Journal Pre-proof Volume averaging theory (VAT) based modelling for longitudinal mass dispersion in structured porous medium with porous particles Chen Yang Rong Huang Yixiong Lin Ting Qiu PII: S0263-8762(19)30518-0 DOI: https://doi.org/doi:10.1016/j.cherd.2019.10.048 Reference: CHERD 3883 To appear in: Chemical Engineering Research and Design Received Date: 1 September 2018 Revised Date: 17 September 2019 Accepted Date: 31 October 2019 Please cite this article as: Yang, C., Huang, R., Lin, Y., Qiu, T.,Volume averaging theory (VAT) based modelling for longitudinal mass dispersion in structured porous medium with porous particles, Chemical Engineering Research and Design (2019), doi: https://doi.org/10.1016/j.cherd.2019.10.048 This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier. Research Highlights Highlights Mass transfer in structured porous media with porous particles was upscaled. A macroscopic solute transport equation was proposed by using VAT. A new correlation of longitudinal mass dispersion was presented. A comparison between random and structured porous media correlations was Jo ur na lP re -p ro of given. Page 1 of 37 *Manuscript Click here to view linked References 1 Volume averaging theory (VAT) based modelling for longitudinal 2 mass dispersion in structured porous medium with porous particles 3 Chen Yang, Rong Huang, Yixiong Lin, Ting Qiu* 4 Correspondence author: [email protected] (T. Qiu) 5 Fujian Universities Engineering Research Center of Reactive Distillation Technology, 6 College of Chemical Engineering, Fuzhou University, Fuzhou 350116, Fujian, China Abstract ro of 7 In this study, to investigate the mass transport characteristics in a structured 9 porous medium with porous particles under an interfacial concentration discontinuity, 10 a macroscopic solute transport equation is proposed based on the volume averaging 11 theory. A typical three-dimensional geometry (a body center cubic arrangement of 12 spheres) was chosen as the representative elementary volume. The corresponding 13 closure problem was solved to obtain the longitudinal mass dispersion. Based on the 14 numerical results, a new correlation of the longitudinal mass dispersion for a 15 structured porous medium with porous particles is presented. Through a comparison 16 with the correlation of the longitudinal mass dispersion for a random porous medium, 17 it can be determined that, for a mechanical dispersion, the dependence of a random 18 porous medium and that of a structured porous medium on the Péclet number Pe are 19 linear and quadratic, respectively. Furthermore, it was also determined that for a 20 holdup dispersion, a structured porous medium is less dependent on the ratio of 21 diffusivity of the fluid phase to the diffusivity of the porous particle phase. In addition, 22 it is more dependent on the inverse ratio of the solubilities of the solute in the fluid Jo ur na lP re -p 8 1 / 36 Page 2 of 37 1 and in the catalyst particles than in a random porous medium. 2 Keywords: Volume averaging theory (VAT); Longitudinal mass dispersion; 3 Structured porous medium; Porous particles; Péclet number 4 1. Introduction Owing to its convenience and efficiency, a porous medium model is important 6 for understanding the mass transport characteristics in many technological and 7 environmental processes (Bear, 1988; Huang et al., 2010; Sano and Nakayama, 2012; 8 Yang et al., 2015). To implement a porous medium model for practical applications, 9 an effective diffusion coefficient that is affected by the geometrical and hydraulic 10 factors should be obtained in advance. With respect to the hydraulic flow of a liquid in 11 a porous medium, the dispersion resulting from the hydrodynamic mixing of the fluid 12 particles passing through pores plays an important role in dealing with the transport 13 processes. -p re lP Based on an asymptotic analysis in a bed of fixed spheres, Koch and Brady Jo ur na 14 ro of 5 15 (1985) proposed several physical mechanisms causing dispersion, namely, mechanical 16 dispersion, boundary-layer dispersion, and holdup dispersion. The latter two 17 mechanisms are also a type of non-mechanical dispersion. Chai et al. (2016) 18 summarized 19 experimental, numerical, and analytical (or semi-analytical). three categories related to dispersion measurement methods: 20 By introducing a step change in the concentration of a dilute solute, Fried and 21 Combarnous (1971) obtained numerous experiment results on the mass dispersion in 22 packed beds of nonporous particles. This was achieved by fitting the exponential 2 / 36 Page 3 of 37 profiles based on Fick’s law at the macroscopic scale with the resultant concentration 2 profile downstream from the inlet. For a fixed bed of random particles, Kandhai et al. 3 (2002) used pulsed field gradient nuclear magnetic resonance (PFG-NMR) to obtain 4 the longitudinal mass dispersion of both porous and nonporous particles. Moreover, 5 their experiment results also indicated the importance of a holdup dispersion. For 6 another experimental method associated with the mass dispersion of gases and liquids 7 in a packed bed, see the excellent study by Delgado (2006). Nevertheless, the problem 8 related to the experimental method is that the measured results are insufficient to 9 provide a detailed interpretation of the dispersion mechanisms. -p ro of 1 Using a numerical method, Blunt et al. (2013) computed the mass dispersion in 11 different rock samples, and obtained the physical geometries through pore-scale 12 imaging. Yang et al. (2018) recently utilized a D2Q9 model with a 13 multi-relaxation-time (MRT) collision operator, which is a type of lattice Boltzmann 14 method used to calculate the longitudinal mass dispersion of a porous medium with 15 randomly positioned particles. A random placement (RP) method was used to 16 reconstruct the physical geometry of the porous medium with random and 17 nonoverlapping particles. Moreover, a general correlation of the longitudinal mass 18 dispersion validated through the experiment data under a wide-ranging porosity was 19 proposed. Although the numerical method has been considered the most robust, it is 20 normally time-consuming owing to the difficulty in obtaining the microscopic 21 geometry through laboratory measurements, as pointed out by Raeini et al. (2014). 22 Jo ur na lP re 10 To elucidate the mass transport mechanisms in a porous medium, analytical and 3 / 36 Page 4 of 37 semi-analytical methods have been extensively applied. Quintard and Whitaker (1994) 2 developed a macroscopic model for a homogeneous porous medium using a volume 3 averaging method. They obtained analytical solutions to the longitudinal mass 4 dispersion and transverse mass dispersion for a few one-dimensional and 5 two-dimensional unit cells. Furthermore, Neculae et al. (2002) used the volume 6 averaging method to obtain the solute dispersion in a columnar dendritic mushy zone, 7 thereby improving the mass transport description during solidification modelling. To 8 investigate the effects of both heterogeneous and homogenous chemical reactions on 9 the mass transport in a porous medium, Valdés-Parada et al. (2011) applied upscaling 10 processes to achieve a mass dispersion under consideration of heterogeneous and 11 homogeneous chemical reactions, and affirmed that the increase in the Thiele moduli 12 results in a decrease in the mass dispersion. A summary of the related studies can be 13 seen in Table 1. -p re lP Table 1 Summary of the mass transfer process in a porous medium based on VAT Jo ur na 14 ro of 1 Reference Geometry Quintard and Whitaker Interfacial condition Constant concentration or zero Stratified system (1994) mass flux Neculae et al. (2002) Dendritic structure Zero mass flux Valdés-Parada et al. In-line arrangement of Homogeneous and (2011) squares or spheres heterogeneous reactions Heterogeneous reaction coupled Yang et al. (2015) Stratified system with temperature 4 / 36 Page 5 of 37 In-line arrangement of Reversible linear heterogeneous spheres chemical reaction Qiu et al. (2017) It should be noted that in the upscaling studies on mass dispersion in a porous 2 medium described above, all solid matrixes were assumed to be impermeable. 3 However, for practical applications (Gerke and Genuchten, 1993; Nakayama and 4 Sano, 2013), a porous medium with permeable particles (such as porous pellets, 5 which have an interfacial concentration discontinuity) is more frequently encountered. 6 Therefore, we prefer the use of the volume averaging theory (VAT) to obtain a 7 macroscopic solute transport equation for a porous medium with permeable particles. 8 Moreover, a three-dimensional structured geometry is chosen as representative 9 elementary volume (REV). By solving the corresponding closure problems, the 10 longitudinal mass dispersion is calculated to elucidate the mass transport mechanisms 11 in a structured porous medium with permeable particles. 12 2. Volume averaging theory -p re lP Jo ur na 13 ro of 1 Fig. 1 shows a schematic diagram of the mass transfer in a porous medium 14 composed of a void and a solid matrix. Fluid passes through the permeable void of the 15 solid matrix. Inside the permeable particles, no fluid flow is considered. Therefore, 16 there are two phases (a fluid phase in the void and a porous particle phase) when 17 considering the solute diffusion in the permeable particles. Moreover, owing to the 18 difference in solubility between the fluid phase and the porous particle phase, a solute 19 concentration discontinuity occurs at the interface (Koch and Brady, 1985). For the 20 interphase mass transfer of single drops in an immiscible liquid, Yang and Mao (2005) 5 / 36 Page 6 of 37 also considered the effect of the interfacial mass discontinuity on the overall mass 2 transfer coefficient. ro of 1 3 Fig. 1. Schematic diagram of mass transfer in porous media -p 4 To investigate the convective mass transport characteristics of a porous medium 6 with permeable catalyst particles, the governing equations for the mass transfer 7 system without the chemical reactions of the mass, momentum, and solute 8 concentration are expressed as follows: Jo ur na lP re 5 ( u) 0 t ( u) ( uu) p σ t C f t (C f u) ( D f C f ) Cm ( DmCm ) t (1) in V in Vf (2) in Vf (3) in Vm (4) 9 where is the local mass density; u is the fluid velocity vector; p is the 10 pressure; σ is the viscous stress tensor; C is the solute concentration; D is the 11 related diffusion coefficient; and subscripts f and m refer to the fluid and porous 6 / 36 Page 7 of 37 1 particle phases, respectively. Note that Dm is the intraparticle diffusivity of a solute 2 inside porous particles and is normally affected by the pore structure of the particles, 3 such as the porosity and tortuosity (Zalc et al., 2004; Xuan et al., 2010; Lin et al., 4 2019; Zheng et al., 2019). In addition, V is the total volume, whereas Vf is the fluid 5 volume around porous particles. At the interface between the fluid phase and porous particle phase, Koch and 7 Brady (1985) used the continuity of the flux and solubility conditions, which is 8 indicated as follows: ro of 6 -p n fm D f C f n fm DmCm C f mCm at Afm at Afm (5a) (5b) where n fm is the normal unit vector from the fluid phase to the porous particle phase; 10 m is the ratio of the solubilities of the solute in the fluid and in the catalyst particles; 11 and Afm is interfacial surface area between the fluid and solid phases. 12 2.1. Transformation of governing equations lP Jo ur na 13 re 9 Chastanet and Wood (2008) investigated the mass transfer process in a 14 two-region medium, although a continuous mass concentration was assumed, namely, 15 m = 1. Because m is generally different from unity, the discontinuous mass 16 concentration in the interface, as shown in Eq. (5), brings about difficulty in the 17 upscaling procedures, and thus macroscopic equations are obtained from microscopic 18 equations based on the VAT. Based on our previous study (Yang et al., 2018), the 19 proposed correlation shows better agreement with the experiment data for the case of 20 m = 5. Zhou et al. (2015) resolved the concentration under a discontinuous condition 7 / 36 Page 8 of 37 during the gas–solid adsorption process through the iteration of the boundary 2 conditions. In this way, the concentrations in the fluid and porous particle phases were 3 determined at the same time by solving two mass transfer equations directly. However, 4 it should be mentioned that this method requires more computing resources and is 5 inapplicable during upscaling procedures. Therefore, the governing equations, namely, 6 Eqs. (1)–(4), are transformed to simplify the upscaling processes. In this study, the 7 concentration transformation method first proposed by Yang and Mao (2005) was 8 used to make the concentration field continuous across the interface. For this purpose, 9 the concentration transformations are as follows: -p ro of 1 C f C f m, (6) 11 Cm Cm m , (7) 14 15 C f t m lP 13 Thus, Eqs. (3) and (4) can be rewritten separately as follows: (C f Jo ur na 12 re 10 Cm t m mu) ( mD f C f ) , ( Dm m Cm ) . (9) Correspondingly, the transformed boundary conditions are as indicated below: n fm mD f C f n fm Dm C f Cm 16 (8) m Cm (10) (11) In Eqs. (10) and (11), it can be clearly seen that, after the implementation of the 17 concentration transformation method, both the interfacial concentration and the mass 18 flux become continuous across the interface. Although there are some other 19 transformation forms, Yang and Mao (2005) pointed out that such form has no effect 20 on the numerical solutions. 8 / 36 Page 9 of 37 2 For further manipulations, the microscopic governing equations for a solute transport in a porous medium with porous particles are obtained as follows: C f (C f u) ( Df C f ) t f 3 Cm ( Dm Cm ) tm 4 5 (12) in Vm (13) where 6 t f t 7 Df D f m m tm t m and Dm Dm and u u m m (14a, 14b) (14c, 14d) (14e) -p 8 9 in Vf ro of 1 2.2. Upscaling procedures To obtain the longitudinal mass dispersion, the volume averaging method (Cheng, 11 1979; Nakayama 1995; Whitaker, 1999) was used. Macroscale concentrations are 12 defined over the representative elementary volume, shown in Fig. 2, as follows: 14 15 16 1 V lP f Vf f f dV , Jo ur na 13 re 10 f 1 Vf f f f Vf f dV , (15a, 15b) f , (15c) where f = V f V . In addition, the decomposition in terms of the average intrinsic concentration and 17 the spatial deviation concentration based on Gray’s (1975) spatial decomposition are 18 introduced as follows: C f C f f 19 Cm Cm m 20 21 C f (16a) Cm (16b) In the same way, the decomposition of the velocity can be expressed in terms of 9 / 36 Page 10 of 37 1 the intrinsic average velocity and the spatial deviation velocity, which is defined as 2 u u u (17) lP re -p ro of f 3 5 Fig. 2. Averaging volume for fluid and porous particle phases in the REV Jo ur na 4 Based on the assumption of a scale separation, usually expressed as L , the macroscopic solute transport equation of the fluid phase and 6 l f , lm r0 7 porous particle phase are given as follows: f C f f t f 8 f u f C f f D ( C f f uC f m Cm 9 tm m D ( C 1 V Amf m m m m 1 V f 1 V Amf A fm f 1 V A fm n fmC f dA) , (18) n fm Df C f dA n mf Cm dA) . (19) n mf Dm Cm dA 10 / 36 Page 11 of 37 1 2 Moreover, the boundary conditions are given as n fm Df C f C f 3 f C f n fm Dm Cm f C f Cm m Cm , Cm . m (20) (21) To close the macroscopic equations, we can obtain the closure problem to 5 eliminate the fluctuating variables. By applying decomposition Eqs. (16a) and (16b) 6 in the microscale governing Eqs. (12) and (13) and subtracting the macroscale Eqs. 7 (18) and (19), the corresponding closure problems can be obtained. Normally, the 8 governing equations of the fluctuating quantities can be further simplified through the 9 following inequalities: u C f 11 13 -p Df V f 1 Dm Cm n fmC f dA , Afm f 1 uf Cf , Dm V m1 Amf n mf Cm dA . (22) (23) (24) Jo ur na 12 re Df C f lP 10 ro of 4 For a diffusive heat transport through a two-phase material, Monye (1997) used 14 the volume averaging approach to obtain a local thermal non-equilibrium model, and 15 to compare the results of an unsteady closure and a quasi-steady closure. The 16 unsteady closure results in an extra expression of the phase exchange term and 17 conductive terms as temporal convolutions, although (surprisingly) the quasi-steady 18 closure gives satisfactory results. Therefore, the closure problems are usually assumed 19 quasi-steady, as described in related studies by Moyne (1997), Wood et al. (2003), and 20 Davit et al. (2012). Moreover, the simplified steady governing equations of the spatial 21 fluctuations are indicated as follows: 11 / 36 Page 12 of 37 1 u C f +u C f Df C f 0 Dm Cm 2 3 f m1 V Amf f 1 V A fm n fm Df C f dA , n mf Dm Cm dA . (25) (26) 2.3. One-equation model In practical applications, it is more convenient to represent the concentrations in 5 the fluid and porous particle phases in terms of a single concentration and a Darcy 6 deviation. For heat transfer in a porous medium, a similar approach was given by 7 Quintard and Whitaker (2000). Based on Eqs. (18) and (19), a one-equation model is 8 constructed. These new compositions are given through the following: Cm m 10 where C 14 15 (28) 1 V V C dV f C f f m Cm m . (29) Jo ur na 13 C Cˆ m , represents the spatial average concentration defined explicitly by C 12 (27) lP 11 C Cˆ f , re C f f 9 -p ro of 4 By substituting Eqs. (27) and (28) into Eqs. (18) and (19), the following is obtained: m C f m f t f u C m D D f Df m Dm C + f n fmC f dA m n mf Cm dA V Afm V Amf Cˆ f m Cˆ m f uC f m f f u Cˆ f f Df Cˆ f m Dm Cˆ m t m t 16 (30) 17 For the local mass equilibrium conditions, the last four terms on the right side of 18 Eq. (30) are sufficiently small to be discarded, as pointed out by Quintard and 12 / 36 Page 13 of 37 1 Whitaker (2000). Furthermore, the spatial deviation concentrations in terms of the 2 closure variables are as follows: C f b f C , 3 Cm bm C . 4 Therefore, the conductive terms in Eq. (30) can be expressed as 14 15 18 n fmC f dA (33) -p (34) re By substituting Eqs. (33) and (34) into Eq. (30), the one-equation model can be given as m C f m f t f u C f D C , m (35) where f D f Dstag + f Ddis = f Df m Dm I D D f m V A fm n fmb f dA ub f . (36) According to the transformation rules given by Eqs. (14a)–(14e), the general form of the one-equation model can be given as C *f u t 16 17 A fm Jo ur na 13 uCf ub f C = f Ddis C . 11 12 Df m Dm C + In addition, the dispersive transport term can be determined as 8 10 f ro of 6 9 Df Dm n mf Cm dA= V V Amf . Df Dm f Df m Dm I Afm n fmb f dA C f Dstag C V 7 (32) lP 5 (31) f C *f D C , (37) where f D f m Dm f m f D f Dstag + f Ddis = f D f m Dm I n fmb f dA f ub f A fm V f , (38) 13 / 36 Page 14 of 37 m f f 1 m f m m m 2 m m m m m f , (39) . (40) Note that when m is infinitive, dispersion tensor Ddis in Eq. (38) can be used in an 4 impermeable case. 5 2.4 Closure problem ro of 3 As shown in Eq. (38), the theoretical prediction of the mass dispersion tensor 7 requires the closure variable b f . Based on the closure variables defined in Eqs. (31) 8 and (32), the closure problem is given as 11 12 13 14 15 16 17 re lP 10 u b f +u Df b f 0 Dm b m m1 V Jo ur na 9 -p 6 Amf f 1 V A fm n fm Df b f dA , n mf Dm b m dA . (41) (42) The boundary conditions are as follows: b f =b m n fm Df b f =n fm Dm b m n fm Df Dm at Afm, (43) at Afm, (44) i 1, 2,3 . (45) The periodicity is as follows: b f r i b f r , bm r i bm r , Finally, the average is bf f 0, bm m 0 (46) 18 As shown in Eqs. (41) and (42), these are integro-differential closure equations. 19 To handle problems involving integrals of the closure variables as the source terms on 14 / 36 Page 15 of 37 the right-hand side of Eqs. (41) and (42), the decomposition method proposed by 2 Quintard and Whitaker (1994) and Quintard et al. (1997) was used. However, it 3 should be mentioned that the present approach is capable of solving the difficulty in 4 the upscaling procedures resulting from a discontinuous mass concentration at the 5 interface, as shown in Eq. (5b). Under the constraints of Eq. (46), Eqs. (41) and (42) 6 can be easily solved under the boundary conditions of Eqs. (43) and (44) in a periodic 7 unit cell, where the spatially periodic boundary conditions at the inlet and outlet of the 8 fluid, as shown in Eq. (45), were applied. Subsequently, based on the given results of 9 the closure variable b f , the dispersion tensor can be determined through Eq. (38). -p 3. Validation and physical geometry re 10 ro of 1 As shown in Eq. (38), the dispersion tensor includes nine elements in three 12 directions. To validate the VAT approach presented in the present study, the 13 longitudinal mass dispersion Ddisxx , which can be obtained using the spatial deviation 14 of velocity u and the value of closure variable b f , both in the x direction, is 15 compared with the results of several previous studies under the variation of the Péclet 16 number. Note that the REV is the two-dimensional in-line array of cylinders shown in 17 Fig. 3 and that the porosity is 0.48. Moreover, the Péclet number is calculated based 18 on the particle diameter rather than the equivalent particle diameter, which is defined 19 by 6 aV . The equations related to the closure problem are solved using the COMSOL 20 Multiphysics package. As the convergence criteria, the residuals of all equations are 21 less than 10-5. Furthermore, it should be mentioned that all numerical results are 22 independent of the grid size. As demonstrated in Fig. 3, the comparison between the Jo ur na lP 11 15 / 36 Page 16 of 37 present study and that by Eidsath et al. (1983) was conducted for the same REV, 2 allowing the implementations of all parameters in the VAT approach to be confirmed. 3 The agreement between these two studies was found to be satisfactory. However, it 4 should be noted that the VAT approach proposed by Eidsath et al. is only applicable 5 for a porous medium composed of impermeable particles. Because the only 6 experiment measurements of the longitudinal mass dispersion for a spatially periodic 7 porous medium applied are those of Gunn and Pryce (1969), the present VAT 8 approach was validated by comparing the numerical results with limited experiment 9 data. The figure clearly shows that our numerical results are in accordance with the -p experiment data within a large range of Péclet number. Jo ur na lP re 10 ro of 1 11 12 Fig. 3. Comparison of the present study with the numerical results by Eidsath et al. 13 and experiment data by Gunn and Pryce 16 / 36 Page 17 of 37 Based on the VAT approach, a representative elementary volume is necessary to 2 obtain the effective coefficients. In the present study, we attempted to obtain the 3 longitudinal mass dispersion in a structured porous medium with porous particles. In 4 the literature, there are several classic three-dimensional sphere-packed structures, 5 including those reported by Yang et al. (2010), Bu et al. (2014), and Wang et al. 6 (2017), such as simple cubic, body center cubic (BCC), and face center cubic ordered 7 architectures. Owing to its applicability to a large porosity variation and simplicity, a 8 BCC arrangement of spheres is chosen as the representative elementary volume, as 9 illustrated in Fig. 4. 10 11 12 13 Jo ur na lP re -p ro of 1 Fig. 4. REV in BCC arrangement of spheres 4. Results and discussion As indicated in Eq. (38), the expression of mass dispersion is given as 14 Ddis = ub f f . (47) 15 Moreover, it can be seen from Eq. (37) that the intrinsic averaged velocity in the 16 present study is f u . Therefore, the Péclet number in the present study is defined 17 as follows: f 17 / 36 Page 18 of 37 Pe 1 2 f u d p Df , (48) where d p is the particle diameter. In this section, we describe the correlation of longitudinal mass dispersion in a 4 structured porous medium with porous particles. Note that the longitudinal mass 5 dispersion is commonly expressed as DL in the literature, which is equal to Ddisxx in 6 the present study. Based on an analytical analysis conducted by Koch and Brady 7 (1985), the full form of the longitudinal mass dispersion can be written as follows: ro of 3 n (49) -p 2 n D f 1 3 n4 DL n1 m Pe b Pe ln Pe h Pe Df Dm m Note that there are three terms in Eq. (49), namely, the contributions of mechanical 9 dispersion, boundary-layer dispersion, and holdup dispersion. It should also be noted 10 that m , b , and h are the coefficients of the three counterparts of longitudinal 11 mass dispersion, which are the functions of porosity. In Eq. (49), it can easily be seen 12 that the longitudinal mass dispersion varies with several parameters, namely, ε, Pe, lP Df Jo ur na 13 re 8 Dm , and m. In the following subsections, numerical simulations related to the effects Df 14 of 15 and ε to obtain a general correlation of longitudinal mass dispersion are described. 16 4.1 Effects of m variation Dm and m on the longitudinal mass dispersion performed under variations of Pe 17 In the present study, the aim is to obtain the longitudinal mass dispersion in a 18 structured porous medium with porous particles. Because a concentration 19 discontinuity occurs at the interface between the fluid phase and the porous particle 18 / 36 Page 19 of 37 phase, the effect of an interfacial concentration discontinuity on the longitudinal mass 2 dispersion is investigated. Moreover, the interfacial mass discontinuity can be 3 represented using the solubility ratio m. Here, m 1 0 and m 1 0 represent two 4 distinct situations, namely, fixed beds with impermeable and permeable particles. In 5 Fig. 5, DL D f is plotted against the variation of Pe for ε = 0.35 and D f Dm 10 . 6 Three solubilities, namely, m = 1, 2, and 5, are considered. Furthermore, similar 7 numerical simulations were conducted for ε = 0.45 and 0.55, as indicated in Figs. 6 8 and 7. In these three figures, it can be observed that the longitudinal mass dispersion 9 of a porous medium with permeable particles is larger than that of a porous medium 10 with impermeable particles. In addition, the difference indicates the significance of a 11 holdup dispersion. This is consistent with the experiment results of Kandhai et al. 12 (2002), who used PFG-NMR to measure the longitudinal mass dispersion of a porous 13 medium with porous and nonporous particles. It can clearly be seen in these three 14 figures that with a decrease in m, the holdup dispersion contribution becomes more 15 significant, which is in accordance with the expression of the holdup dispersion. 16 Moreover, it can also be found that, for the structured arrangement, the longitudinal 17 mass dispersion of a porous medium with permeable particles is close to that of a 18 porous medium with impermeable particles when m = 5. For a random arrangement of 19 permeable particles, however, Yang et al. (2018) pointed out that the longitudinal 20 mass dispersion of a porous medium with permeable particles for m = 10 approaches 21 that of a porous medium with impermeable particles. Jo ur na lP re -p ro of 1 19 / 36 Page 20 of 37 ro of -p Fig. 5. Effect of m variation on longitudinal mass dispersion for ε = 0.35 and Df Dm =10 Jo ur na lP 2 re 1 3 4 Fig. 6. Effect of m variation on longitudinal mass dispersion for ε = 0.45 and Df Dm =10 20 / 36 Page 21 of 37 ro of -p 1 Fig. 7. Effect of m variation on longitudinal mass dispersion for ε = 0.55 and 3 4.2 Effect of D f Dm variation Df Dm =10 lP re 2 As shown in the expression of a holdup dispersion, D f Dm is also an important 5 parameter. Hence, DL D f was plotted against the variation of Pe for ε = 0.35 and m 6 = 1 in Fig. 8. The intraparticle diffusion coefficient of a porous particle is mainly 7 dependent on the microstructure of a porous particle. Hussain et al. (2015) proposed a 8 new algorithm, RGMMP, based on a connection of the macro-meso pores to generate 9 the microstructure of porous particles with hierarchical pores, such as the building 10 materials, ion exchanger resin, and molecular sieve. Based on the macro-meso 11 structures, the corresponding numerical results indicate that the connection between 12 the macro and meso pores is crucial to predict the intraparticle diffusion coefficient. 13 Moreover, Yoshida and Taktsuji (2000) experimentally obtained the intraparticle Jo ur na 4 21 / 36 Page 22 of 37 diffusion coefficients of acetoc and lactic acids in a weakly basic ion exchanger, and 2 pointed out that Df is normally one- or two-orders of magnitude higher than Dm. 3 Therefore, three ratios of the molecular diffusion coefficient of a solute to the 4 intraparticle diffusion coefficient, namely, D f Dm 2 , 5 and 50, are considered. 5 Furthermore, similar numerical simulations were conducted for ε = 0.45 and 0.55, in 6 Figs. 9 and 10. As shown in these three figures, it can be seen that the increase in 7 D f Dm results in an increase in the longitudinal mass dispersion. The reason for this 8 is consistent with the interpretation given by Maier et al. (2000), who claimed that a 9 holdup dispersion stems from dead-end pores, recirculation cells, and other regions of 10 zero fluid velocity. However, it should be noted that compared with solubility m, the 11 longitudinal mass dispersion is less sensitive to D f Dm . Jo ur na lP re -p ro of 1 12 13 Fig. 8. Effect of D f Dm variation on longitudinal mass dispersion for ε = 0.35 and m 14 =1 22 / 36 Page 23 of 37 ro of -p 1 Fig. 9. Effect of D f Dm variation on longitudinal mass dispersion for ε = 0.45 and m 3 =1 Jo ur na lP re 2 4 5 Fig. 10. Effect of D f Dm variation on longitudinal mass dispersion for ε = 0.55 and m 6 =1 23 / 36 Page 24 of 37 1 4.3 Correlation of longitudinal mass dispersion To investigate the macroscopic mass transport characteristics of a solute in a 3 porous medium, the prediction of the longitudinal mass dispersion coefficient is 4 important. There are no existing correlations of a structured porous medium that are 5 capable of predicting the longitudinal mass dispersion in a structured porous medium 6 with porous particles with an interfacial concentration discontinuity. Therefore, based 7 on the numerical results described in the previous two subsections, we aim to obtain a 8 general correlation. According to Eq. (49), the unknown values of the parameters 9 ( m , b , h ), n1, n2, n, and n4 were obtained using the least-squares method and -p are given as follows: m (-0.0421 3.449 10-4 ) ln (0.0134 2.883 10-4 ) (50) b (0.0355 2.80 104 ) / 2 (0.00927 0.00163) (51) h (0.261 3.274 1017 ) / 2 (0.684 1.908 1016 ) (52) n1 2.0 (53) n2 0.122 (54) n3 1.723 (55) n4 1.521 (56) 11 Jo ur na lP re 10 ro of 2 Furthermore, the consistency of the proposed correlation with the calculated 12 results was checked for ε = 0.45. In Fig. 11, comparisons of the calculated results and 13 the fitting results are conducted. As this figure shows, the coefficients of 14 determination R2 in these six cases are larger than 0.98, indicating that the predicted 15 results stemming from the general correlation are in good agreement with the 24 / 36 Page 25 of 37 calculated results. re -p ro of 1 3 lP 2 Fig. 11. Comparisons of calculated results and fitting results for ε = 0.45 For a random porous medium with permeable particles, Yang et al. (2018) used a 5 lattice Boltzmann simulation to calculate the asymptotic longitudinal mass dispersion 6 and presented the following correlations: 7 Jo ur na 4 D DL m Pe b Pe ln Pe h f D Df p 0.241 1 m 0.95 Pe2 (57) m (1.110 0.0268) 2 (0.00976 0.00101) (58) b (3.099 0.312) ln( ) (3.287 0.168) 1 (59) h (0.00868 3.075 106 ) / 2 (0.00852 9.374 10 6 ) (60) 8 By comparing Eq. (49) with Eq. (57), it should be noted that, for a mechanical 9 dispersion contribution, the dependence of a random porous medium and a structured 25 / 36 Page 26 of 37 1 2 porous medium on the Péclet number are Pe and Pe , respectively. Based 2 on the VAT, Quintard and Whitaker (1994) obtained the analytical solutions of 3 mechanical mass dispersion for the three cases of a structured porous medium, and 4 2 found that their dependence on the Péclet number is Pe . Therefore, it can be 5 concluded that for mechanical dispersion, a structured porous medium exhibits a 6 higher dependence on the Péclet number than that of a random porous medium. 7 Conversely, the holdup dispersion of a structured porous medium is less dependent on 8 the Péclet number than that of a random porous medium. Furthermore, a holdup 9 dispersion is also influenced by D f Dm and 1 m . However, it is interesting to note 10 that, for a holdup dispersion, a structured porous medium is less dependent on 11 D f Dm and more dependent on 1 m than a random porous medium. re -p ro of In conclusion, a general correlation of the longitudinal mass dispersion of a 13 structured porous medium with porous particles was established, which can be 14 convenient and useful for macroscopic numerical simulations of the mass transfer 15 process. Furthermore, it should also be noted that the macroscopic governing 16 equations of a mass transfer in a porous medium with porous particles can be applied 17 to the analysis and optimization of catalytic packings (Ding et al., 2015) and packed 18 bed reactors (Tanwar et al., 2018), among other areas. 19 5. Conclusions Jo ur na lP 12 20 Based on the VAT, a macroscopic solute transport equation for a porous medium 21 with porous particles and an interfacial concentration discontinuity was established. 26 / 36 Page 27 of 37 1 For the difficulty resulting from the interfacial concentration discontinuity during the 2 upscaling process, a concentration transformation method was utilized. To obtain a 3 longitudinal mass dispersion, a BCC arrangement of the spheres was chosen as the 4 REV. By solving the closure problem, the effects of D f Dm and m on the 5 longitudinal mass dispersion were investigated under variations of ε and Pe. Based on 6 the results achieved in this study, the following conclusions can be drawn: (1) The concentration transformation method used herein is effective at handling 8 the interfacial concentration discontinuity during the upscaling process. Furthermore, 9 a resolution of the closure problem was proposed to obtain the longitudinal mass -p 10 ro of 7 dispersion. (2) The variations of D f Dm and m exhibit an adverse influence on the holdup 12 dispersion. Moreover, the holdup dispersion is more sensitive to m than D f Dm . It 13 can also be seen that the decrease in m significantly augments the longitudinal mass 14 dispersion, indicating the significance of a holdup dispersion. lP Jo ur na 15 re 11 (3) A general correlation of the longitudinal mass dispersion for a structured 16 porous medium with an interfacial concentration discontinuity was obtained based on 17 the least-squares method. In comparison with the existing correlation of a random 18 porous medium, it is interesting to note that, for a mechanical dispersion, the 19 dependence of a random porous medium and a structured porous medium on the 20 2 Péclet number are Pe and Pe , respectively. Furthermore, was also found 21 that for a holdup dispersion, a structured porous medium is less dependent on 22 D f Dm and more dependent on 1 m than a random porous medium. 27 / 36 Page 28 of 37 Nevertheless, the current approach is only applicable for a single-phase fluid 2 system with a single component. Because two-phase flows with multiple components 3 in a porous medium appear in numerous chemical engineering systems, such as 4 chemical reactors and the transport used in petroleum reservoirs, we will address this 5 problem in a forthcoming study using the ideas put forth by Soulaine et al. (2011), 6 who dealt with the upscaling of a multi-component two-phase flow in a porous 7 medium. 8 Acknowledgements -p ro of 1 The authors would like to express their sincere thanks to the National Natural 10 Science Foundation of China (Project Nos. 91534106 and 21506032) for financially 11 supporting this study. The authors would also like to thank Prof. Gérald Debenest of 12 Institut de Mécanique des Fluides de Toulouse (IMFT), France and Dr. Jianwei Guo 13 of the School of Mechanics and Engineering, Southwest Jiaotong University for their 14 valuable suggestions. 15 Nomenclature Jo ur na lP re 9 aV interfacial surface area per unit cell volume (m-1) Afm interfacial surface area between the fluid and solid phases (m2) b closure variable (-) C solute concentration (mol/m3) D mass diffusivity (m2/s) 28 / 36 Page 29 of 37 D effective mass diffusivity tensor (m2/s) dp particle diameter (m) I unit matrix (-) L macroscopic characteristic length (m) i i = 1, 2, 3, lattice vectors (m) microscopic characteristic length (m) m ratio of the solubilities of the solute in the fluid and in the catalyst ro of l particles (-) normal unit vector from the fluid to the catalyst particle surface (-) -p n fm coefficients in Eq. (47) (-) p pressure (Pa) Pe Péclet number (-) r position vector (m) ro characteristic length related to averaging volume R2 coefficient of determination Jo ur na lP re n t time (s) u fluid velocity vector (m/s) V volume (m3) x, y, z Cartesian coordinates (m) Greek letters ε porosity (-) density (kg/m3) 29 / 36 Page 30 of 37 σ viscous stress tensor (Pa) arbitrary variable coefficients in Eq. (34) Special symbols bulk average f ,m intrinsic average deviation from intrinsic average transformation form defined in Eqs. (14) ˆ large-scale spatial deviation defined in Eqs. (27) and (28) -p Subscripts and superscripts boundary-layer re b ro of dispersion f fluid phase h holdup L longitudinal mass dispersion m mechanical or porous particle phase p pore Jo ur na lP dis stag stagnant 1 2 3 30 / 36 Page 31 of 37 1 References 2 Bear, J., 1988. Dynamics of fluids in porous media. 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