Numerical study of turbulence on drag coefficient determination for particle agglomerates
Numerical simulations of the flow surrounding particle agglomerates were carried out using computational fluid dynamics to assess the ability of five RANS turbulence models to estimate the drag coefficient in particle agglomerates. Simulations were carried out in steady conditions for Reynolds numbe...
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| Format: | Article |
| Language: | English |
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Association of the Chemical Engineers of Serbia
2024-01-01
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| Series: | Chemical Industry and Chemical Engineering Quarterly |
| Subjects: | |
| Online Access: | https://doiserbia.nb.rs/img/doi/1451-9372/2024/1451-93722300021O.pdf |
| _version_ | 1797215521119141888 |
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| author | de Oliveira Ricardo Arbach Fernandes Zanata Julio Henrique Lopes Gabriela Cantarelli |
| author_facet | de Oliveira Ricardo Arbach Fernandes Zanata Julio Henrique Lopes Gabriela Cantarelli |
| author_sort | de Oliveira Ricardo Arbach Fernandes |
| collection | DOAJ |
| description | Numerical simulations of the flow surrounding particle agglomerates were carried out using computational fluid dynamics to assess the ability of five RANS turbulence models to estimate the drag coefficient in particle agglomerates. Simulations were carried out in steady conditions for Reynolds numbers between 1 and 1500. Streamlines showed that symmetrical agglomerates present a velocity profile similar to the single sphere profile. Results showed that both Spalart-Allmaras and SST k-ω turbulence models could represent the flow profile in the regions near and far from the walls of the agglomerates and the wake region in the rear of the agglomerates. The RNG k-ε model showed poor quality in predicting the velocity profile and the drag coefficient. The drag coefficient obtained by simulations presented a trend better represented by the Tran-Cong model, also showing that deviations from the predictions decreased as the packing density of the agglomerate increased. The use of steady RANS simulations showed to be a feasible and efficient method to predict, with low computational cost, the drag coefficient in particle agglomerates. For the transition and turbulent flows, results presented good agreement, with deviations between -15% and 13%, while for lower Reynolds numbers, deviations varied between -25% and 5%. |
| first_indexed | 2024-04-24T11:31:23Z |
| format | Article |
| id | doaj.art-cf72125915d14621aed12898b1ff4cf1 |
| institution | Directory Open Access Journal |
| issn | 1451-9372 2217-7434 |
| language | English |
| last_indexed | 2024-04-24T11:31:23Z |
| publishDate | 2024-01-01 |
| publisher | Association of the Chemical Engineers of Serbia |
| record_format | Article |
| series | Chemical Industry and Chemical Engineering Quarterly |
| spelling | doaj.art-cf72125915d14621aed12898b1ff4cf12024-04-10T10:15:38ZengAssociation of the Chemical Engineers of SerbiaChemical Industry and Chemical Engineering Quarterly1451-93722217-74342024-01-0130216117710.2298/CICEQ221206021O1451-93722300021ONumerical study of turbulence on drag coefficient determination for particle agglomeratesde Oliveira Ricardo Arbach Fernandes0Zanata Julio Henrique1Lopes Gabriela Cantarelli2Department of Chemical Engineering, Federal University of São Carlos, São Carlos, São Paulo, BrazilDepartment of Chemical Engineering, Federal University of São Carlos, São Carlos, São Paulo, BrazilDepartment of Chemical Engineering, Federal University of São Carlos, São Carlos, São Paulo, BrazilNumerical simulations of the flow surrounding particle agglomerates were carried out using computational fluid dynamics to assess the ability of five RANS turbulence models to estimate the drag coefficient in particle agglomerates. Simulations were carried out in steady conditions for Reynolds numbers between 1 and 1500. Streamlines showed that symmetrical agglomerates present a velocity profile similar to the single sphere profile. Results showed that both Spalart-Allmaras and SST k-ω turbulence models could represent the flow profile in the regions near and far from the walls of the agglomerates and the wake region in the rear of the agglomerates. The RNG k-ε model showed poor quality in predicting the velocity profile and the drag coefficient. The drag coefficient obtained by simulations presented a trend better represented by the Tran-Cong model, also showing that deviations from the predictions decreased as the packing density of the agglomerate increased. The use of steady RANS simulations showed to be a feasible and efficient method to predict, with low computational cost, the drag coefficient in particle agglomerates. For the transition and turbulent flows, results presented good agreement, with deviations between -15% and 13%, while for lower Reynolds numbers, deviations varied between -25% and 5%.https://doiserbia.nb.rs/img/doi/1451-9372/2024/1451-93722300021O.pdfparticulate matterparticle agglomeratesturbulencedrag coefficientcomputational fluid dynamics |
| spellingShingle | de Oliveira Ricardo Arbach Fernandes Zanata Julio Henrique Lopes Gabriela Cantarelli Numerical study of turbulence on drag coefficient determination for particle agglomerates Chemical Industry and Chemical Engineering Quarterly particulate matter particle agglomerates turbulence drag coefficient computational fluid dynamics |
| title | Numerical study of turbulence on drag coefficient determination for particle agglomerates |
| title_full | Numerical study of turbulence on drag coefficient determination for particle agglomerates |
| title_fullStr | Numerical study of turbulence on drag coefficient determination for particle agglomerates |
| title_full_unstemmed | Numerical study of turbulence on drag coefficient determination for particle agglomerates |
| title_short | Numerical study of turbulence on drag coefficient determination for particle agglomerates |
| title_sort | numerical study of turbulence on drag coefficient determination for particle agglomerates |
| topic | particulate matter particle agglomerates turbulence drag coefficient computational fluid dynamics |
| url | https://doiserbia.nb.rs/img/doi/1451-9372/2024/1451-93722300021O.pdf |
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