2016
DOI: 10.1016/j.cnsns.2015.12.010
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Exact solutions of population balance equation

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Cited by 10 publications
(17 citation statements)
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“…Whilst the theoretical solution of the MPB equation can only be obtained for some ideal (simple) cases, numerical solution methods can provide the most convenient and available approach. Lin et al (Lin et al, 2016) developed an invariant method of moments to obtain analytical solution of a PB system, but this method could only be used for solving a one-dimensional homogeneous PB equation with size independent growth rate. Therefore, many other different numerical solution methods have been developed, for example, method of characteristics (Gunawan et al, 2004, Sotowa et al, 2000, moment of classes (David et al, 1995, Puel et al, 2003a, high resolution discretisation schemes (Gunawan et al, 2004, Ma et al, 2002, Wan et al, 2009, method of lines (Gerstlauer et al, 2001), finite-element schemes (Gerstlauer et al, 2006), moving grid techniques (Kumar and Ramkrishna, 1997), hierarchical solution strategies based on multilevel discretisation (Pinto et al, 2007, Sun andImmanuel, 2005), cell-ensemble method (Henson, 2005), Monte Carlo methods (Yu et al, 2015), etc.…”
Section: S3 Mpb Solution Methodsmentioning
confidence: 99%
“…Whilst the theoretical solution of the MPB equation can only be obtained for some ideal (simple) cases, numerical solution methods can provide the most convenient and available approach. Lin et al (Lin et al, 2016) developed an invariant method of moments to obtain analytical solution of a PB system, but this method could only be used for solving a one-dimensional homogeneous PB equation with size independent growth rate. Therefore, many other different numerical solution methods have been developed, for example, method of characteristics (Gunawan et al, 2004, Sotowa et al, 2000, moment of classes (David et al, 1995, Puel et al, 2003a, high resolution discretisation schemes (Gunawan et al, 2004, Ma et al, 2002, Wan et al, 2009, method of lines (Gerstlauer et al, 2001), finite-element schemes (Gerstlauer et al, 2006), moving grid techniques (Kumar and Ramkrishna, 1997), hierarchical solution strategies based on multilevel discretisation (Pinto et al, 2007, Sun andImmanuel, 2005), cell-ensemble method (Henson, 2005), Monte Carlo methods (Yu et al, 2015), etc.…”
Section: S3 Mpb Solution Methodsmentioning
confidence: 99%
“…It is difficult to find the infinitesimal generators of Equation () by directly applying the developed Lie group analysis method 12,13 to the original equation (). However, the admitted scaling group of Equation () is investigated by use of the scaling group analysis 8,12‐14 in this section. Let us consider the following scaling group truet¯=taλ1,0.1em0.1em0.1emtruex¯=xaλ2,0.1em0.1em0.1emtruef¯=faμ, where a is an arbitrary real group parameter, and equation truef¯false(truex¯,truet¯false)truet¯+gtruex¯ntruef¯false(truex¯,truet¯false)truex¯=2ktruex¯truey¯n2truef¯false(truey¯,truet¯false)0.1emdtruey¯false(k+gnfalse)truex¯n1truef¯false(truex¯,truet¯false). …”
Section: Admitted Scaling Group Of Equation ()mentioning
confidence: 99%
“…According to the algorithm 8,12‐14 and scaling group () the admitted infinitesimal generator of Equation () can be given by X=λ1tt+λ2xx+μff. …”
Section: Admitted Scaling Group Of Equation ()mentioning
confidence: 99%
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