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Reduction of Shape Variables in B-Spline Based Optimization by Implementation of Analytical Shapes

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EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization (EngOpt 2018)

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Abstract

The aim of this paper is to develop a simplified geometry representation of a generic engineering shape (initially represented by a B-spline surface or B-spline curve) by a simple shape description aiming at application in engineering shape optimization with objective of minimizing the number of shape variables and reducing the shape complexity for potential easier manufacturing.

First step in achieving this objective is the analysis of a simple shape which can be described by a B-spline curve and approximated by a single circular arc. The analysis was conducted on an example of shape optimization for Savonius-like vertical axis wind turbine (VAWT). Initially, the shape was described by a B-spline curve and the shape optimization process which includes CFD simulation was conducted to maximize the VAWT power coefficient. The shape parameterization was subsequently changed in order to reduce the parameter set. The performance of the initial design (B-spline) is compared to the shape obtained by the simplified geometry. To test the possibilities of reducing the parameter set in 3D shape we used a point cloud obtained from a 3D scanning of Savonius-like blade. Simple shapes and the B-spline surface were fitted to the same blade and the results were compared.

Overall, it was shown that the usage of the simplified shape with reduced parameter set can significantly reduce the number of shape variables for 2D shape without significant loss in the performance of the final optimal result. However, complex 3D shape cannot as easily be described by a simple analytical function.

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Correspondence to Ivo Marinić-Kragić .

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Marinić-Kragić, I., Vučina, D. (2019). Reduction of Shape Variables in B-Spline Based Optimization by Implementation of Analytical Shapes. In: Rodrigues, H., et al. EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization. EngOpt 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-97773-7_41

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  • DOI: https://doi.org/10.1007/978-3-319-97773-7_41

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-97773-7

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