Three dimensional evolutionary aerodynamic design optimization with CMA-ES

Hasenjäger, Martina; Sendhoff, Bernhard; Sonoda, Toyotaka; Arima, Toshiyuki

doi:10.1145/1068009.1068366

Cited by 20 publications

(12 citation statements)

References 13 publications

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“…In the evaluation case that was de ned, optimization of a 2D airfoil, the number of dimensions and samples is not very large. When optimizing 3D shapes however, the dimensionality can easily go up to 88 or 96 dimensions [4,11].…”

Section: Surrogate Assisted Illuminationmentioning

confidence: 99%

Hierarchical surrogate modeling for illumination algorithms

Hagg

2017

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Evolutionary illumination is a recent technique that allows producing many diverse, optimal solutions in a map of manually de ned features. To support the large amount of objective function evaluations, surrogate model assistance was recently introduced [9]. Illumination models need to represent many more, diverse optimal regions than classical surrogate models. In this PhD thesis, we propose to decompose the sample set, decreasing model complexity, by hierarchically segmenting the training set according to their coordinates in feature space. An ensemble of diverse models can then be trained to serve as a surrogate to illumination. KEYWORDSsurrogate modeling, evolutionary illumination, bagging ACM Reference format: Alexander Hagg. 2017. Hierarchical Surrogate Modeling for Illumination Algorithms. In

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Section: Surrogate Assisted Illuminationmentioning

confidence: 99%

Hierarchical surrogate modeling for illumination algorithms

Hagg

2017

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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“…Most methods have used representations that define the design directly (e.g., spline surfaces [32]), or through representations designed specifically for the task (e.g., 3D aerofoils [56].) Menzel and Sendhoff [49] evolved parameters to a free form deformation (FFD) [69] algorithm to design the 3D stator blade of a jet turbine using CFD simulations to evaluate solutions.…”

Section: Evolving Wind Turbines and Bladesmentioning

confidence: 99%

Towards the evolution of vertical-axis wind turbines using supershapes

Preen

Bull

2014

Evol. Intel.

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We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under fan generated wind conditions after being physically instantiated by a 3D printer. That is, unlike other approaches such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made. However, the representation used significantly restricted the range of morphologies explored. In this paper, we present initial explorations into the use of a simple generative encoding, known as Gielis superformula, that produces a highly flexible 3D shape representation to design VAWT. First, the target-based evolution of 3D artefacts is investigated and subsequently initial design experiments are performed wherein each VAWT candidate is physically instantiated and evaluated under fan generated wind conditions. It is shown possible to produce very closely matching designs of a number of 3D objects through the evolution of supershapes produced by Gielis superformula. Moreover, it is shown possible to use artificial physical evolution to identify novel and increasingly efficient supershape VAWT designs.

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“…This is often the case in many real-world problems such as gas turbine stator blades [22], multidisciplinary design optimization [55], and target shape design optimization [37].…”

Section: Introductionmentioning

confidence: 99%