strategy is that, as all surface mesh nodes can move independently, the implementation of a post optimization smoothing algorithm is required to prevent the appearance of non-smooth shapes during the optimization process.…”

Section: Introductionmentioning

confidence: 99%

Enhancing CAD-based shape optimisation by automatically updating the CAD model’s parameterisation

Agarwal

Robinson

Armstrong

et al. 2018

Struct Multidisc Optim

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This paper presents an approach which increases the flexibility of a computer-aided design (CAD) model by automatically refining its parameterization and adding new CAD features to the model´s feature tree. It aims to overcome the limitations imposed by the choice of parameters used during the initial model creation, which constrains how the model shape can change during design optimization. Parametric Effectiveness compares the maximum performance improvement that can be achieved using a parameterization strategy, to the maximum performance improvement that can be obtained where the model is unconstrained in how it moves. As such, it provides a measure of how good a parameterization strategy is and allows different strategies to be compared. The change in parametric effectiveness due to inserting multiple different CAD features can be calculated using a single adjoint analysis, therefore the computational cost is essentially independent of the number of parameterisation strategies being analysed. The described approach can be used to automatically add new features to the model, and subsequently allows the use of the newly-added parameters, along with the existing parameters to be used for optimization, providing the opportunity for a better performing product. The developed approach is applied on CAD models created in CATIA V5 for 2D and 3D finite element and computational fluid dynamics problems.

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“…This process can be complex for some optimisation processes (e.g. mesh based approaches [1]), and to reassociate an externally optimized geometry with a set of CAD features and parameters is virtually impossible, and if required has to be created from scratch.…”

Section: Introductionmentioning

confidence: 99%

A CAD Based Framework for Optimizing Performance While Ensuring Assembly Fit

Agarwal

Robinson

Armstrong

2018

Communications in Computer and Information Science

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The optimization of an individual component usually happens in isolation of the components it will interface with or be surrounded by in an assembly. This means that when the optimized components are assembled together fit issues can occur. This paper presents a CAD-based optimization framework, which uses constraints imposed by the adjacent or surrounding components in the CAD model product assembly, to define the limits of the packaging space for the component being optimized. This is important in industrial workflows, where unwanted interference is costly to resolve. The gradient-based optimization framework presented uses the parameters defining the features in a feature-based CAD model as design variables. The two main benefits of this framework are: (1) the optimized geometry is available as a CAD model and can be easily used in the manufacturing stages, and (2) the resulting manufactured should be able to be assembled with other components during the assembly process. The framework is demonstrated for the optimization of 2D and 3D parametric models created in CATIA V5.

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Aerodynamic Shape Optimization of a Pipe Using the Adjoint Method

Cited by 12 publications

References 14 publications

Model hierarchy for the shape optimization of a microchannel cooling system

Model hierarchy for the shape optimization of a microchannel cooling system

Enhancing CAD-based shape optimisation by automatically updating the CAD model’s parameterisation

A CAD Based Framework for Optimizing Performance While Ensuring Assembly Fit

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