DC FieldValueLanguage
dc.contributor.authorKhalifa, Ali-
dc.contributor.authorBreuer, Michael-
dc.contributor.authorGollwitzer, Jasper-
dc.date.accessioned2022-02-24T05:32:37Z-
dc.date.available2022-02-24T05:32:37Z-
dc.date.issued2022-02-21-
dc.identifier.citationInternational Journal of Heat and Fluid Flow 94 (2022) 108897de_DE
dc.description.abstractThe present study proposes a novel modeling approach for predicting the wall-impact breakage of agglomerates in wall-bounded particle-laden turbulent flows based on artificial neutral networks (ANN). The suggested model is especially useful for efficient Euler-Lagrange simulation methods relying on the hard-sphere approach and the equivalent-sphere model for the agglomerate structure, allowing LES predictions of high mass loadings. Based on the impact conditions, i.e., the impact velocity, the impact angle, the number of included primary particles and the diameter of the primary particles, the outcomes of the breakage events are forecasted using two pre-trained artificial neural networks. The first network is concerned with the prediction of the possibility of breakage and the resulting fragment size distribution, whereas the second network predicts the post-breakage velocities of the fragments. The supervised training of the employed networks relies on a database obtained by extensive DEM simulations of agglomerate wall-impacts covering wide ranges of impact conditions, which were partially re­ ported in Khalifa and Breuer (2020, 2021) for developing a breakage model based on a dimensional analysis and regression techniques. In the present contribution, the database mainly comprising the normal or oblique impact case is extended by adding results for the practically relevant shear impact case of wall-bounded particle-laden flows at extremely small impact angles, i.e., 3◦ and practically flat (0.2◦ ). In addition, the breakage behavior of agglomerates containing very small numbers of particles are investigated under different impact angles and primary particle sizes. The ANN model is employed in Euler-Lagrange LES predictions of duct flows taking three Reynolds numbers and agglomerates of two powders distinguished by the size of the primary particles into account. The results obtained are compared with those based on a previous regression model (Khalifa and Breuer, 2021). In general, a good agreement between the results is found. However, the new ANN model is more widely applicable since the shear impact case is taken into account, which leads to subtle differences enhancing the reliability of the predictions.de_DE
dc.description.sponsorshipStrömungsmechanikde_DE
dc.description.sponsorshipStrömungsmechanikde_DE
dc.language.isoende_DE
dc.publisherElsevierde_DE
dc.relation.ispartofInternational Journal of Heat and Fluid Flowde_DE
dc.subjectArtificial neural networksde_DE
dc.subjectParticle-laden flowsde_DE
dc.subjectWall impactde_DE
dc.subjectBreakage of agglomeratesde_DE
dc.subjectHard-sphere modelde_DE
dc.subjectDEMde_DE
dc.subject.ddcDDC::000 Informatik, Informationswissenschaft, allgemeine Werkede_DE
dc.subject.ddcDDC::500 Naturwissenschaften und Mathematikde_DE
dc.subject.ddcDDC::600 Technik, Medizin, angewandte Wissenschaftende_DE
dc.titleNeural-network based approach for modeling wall-impact breakage of agglomerates in particle-laden flows applied in Euler–Lagrange LESde_DE
dc.typeArticlede_DE
dc.identifier.doi10.1016/j.ijheatfluidflow.2021.108897-
dcterms.bibliographicCitation.volume94de_DE
dcterms.bibliographicCitation.articlenumber94 (2022) 108897de_DE
dc.relation.pages22 S.de_DE
local.submission.typeonly-metadatade_DE
dc.description.peerReviewedYesde_DE
dc.type.articleScientific Articlede_DE
item.grantfulltextnone-
item.fulltext_sNo Fulltext-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairetypeArticle-
crisitem.author.deptStrömungsmechanik-
crisitem.author.deptStrömungsmechanik-
crisitem.author.orcid0000-0002-4822-3436-
crisitem.author.orcid0000-0003-4467-478X-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
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