Lange, AndreasAndreasLangeFieg, GeorgGeorgFieg2022-09-142022-09-142022-06Computer Aided Chemical Engineering 49 : 1291-1296 (2022)978-0-323-85159-6http://hdl.handle.net/11420/13606Additive manufacturing (AM) reveals a completely new freedom in design and development of structured packings for thermal separation columns. This potential might lead to the next generation of high-performance packings, but it can only be fully used if novel design methods are developed. One of these innovative design methods is presented in this contribution. A topology optimization approach based on the coupling of a stochastic optimization algorithm and computational fluid dynamics (CFD) simulations is applied to generatively design structured packings. By its application, novel structured packing shapes may be found. Binary elements, which are either defined as packing material or as empty elements, are considered as design variables in a defined design space. A multi-objective genetic algorithm with tailored process- and manufacturing- related constraints is used to identify the best packing material distribution within the column shell, revealing minimized pressure drop and maximized surface area. In this paper, the optimization tool and CFD model are presented before selected results of an exemplary topology optimization study are given. The objective of this study is the development of a packing element for a lab scale distillation column. The promising results prove the viability of the design method, showing that it is possible to generatively design structured packings algorithm-based and without any well-defined initial packing geometries as starting point.enAdditive ManufacturingGenerative DesignStructured PackingsTopology OptimizationNatural Sciences and Mathematics::500: ScienceConference Paper10.1016/B978-0-323-85159-6.50215-3Conference Paper