Publication:
Data-driven ANN approach for binary agglomerate collisions including breakage and agglomeration

cris.virtual.departmentStrömungsmechanik
cris.virtual.departmentStrömungsmechanik
cris.virtual.departmentbrowseStrömungsmechanik
cris.virtual.departmentbrowseStrömungsmechanik
cris.virtual.departmentbrowseStrömungsmechanik
cris.virtual.departmentbrowseStrömungsmechanik
cris.virtualsource.departmentca573de0-5426-465c-8cb1-2c3a64fcdb89
cris.virtualsource.departmentba61e71a-d073-4609-89b6-c10b460b09a8
dc.contributor.authorKhalifa, Ali Ahmad
dc.contributor.authorBreuer, Michael
dc.date.issued2023-06
dc.description.abstractThe present contribution is a follow-up of a recently conducted study to derive a data-driven model for the breakage of agglomerates by wall impacts. This time the collision-induced breakage of agglomerates and concurrently occurring particle agglomeration processes are considered in order to derive a model for Euler--Lagrange methods, in which agglomerates are represented by effective spheres. Although the physical problem is more challenging due to an increased number of influencing parameters, the strategy followed is very similar. In a first step extensive discrete element simulations are carried out to study a variety of binary inter-agglomerate collision scenarios. That includes different collision angles, collision velocities, agglomerate sizes and powders. The resulting extensive database accounts for back-bouncing, agglomeration and breakage events. Subsequently, the collision database is used for training artificial neural networks to predict the post-collision number of arising entities, their size distributions and their velocities. Finally, it is shown how the arising data-driven model can be incorporated into the Euler--Lagrange framework to be used in future studies for efficient computations of flows with high mass loadings.
dc.description.versionNA
dc.identifier.citationChemical Engineering Research and Design 195 (2023) 14–27
dc.identifier.doi10.1016/j.cherd.2023.05.040
dc.identifier.urihttps://openhsu.ub.hsu-hh.de/handle/10.24405/15015
dc.language.isoen
dc.publisherElsevier
dc.relation.journalChemical Engineering Research and Design
dc.relation.orgunitStrömungsmechanik
dc.rights.accessRightsmetadata only access
dc.subjectData-driven modeling
dc.subjectArtificial neural network
dc.subjectParticle-laden flow
dc.subjectCollision-induced breakage of agglomerates
dc.subjectAgglomeration
dc.subjectDEM
dc.titleData-driven ANN approach for binary agglomerate collisions including breakage and agglomeration
dc.typeResearch article
dcterms.bibliographicCitation.originalpublisherplaceAmsterdam
dspace.entity.typePublication
hsu.peerReviewed
hsu.uniBibliography
oaire.citation.endPage27
oaire.citation.startPage14
oaire.citation.volume195(2023)
Files