Machine Learning-Based Filtered Drag Model for Cohesive Gas-Particle Flows

Josef Franz Viktor Tausendschön*, Mohammadsadegh Salehi, Stefan Radl

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

Coarse-grid simulations of large-scale gas-solid flows using a filtered two-fluid model (fTFM) require appropriate sub-grid closure models to approximate unresolved physical phenomena. Such a sub-grid closure should be accurate enough to account for the effects of the inhomogeneous particle distribution. Several constitutive models are available in the literature for non-cohesive gas-solid flows, while they are not applicable for cohesive flows. Therefore, we aim to investigate the dependency of the drag force closure on the cohesion level, and integrate it into a drag correction concept based on machine learning (ML).
To do so, the results of fully-resolved CFDDEM simulations of cohesive gas- article flow are filtered with different filter sizes to develop a new drag closure. In detail, we simulated different systems by changing the cohesion level from cohesionless to highly cohesive, and the size of the systems, via coarse-graining. Afterwards, a dataset for the ML algorithm was created, and various markers were analyzed. Subsequently, a neural network-based drag correction model was created, trained, and tested with the identified markers. Finally, we benchmark the accuracy of the developed models for a range of cohesion levels.
Originalspracheenglisch
TitelConference on Modelling Fluid Flow
Seitenumfang9
PublikationsstatusVeröffentlicht - 30 Aug. 2022
Veranstaltung18th International Conference on Fluid Flow Technologies: CMFF 2022 - Budapest, Ungarn
Dauer: 30 Aug. 20222 Sept. 2022

Konferenz

Konferenz18th International Conference on Fluid Flow Technologies
KurztitelCMFF '22
Land/GebietUngarn
OrtBudapest
Zeitraum30/08/222/09/22

ASJC Scopus subject areas

  • Allgemeine chemische Verfahrenstechnik

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