Suscripción a Biblioteca: Guest
International Journal for Multiscale Computational Engineering

Publicado 6 números por año

ISSN Imprimir: 1543-1649

ISSN En Línea: 1940-4352

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.4 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 2.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00034 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

Computational Study of Hydrodynamics of a Standard Stirred Tank Reactor and a Large-Scale Multi-Impeller Fermenter

Volumen 7, Edición 6, 2009, pp. 559-576
DOI: 10.1615/IntJMultCompEng.v7.i6.60
Get accessGet access

SINOPSIS

We present single-phase simulations of the fully turbulent flow in a standard stirred tank reactor and a large-scale multi-impeller fermenter, both stirred by Rushton turbines. The mean flow characteristics and the turbulence predictions were obtained by solving the Reynolds-averaged Navier-Stokes (RANS) equations using the commercial computational fluid dynamics (CFD) code, Fluent 6.3. The standard, realisable and RNG k- models, and the Reynolds stress model (RSM) were employed for the modeling of turbulence. A moving reference frame (MRF) model was used for the modeling of the moving parts. Results showed that using the standard k - model, good predictions of the impeller power number can be calculated from the integrated turbulence kinetic energy dissipation rate as long as the grid resolution is sufficient. The underprediction in the power number was only 5% unlike the earlier studies, where values up to 50% were reported. The impeller flow number calculated was also in good agreement with the experimental values reported in the literature. The predictions of the turbulence kinetic energy and the turbulence energy dissipation profiles at the impeller discharge stream revealed that, despite its simple form, the standard k - model gave the best predictions, except in the close vicinity of the blade tip, where the RSM model matched better with the experimental data.

REFERENCIAS
  1. Brucato, A., Ciofalo, M., Grisafi, F., and Micale, G., Numerical Prediction of Flow Fields in Baffled Stirred Vessels: A Comparison of Alternative Modelling Approaches. DOI: 10.1016/S0009-2509(98)00149-3

  2. Deen, N., An Experimental and Computational Study of Fluid Dynamics in Gas-Liquid Chemical Reactors.

  3. Ranade, V. V., An efficient Computational Model for Simulating Flow in Stirred Vessels: A Case of Rushton Turbine. DOI: 10.1016/S0009-2509(97)00292-3

  4. Ranade, V. V., Computational Flow Modeling for Chemical Reactor Engineering.

  5. Derksen, J., and Van den Akker, H. E. A., Large Eddy Simulations on the Flow Driven by a Rushton Turbine. DOI: 10.1002/aic.690450202

  6. Luo, J., Issa, R., and Gosman, A., Prediction of Impeller-Induced Flows in Mixing Vessels Using Multiple Frames of Reference.

  7. Lane, G., Computational Modelling of Gas-Liquid Flow in Stirred Tanks.

  8. Murthy, B. N., and Joshi, J. B., Assessment of Standard κ – ∈, RSM and LES Turbulence Models in a Baffled Stirred Vessel Agitated by Various Impeller Designs.

  9. Yeoh, S. L., Papadakis, G., and Yianneskis, M., Numerical Simulation of Turbulent Flow Characteristics in a Stirred Vessel Using the LES and RANS Approaches with the Sliding/ Deforming Mesh Methodology. DOI: 10.1205/0263876041596751

  10. Ng, K., and Yianneskis, M., Observations on the Distribution of Energy Dissipation in Stirred Vessels. DOI: 10.1205/026387600527446

  11. Deglon, D. A., and Meyer, C. J., CFD Modelling of Stirred Tanks: Numerical Considerations. DOI: 10.1016/j.mineng.2006.04.001

  12. Hartmann, H., Derksen, J. J., Montavon, C., Pearson, J., Hamill, I. S., and Van den Akker, H. E. A., Assessment of Large Eddy and RANS Stirred Tank Simulations by Means of LDA. DOI: 10.1016/j.ces.2004.01.065

  13. Bartels, C., Breuer, M., Wechsler, K., and Durst, F., Computational Fluid Dynamics Applications on Parallel-Vector Computers: Computations of Stirred Vessel Flows. DOI: 10.1016/S0045-7930(01)00016-0

  14. Zhou, G., and Kresta, S. M., Distribution of Energy between Convective and Turbulent Flow for Three Frequently Used Impellers.

  15. Delafosse, A., Line, A., Morchain, J., and Guiraud, P., LES and URANS Simulations of Hydrodynamics in Mixing Tank: Comparison to PIV Experiments. DOI: 10.1016/j.cherd.2008.07.008

  16. Ranade, V. V., Tayalia, Y., and Krishnan, H., CFD Predictions of Flow near Impeller Blades in Baffled Stirred Vessels: Assessment of Computational Snapshot Approach. DOI: 10.1080/00986440213134

  17. Fluent, Fluent 6.3 User’s Guide.

  18. Speziale, C. G., Sarkar, S., Gatski., T. B., Modelling the Pressure-Strain Correlation of Turbulence: An Invariant Dynamical Systems Approach. DOI: 10.1017/S0022112091000101

  19. Tatterson, G. B., Fluid Mixing and Gas Dispersion in Agitated Tanks.

  20. Bujalski, W., Nienow, A. W., Chatwin, S., and Cooke, M., The Dependency on Scale of Power Numbers of Rushton Disc Turbines. DOI: 10.1016/0009-2509(87)85061-3

  21. Nienow, A. W., Hydrodynamics of Stirred Bioreactors. DOI: 10.1115/1.3098990

  22. Revill, B. K., Pumping Capacity of Disc Turbine Agitators–A Literature Review.

  23. Costes, J., and Couderc, J. P., Study by Laser Doppler Anemometry of the Turbulent Flow Induced by a Rushton Turbine in a Stirred Tank: Influence of the Size of the Units. I. Mean Flow and Turbulence. DOI: 10.1016/0009-2509(88)80018-6

  24. Vasquez, S. A., and Ivanov, V. A., A Phase Coupled Method for Solving Multiphase Problems on Unstructured Meshes.

  25. Vrá bel, P., Van der Lans, R. G. J. M., Luyben, K. C. A. M., Boon, L., and Nienow, A. W., Mixing in Large-Scale Vessels Stirred with Multiple Radial or Radial and Axial Up- Pumping Impellers: Modelling and Measurements. DOI: 10.1016/S0009-2509(00)00175-5

  26. Barigou, M., and Greaves, M., Bubble-Size Distributions in a Mechanically Agitated Gas–Liquid Contactor. DOI: 10.1016/0009-2509(92)80318-7

  27. Noorman, H., Hjertager, B. H., Morud, K., Tragardh, C., Enfors, S.-O., Larsson, G., and Tornkvist, M., Measurement and Computational Fluid Dynamics Simulation of Saccharomyces Cerevisiae Production in a 30 m3 Stirred Tank Reactor.

  28. Khopkar, A. R., and Ranade, V. V., CFD Simulation of Gas-Liquid Stirred Vessel: VC, S33, and L33 Flow Regimes. DOI: 10.1002/aic.10762

  29. Wu, H., and Patterson, G., Laser-Doppler Measurements of Turbulent-Flow Parameters in a Stirred Mixer. DOI: 10.1016/0009-2509(89)85155-3

  30. Van’t Riet, K., and Smith, J. M., The Trailing Vortex System Produced by Rushton Turbine Agitators. DOI: 10.1016/0009-2509(75)87012-6

CITADO POR
  1. Noorman Henk, An industrial perspective on bioreactor scale-down: What we can learn from combined large-scale bioprocess and model fluid studies, Biotechnology Journal, 6, 8, 2011. Crossref

  2. Martinez-Delgadillo Sergio, Mollinedo-Ponce Helvio, Mendoza-Escamilla Victor, Gutiérrez-Torres Claudia, Jiménez-Bernal José, Barrera-Diaz Carlos, Performance evaluation of an electrochemical reactor used to reduce Cr(VI) from aqueous media applying CFD simulations, Journal of Cleaner Production, 34, 2012. Crossref

  3. Russ David C., Thomas Jonathan M. D., Miller Q. Sean, Berson R. Eric, Predicting Power for a Scaled‐up Non‐Newtonian Biomass Slurry, Chemical Engineering & Technology, 38, 1, 2015. Crossref

  4. Devi T. T., Kumar B., Large-eddy simulation of turbulent flow in stirred tank with a curved blade impeller, Journal of Engineering Thermophysics, 24, 2, 2015. Crossref

  5. Noorman Henk, Scale-Up and Scale-Down, in Fundamental Bioengineering, 2015. Crossref

  6. Haringa Cees, Tang Wenjun, Deshmukh Amit T., Xia Jianye, Reuss Matthias, Heijnen Joseph J., Mudde Robert F., Noorman Henk J., Euler-Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines, Engineering in Life Sciences, 16, 7, 2016. Crossref

  7. Haringa Cees, Noorman Henk J., Mudde Robert F., Lagrangian modeling of hydrodynamic–kinetic interactions in (bio)chemical reactors: Practical implementation and setup guidelines, Chemical Engineering Science, 157, 2017. Crossref

  8. Mudde Rob, Noorman Henk, Reuss Matthias, Bioreactor Modeling, in Industrial Biotechnology, 2016. Crossref

  9. Haringa Cees, Deshmukh Amit T., Mudde Robert F., Noorman Henk J., Euler-Lagrange analysis towards representative down-scaling of a 22 m 3 aerobic S. cerevisiae fermentation, Chemical Engineering Science, 170, 2017. Crossref

  10. Noorman Henk J., Heijnen Joseph J., Biochemical engineering’s grand adventure, Chemical Engineering Science, 170, 2017. Crossref

  11. Haringa Cees, Vandewijer Ruben, Mudde Robert F., Inter-compartment interaction in multi-impeller mixing: Part I. Experiments and multiple reference frame CFD, Chemical Engineering Research and Design, 136, 2018. Crossref

  12. Haringa Cees, Mudde Robert F., Noorman Henk J., From industrial fermentor to CFD-guided downscaling: what have we learned?, Biochemical Engineering Journal, 140, 2018. Crossref

  13. Haringa Cees, Vandewijer Ruben, Mudde Robert F., Inter-compartment interaction in multi-impeller mixing. Part II. Experiments, sliding mesh and large Eddy simulations, Chemical Engineering Research and Design, 136, 2018. Crossref

  14. Zhang Pan, Chen Guanghui, Duan Jihai, Wang Weiwen, Mixing characteristics in a vessel equipped with cylindrical stirrer, Results in Physics, 10, 2018. Crossref

  15. Ding Mingzhu, Chen Biqiang, Ji Xiaojun, Zhou Jingwen, Wang Huiyuan, Tian Xiwei, Feng Xudong, Yue Hua, Zhou Yongjin, Wang Hailong, Wu Jianping, Yang Pengpeng, Jiang Yu, Mao Xuming, Xiao Gang, Zhong Cheng, Xiao Wenhai, Li Bingzhi, Qin Lei, Cheng Jingsheng, Yao Mingdong, Wang Ying, Liu Hong, Zhang Lin, Yu Linling, Chen Tao, Dong Xiaoyan, Jia Xiaoqiang, Zhang Songping, Liu Yanfeng, Chen Yong, Chen Kequan, Wu Jinglan, Zhu Chenjie, Zhuang Wei, Xu Sheng, Jiao Pengfei, Zhang Lei, Song Hao, Yang Sheng, Xiong Yan, Li Yongquan, Zhang Youming, Zhuang Yingping, Su Haijia, Fu Weiping, Huang Yingming, Li Chun, Zhao Zongbao K., Sun Yan, Chen Guo-Qiang, Zhao Xueming, Huang He, Zheng Yuguo, Yang Lirong, Su Zhiguo, Ma Guanghui, Ying Hanjie, Chen Jian, Tan Tianwei, Yuan Yingjin, Biochemical engineering in China, Reviews in Chemical Engineering, 35, 8, 2019. Crossref

  16. Sharan V, Rohit K, Ravishankar M, Bhuvaneshwar D, Harish R, CFD simulation of turbulent flow behaviour in a mixing reactor with Rushton impeller, Journal of Physics: Conference Series, 1716, 1, 2020. Crossref

  17. Vivek Vasudevan, Eka Fitriani Nur, Chew Wee, Mixing studies in an unbaffled bioreactor using a computational model corroborated with in-situ Raman and imaging analyses, Chemical Engineering Journal Advances, 9, 2022. Crossref

  18. Macroscale Modelling, in Hydrodynamics of Gas‐Liquid Reactors, 2011. Crossref

  19. Haringa Cees, An analysis of organism lifelines in an industrial bioreactor using Lattice‐Boltzmann CFD, Engineering in Life Sciences, 2022. Crossref

  20. Hajian Christopher Sarkizi Shams, Zieringer Julia, Takors Ralf, Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors, in Digital Twins, 177, 2020. Crossref

  21. Mangipudi Sriramani, Vikram Reddy Dekketi G.C., Ranganathan Panneerselvam, Computational tools in bioprocessing, in Current Developments in Biotechnology and Bioengineering, 2022. Crossref

  22. Thomas John A., DeVincentis Brian, Hanspal Navraj, Kehn Richard O., Predicting gas-liquid mass transfer rates in reactors using a bubble parcel model, Chemical Engineering Science, 264, 2022. Crossref

Portal Digitalde Biblioteca Digital eLibros Revistas Referencias y Libros de Ponencias Colecciones Precios y Políticas de Suscripcione Begell House Contáctenos Language English 中文 Русский Português German French Spain