Improved accuracy and convergence of homotopy-based solutions for aggregation–fragmentation models
Prakrati Kushwah
Department of Mathematics, National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, 620015 India
Search for more papers by this authorCorresponding Author
Jitraj Saha
Department of Mathematics, National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, 620015 India
Correspondence
Jitraj Saha, Department of Mathematics, National Institute of Technology Tiruchirappalli, Tamil Nadu, 620015, India.
Email: [email protected]
Communicated by: M. Brokate
Search for more papers by this authorPrakrati Kushwah
Department of Mathematics, National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, 620015 India
Search for more papers by this authorCorresponding Author
Jitraj Saha
Department of Mathematics, National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, 620015 India
Correspondence
Jitraj Saha, Department of Mathematics, National Institute of Technology Tiruchirappalli, Tamil Nadu, 620015, India.
Email: [email protected]
Communicated by: M. Brokate
Search for more papers by this authorFunding information: PK thanks Ministry of Education (MoE), Government of India, for their funding support during her PhD program. JS thanks NITT for their support through seed grant (file no.: NITT / R & C / SEED GRANT / 19 - 20 / P - 13 / MATHS / JS / E1) during this work.
Abstract
We discuss the formulation of a numerical scheme based on the homotopy method to solve different aggregation–fragmentation models including the simultaneous event. Several test cases are considered and analyzed qualitatively and quantitatively to ascertain the improved accuracy and efficiency of the proposed model over the existing semi-analytical models. The generalized solution of the truncated problem is obtained for some test cases, which in the limiting sense tends to the exact solution. A detailed convergence analysis of the scheme is also studied.
CONFLICT OF INTEREST
This work does not have any conflicts of interest.
REFERENCES
- 1Dubovski PB. Structural stability of disperse systems and finite nature of a coagulation front. J Exp Theor Phys. 1999; 89: 384-390.
- 2Kobayashi H, Tanaka H. Rapid formation of gas-giant planets via collisional coagulation from dust grains to planetary cores. Astrophys J. 2021; 922: 16.
- 3Xue Y, Wang L-P, Grabowski Wojciech W. Growth of cloud droplets by turbulent collision–coalescence. J Atmos Sci. 2008; 65: 331-356.
- 4Li XY, Brandenburg A, Svensson G, Haugen NE, Mehlig B, Rogachevskii I. Effect of turbulence on collisional growth of cloud droplets. J Atmos Sci. 2018; 75: 3469-3487.
- 5Castro JM, Burgisser A, Schipper CI, Mancini S. Mechanisms of bubble coalescence in silicic magmas. Bull Volcanol. 2012; 74: 2339-2352.
- 6Mancini S, Forestier-Coste L, Burgisser A, James F, Castro J. An expansion–coalescence model to track gas bubble populations in magmas. J Volcanol Geotherm Res. 2016; 313: 44-58.
- 7Robson DT, Annibale A, Baas ACW. Reproducing size distributions of swarms of barchan dunes on Mars and Earth using a mean-field model. Phys A: Stat Mech Appl. 2022; 2022: 128042.
- 8Génois M, Hersen P, Bertin E, Du Pont SC, Grégoire G. Out-of-equilibrium stationary states, percolation, and subcritical instabilities in a fully nonconservative system. Physical Review E. 2016; 94(04): 2101.
- 9Ahamed F, Singh M, Song HS, Doshi P, Ooi CW, Ho Y. On the use of sectional techniques for the solution of depolymerization population balances. Results Discret-Contin Mesh Adv Powder Technol. 2020; 31: 2669-2679.
- 10Shirazian S, Ismail HY, Singh M, Shaikh R, Croker DM, Walker GM. Multi-dimensional population balance modelling of pharmaceutical formulations for continuous twin-screw wet granulation: determination of liquid distribution. Int J Pharm. 2019; 566: 352-360.
- 11Bart H-J, Drumm C, Attarakih MM. Process intensification with reactive extraction columns. Chem Eng Process: Process Intensification. 2008; 47: 745-754.
- 12Erabit N, Ndoye FT, Alvarez G, Flick D. A population balance model integrating some specificities of the -lactoglobulin thermally-induced aggregation. J Food Eng. 2015; 144: 66-76.
- 13Metzger L, Kind M. The influence of mixing on fast precipitation processes—a coupled 3D CFD-PBE approach using the direct quadrature method of moments (DQMOM). Chem Eng Sci. 2017; 169: 284-298.
- 14Ismail HY, Singh M, Albadarin AB, Walker GM. Complete two dimensional population balance modelling of wet granulation in twin screw. Int J Pharm. 2020; 591: 120018.
- 15Dürr R, Bück A. Influence of moisture control on activity in continuous fluidized bed drying of baker's yeast pellets. Drying Technol. 2020; 2020: 1-6.
- 16Saha J, Kumar J. The singular coagulation equation with multiple fragmentation. Zeitschrift fü,r angewandte Mathematik und Physik. 2015; 66: 919-941.
10.1007/s00033-014-0452-3 Google Scholar
- 17Elminyawi IM, Gangopadhyay S, Sorensen CM. Numerical solutions to the Smoluchowski aggregation–fragmentation equation. J Colloid Interface Sci. 1991; 144: 315-323.
- 18McCoy BJ, Madras G. Evolution to similarity solutions for fragmentation and aggregation. J Colloid Interface Sci. 1998; 201: 200-209.
- 19Wang L, Marchisio DL, Vigil RD, Fox RO. CFD simulation of aggregation and breakage processes in laminar Taylor–Couette flow. J Colloid Interface Sci. 2005; 282: 380-396.
- 20Matveev SA, Stadnichuk VI, Tyrtyshnikov EE, Smirnov AP, Ampilogova NV, Brilliantov NV. Anderson acceleration method of finding steady-state particle size distribution for a wide class of aggregation–fragmentation models. Comput Phys Commun. 2018; 224: 154-163.
- 21Frungieri G, Vanni M. Aggregation and breakup of colloidal particle aggregates in shear flow: a combined Monte Carlo-Stokesian dynamics approach. Powder Technol. 2021; 388: 357-370.
- 22Shen X, Lin M, Zhu Y, et al. A quasi-Monte Carlo based flocculation model for fine-grained cohesive sediments in aquatic environments. Water Res. 2021; 194: 116953.
- 23Wu S, Yang S, Tay KL, Yang W, Jia M. A hybrid sectional moment projection method for discrete population balance dynamics involving inception, growth, coagulation and fragmentation. Chem Eng Sci. 2022; 249: 117333.
- 24Hulburt HM, Katz S. Some problems in particle technology: a statistical mechanical formulation. Chem Eng Sci. 1964; 19: 555-574.
- 25Ziff RM, McGrady ED. The kinetics of cluster fragmentation and depolymerisation. J Phys A Math Gen. 1985; 18: 3027.
- 26Ziff RM. New solutions to the fragmentation equation. J Phys A Math Gen. 2821; 1991: 24.
- 27Kumar J, Peglow M, Warnecke G, Heinrich S, Mörl L. Improved accuracy and convergence of discretized population balance for aggregation The cell average technique. Chem Eng Sci. 2006; 61: 3327-3342.
- 28Kumar R, Kumar J. Numerical simulation and convergence analysis of a finite volume scheme for solving general breakage population balance equations. Appl Math Comput. 2013; 219: 5140-5151.
- 29Kumar R, Kumar J, Warnecke G. Moment preserving finite volume schemes for solving population balance equations incorporating aggregation, breakage, growth and source terms. Math Models Methods Appl Sci. 2013; 23: 1235-1273.
- 30Marchisio DL, Fox RO. Solution of population balance equations using the direct quadrature method of moments. J Aerosol Sci. 2005; 36: 43-73.
- 31Lage PLC. On the representation of QMOM as a weighted-residual method—the dual-quadrature method of generalized moments. Compu Chem Eng. 2011; 35: 2186-2203.
- 32Yuan C, Laurent F, Fox RO. An extended quadrature method of moments for population balance equations. J Aerosol Sci. 2012; 51: 1-23.
- 33Robson DT, Baas ACW, Annibale A. A combined model of aggregation, fragmentation, and exchange processes: insights from analytical calculations. J Stat Mech: Theory Exper. 2021; 2021: 53203.
- 34Gunawan R, Fusman I, Braatz RD. High resolution algorithms for multidimensional population balance equations. AIChE J. 2004; 50: 2738-2749.
- 35Singh R, Saha J, Kumar J. Adomian decomposition method for solving fragmentation and aggregation population balance equations. J Appl Math Comput. 2015; 48: 265-292.
- 36Kaur G, Singh R, Singh M, Kumar J, Matsoukas T. Analytical approach for solving population balances: a homotopy perturbation method. J Phys A Math Theor. 2019; 52: 385201.
- 37He J-H. Homotopy perturbation method: a new nonlinear analytical technique. Appl Math Comput. 2003; 135: 73-79.
- 38Liao S. Beyond Perturbation Introduction to Homotopy Analysis Method Modern mechanics and mathematics: CRC Press; 2004.
- 39Liao S. Comparison between the homotopy analysis method and homotopy perturbation method. Appl Math Comput. 2005; 169: 1186-1194.
- 40Kumar J, Kaur G, Tsotsas E. An accurate and efficient discrete formulation of aggregation population balance equation. Kinet Related Models. 2016; 9: 373.
- 41McCoy BJ, Madras G. Analytical solution for a population balance equation with aggregation and fragmentation. Chem Eng Sci. 2003; 58: 3049-3051.