Abstract
The paper presents an overview of parameter-based geometric modeling as used for shape optimization with respect to fluid-dynamic performance. Parametric modeling is well established in Computer Aided Design, particularly in the phases of detailed design and production. However, production-centric models often require considerable effort, e.g. de-featuring, to prepare them for simulation, above all for Computational Fluid Dynamics. Consequently, to investigate a large number of design variants special engineering models are built, deliberately omitting certain details, especially if they cannot be captured by the simulation within reasonable effort anyway. In the context of aero- and hydrodynamic design dedicated parametric models are utilized that define shapes of high quality with as few parameters as possible. Parametric modeling for shape optimization can be subdivided into fully-parametric and partially-parametric modeling. In fully-parametric modeling the entire shape is defined and realized by means of parameters while in partially-parametric modeling only the changes to an existing shape are described parametrically. Prominent techniques of partially-parametric modeling are free-form deformation, shift transformations and morphing. The most popular techniques are summarized, giving some of their mathematical background while discussing advantages and drawbacks. Examples are drawn from the maritime and aerospace industries, turbomachinery and automotive design.
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Acknowledgements
All examples shown were realized within CAESES®, FRIENDSHIP SYSTEMS’ integration platform for robust variable CAD and optimization. Parts of the work and developments leading to the material presented in this paper were undertaken within the research and development project No-Welle, funded by the Federal Ministry of Economics and Technology (BMWi) on the orders of the German Bundestag and PtJ as the conducting agency (FKZ 03SX362 D).
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Harries, S., Abt, C., Brenner, M. (2019). Upfront CAD—Parametric Modeling Techniques for Shape Optimization. In: Minisci, E., Vasile, M., Periaux, J., Gauger, N., Giannakoglou, K., Quagliarella, D. (eds) Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Computational Methods in Applied Sciences, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-89988-6_12
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DOI: https://doi.org/10.1007/978-3-319-89988-6_12
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