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NODAL COSINE SINE MATERIAL INTERPOLATION IN MULTI OBJECTIVE TOPOLOGY OPTIMIZATION WITH THE GLOBAL CRITERIA METHOD FOR LINEAR ELASTO STATIC, HEAT TRANSFER, POTENTIAL FLOW AND BINARY CROSS ENTROPY SHARPENING

Published online by Cambridge University Press:  27 July 2021

Martin Denk ,
Klemens Rother ,
Mario Zinßer ,
Christoph Petroll  and
Kristin Paetzold
Martin Denk*
Affiliation:
Bundeswehr University Munich, Institute for Technical Product Development;
Klemens Rother
Affiliation:
Munich University of Applied Sciences, Institute for Material and Building Research;
Mario Zinßer
Affiliation:
Centre
Christoph Petroll
Affiliation:
Bundeswehr University Munich, Institute for Materials, Fuels and Lubricants;
Kristin Paetzold
Affiliation:
Bundeswehr University Munich, Institute for Technical Product Development;
*
Denk, Martin, Bundeswehr University Munich, Insitute for Technical Product Development, Germany, martin.denk@unibw.de

Abstract

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Topology optimization is typically used for suitable design suggestions for objectives like mean compliance, mean temperature, or model analysis. Some modern modeling technics in topology optimization require a nodal based material interpolation. Therefore this article is referred to a continuous material interpolation in topology optimization. To cover a smooth and differentiable density field, we address trigonometric shape functions which are infinitely differentiable. Furthermore, we extend a so-known global criteria method with a sharpening function based on binary cross-entropy, so that sharper solutions results. The proposed material interpolation is applied to different applications such as heat transfer, elasto static, and potential flow. Furthermore, these different objectives are together optimized using a multi-objective criterion.

Keywords

Lightweight designTopology OptimisationComputational design methodsConceptual designDesign methods
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2247 - 2256
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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