scholarly journals Numerical investigation and modelling of the venous injection of sclerosant foam

2019 ◽  
Vol 60 ◽  
pp. C261-C278
Author(s):  
K. C. Wong ◽  
S. W. Armfield ◽  
N. Williamson
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Numerical Investigation ◽  
Size Distribution ◽  
Bubble Size ◽  
Bubble Column ◽  
Population Balance ◽  
Bubble Size Distribution ◽  
Ambient Fluid ◽  
Saphenofemoral Junction

Sclerosant foam, a mixture of a surfactant liquid and air, is injected directly into varicose veins as a treatment that causes the vein to collapse. This investigation develops a model that will allow the medical specialist to visualise how the sclerosant foam will interact with the blood and behave within the vein. The process is simulated using a multiphase computational fluid dynamics model with the sclerosant foam considered as a two-phase non-Newtonian power law viscosity liquid. The governing multiphase equations are solved using an Eulerian⁠–⁠Eulerian approach coupled with a population balance model to predict the bubble size distribution within the flow field. The computational results demonstrate similar flow characteristics and flow features to an available set of experimental results. The model predicts the mixing layers between the sclerosant foam and the ambient fluid, and the sclerosant liquid and the ambient fluid, as well as the sclerosant liquid coverage on the vein wall and the bubble size distribution within the vein. These quantities are of interest to medical specialists allowing them to assess the treatment feasibility and safety before treating the patients. References S. Ali Mirjalili, J. C. Muirhead, and M. D. Stringer. Redefining the surface anatomy of the saphenofemoral junction in vivo. Clin. Anat., 27(6):915–919, 2014. doi:10.1002/ca.22386. E. Cameron, T. Chen, D. E. Connor, M. Behnia, and K. Parsi. Sclerosant foam structure is strongly influenced by liquid air fraction. Eur. J. Vasc. Endo. Surg., 46:488–494, 2013. doi:10.1016/j.ejvs.2013.07.013. P. Coleridge-Smith. Saphenous ablation: Sclerosant or sclerofoam? Semin. Vasc. Surg., 18:19–24, 2005. doi:10.1053/j.semvascsurg.2004.12.007. J.-J. Guex. Complications and side-effects of foam sclerotherapy. Phlebology, 24:270–274, 2009. doi:10.1258/phleb.2009.009049. Ansys Inc. ANSYS FLUENT 12.0 population balance module manual. ANSYS, 2010. URL https://www.afs.enea.it/project/neptunius/docs/fluent/html/popbal/main_pre.htm. F. Ren, N. A. Noda, T. Ueda, Y. Sano, Y. Takase, T. Umekage, Y. Yonezawa, and H. Tanaka. CFD-PMB coupled simulation of a nanobubble generator with honeycomb structure. volume 372 of IOP Conference Series: Materials Science and Engineering, page 012012, June 2018. doi:10.1088/1757-899X/372/1/012012. P. Souroullas, R. Barnes, G. Smith, S. Nandhra, D. Carradice, and I. Chetter. The classic saphenofemoral junction and its anatomical variations. Phlebology, 32(3):172–178, 2017. doi:10.1177/0268355516635960. A. H. Syed, M. Boulet, T. Melchiori, and J. M. Lavoie. CFD simulations of an air-water bubble column: Effect of Luo coalescence parameter and breakup kernels. Front. Chem., 5(68):1–16, 2017. doi:10.3389/fchem.2017.00068. T. Wang and J. Wang. Numerical simulation of gas-liquid mass transfer in bubble column with a CFD-PBM coupled model. Chem. Eng. Sci., 62:7107–7118, 2007. doi:10.1016/j.ces.2007.08.033. M. R. Watkins. Deactivation of sodium tetradecyl sulphate injection by blood proteins. Euro. J. Vasc. Endo. Surg., 41(4): 521–525, 2011. doi:10.1016/j.ejvs.2010.12.012. K. Wong. Experimental and numerical investigation and modelling of sclerosant foam. PhD thesis, University of Sydney, 2018. K. Wong, T. Chen, D. E. Connor, M. Behnia, and K. Parsi. Basic physiochemical and rheological properties of detergent sclerosants. Phlebology, 30(5):339–349, 2015. doi:10.1177/0268355514529271. K. C. Wong, T. Chen, D. E. Connor, M. Behnia, and K. Parsi. Computational fluid dynamics of liquid and foam sclerosant injection in a vein model. Appl. Mech. Mater., 553:293–298, 2014. doi:10.4028/www.scientific.net/AMM.553.293.

Parametric numerical study and optimization of mass transfer and bubble size distribution in a gas-liquid stirred tank bioreactor equipped with Rushton turbine using computational fluid dynamics

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sanaz Salehi ◽  
Amir Heydarinasab ◽  
Farshid Pajoum Shariati ◽  
Ali Taghvaie Nakhjiri ◽  
Kourosh Abdollahi
Keyword(s):  
Fluid Dynamics ◽  
Mass Transfer ◽  
Computational Fluid Dynamics ◽  
Transfer Coefficient ◽  
Size Distribution ◽  
Bubble Size ◽  
Bubble Size Distribution ◽  
Stirred Tank ◽  
Operating Parameters ◽  
Stirred Tank Bioreactor

Abstract Designing and optimizing a bioreactor can be an especially challenging process. Computational modelling is an effective tool to investigate the effects of various operating parameters on bioreactor performance and identify the optimum ones. In this work, a computational fluid dynamics-population balance model (CFD-PBM) was developed to elucidate the effect of different geometrical and operating parameters on the hydrodynamics and mass transfer coefficient of a batch stirred tank bioreactor. The validated model was projected to predict the effect of different parameters including the gas flow rate, the impeller off-bottom clearance, the number of agitator blades, and rotational speed of the impeller on the velocity profiles, air volume fraction, bubble size distribution, and the local gas mass transfer coefficient (K l a) in the bioreactor. Air bubble breakup and coalescence phenomena were considered in all simulations. Factorial experimental design approach was employed to statistically investigate the impacts of the aforementioned operating and geometrical parameters on K l a and bubble size distribution in the bioreactor in order to determine the most significant parameters. This can give an essential insight into the most impactful factors when it comes to designing and scaling up a bioreactor.

scholarly journals CFD Prediction of Gas-Liquid Flow in an Aerated Stirred Vessel Using the Population Balance Model

2014 ◽  
Vol 35 (1) ◽  
pp. 55-73 ◽  
Author(s):  
Zbyněk Kálal ◽  
Milan Jahoda ◽  
Ivan Fořt
Keyword(s):  
Size Distribution ◽  
Bubble Size ◽  
Water System ◽  
Population Balance ◽  
Bubble Size Distribution ◽  
Balance Model ◽  
Stirred Vessel ◽  
Rushton Turbine ◽  
Size Groups ◽  
Gas Liquid Flow

Abstract The main topic of this study is the experimental measurement and mathematical modelling of global gas hold-up and bubble size distribution in an aerated stirred vessel using the population balance method. The air-water system consisted of a mixing tank of diameter T = 0.29 m, which was equipped with a six-bladed Rushton turbine. Calculations were performed with CFD software CFX 14.5. Turbulent quantities were predicted using the standard k-ε turbulence model. Coalescence and breakup of bubbles were modelled using the homogeneous MUSIG method with 24 bubble size groups. To achieve a better prediction of the turbulent quantities, simulations were performed with much finer meshes than those that have been adopted so far for bubble size distribution modelling. Several different drag coefficient correlations were implemented in the solver, and their influence on the results was studied. Turbulent drag correction to reduce the bubble slip velocity proved to be essential to achieve agreement of the simulated gas distribution with experiments. To model the disintegration of bubbles, the widely adopted breakup model by Luo & Svendsen was used. However, its applicability was questioned.

Bubble size distribution and gas–liquid interfacial area in a modified downflow bubble column

2006 ◽  
Vol 122 (1-2) ◽  
pp. 1-10 ◽  
Author(s):  
Subrata Kumar Majumder ◽  
Gautam Kundu ◽  
Dibyendu Mukherjee
Keyword(s):  
Size Distribution ◽  
Bubble Size ◽  
Bubble Column ◽  
Interfacial Area ◽  
Bubble Size Distribution

Modelling of Bubble Growth in Supersaturated Solution Using Computational Fluid Dynamics - Population Balance Model (CFD-PBM) Approach

2015 ◽  
Vol 362 ◽  
pp. 200-208
Author(s):  
Zhen Hong Ban ◽  
Kok Keong Lau ◽  
Mohd Shariff Azmi
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Bubble Size ◽  
Bubble Growth ◽  
Population Balance ◽  
Balance Model ◽  
Population Balance Model ◽  
Supersaturated Solution ◽  
Supersaturation Ratio ◽  
Growth Phenomenon

The bubble growth modelling in a supersaturated solution is difficult to be accomplished as it requires coupling of many interrelated hydrodynamics and mass transfer parameters which include pressure drop, supersaturation ratio, bubble size, etc. In the current work, all these factors have been taken into consideration to predict bubble growth in a supersaturated solution using Computational Fluid Dynamics (CFD) – Population Balance Model (PBM) approach. A classical bubble growth model has been used in the simulation. The bubble growth rate was successfully validated with experimental data in terms of bubble size. The attempt to simulate the bubble growth phenomenon of more than a single bubble condition has also been presented. The outcome of this approach is expected to be applied in many engineering areas.

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