Volltext-Downloads (blau) und Frontdoor-Views (grau)

Optimization via Multimodel Simulation

  • Increasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation. Several simulation tools can be run independently and in parallel, e.g., long running computational fluid dynamics simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. However, there are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes. At the same time, data-driven models require input and output data, but analytical models do not. Combining results from models with different input-output structures might improve and accelerate the optimization process. The optimization via multimodel simulation (OMMS) approach, which is able to combine results from these different models, is introduced in this paper. Using cyclonic dust separators as a real-world simulation problem, the feasibility of this approach is demonstrated and a proof-of-concept is presented. Cyclones are popular devices used to filter dust from the emitted flue gases. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities. Pros and cons of this multimodel optimization approach are discussed and experiences from experiments are presented.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Author:Thomas Bartz-BeielsteinGND, Martin Zaefferer, Quoc Cuong Pham
URN:urn:nbn:de:hbz:832-cos4-6209
Series (Serial Number):CIplus (1/2018)
Document Type:Preprint
Language:English
Year of Completion:2017
Release Date:2018/03/22
Tag:3D Printing; Combined simulation; Computational fluid dynamics; Metamodel; Stacking
Pagenumber:16
Descirption of the primary publication:Structural and Multidisciplinary Optimization, online veröffentlicht: 12. Februar 2018; https://doi.org/10.1007/s00158-018-1934-2
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10)
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung