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Phase-Field Simulation of Polymer Crystallization During Cooling Stage

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Phase-Field Simulation of Polymer Crystallization During Cooling Stage International Journal of Chemical Engineering and Applications, Vol. 6, No. 1, February 2015 Phase-Field Simulation of Polymer Crystallization during Cooling Stage Xiaodong Wang and Jie Ouyang and growth, in order to perform cross-scale simulation on Abstract—The phase-field method has been developed to envelop profiles of the domain and the internal structure of simulate the crystal growth of semi-crystalline polymer during spherulites. Ruan et al. [4] proposed a pixel coloring cooling stage by considering the effect of temperature on the technique based on the Hoffman-Lauritzen theory to capture nucleation density. It assumes that the nucleation mechanism is heterogeneous, and the relationship between the nucleation the crystallization morphology evolution and calculating the density and the temperature is described by an empirical crystallization kinetics. According to coupling the function. The crystal growth after nucleation is modeled by a temperature field, non-isothermal crystallizations of polymers modified phase-field method which uses a non-conserved crystal with or without short fiber reinforced components were order parameter to indicate whether the material is solid or simulated. liquid. By using the proposed model, the influence of cooling Except the above mentioned approaches, the phase-field rate on the crystallization morphologies and crystallization kinetics has been investigated. methods can also be used to model the crystallization morphologies of semi-crystalline polymers [5]-[9]. This kind Index Terms—Polymer, crystallization, phase-field, cooling. of method has been successfully applied to simulate the polycrystalline growth of polymers and reveal the corresponding formation mechanisms under isothermal I. INTRODUCTION conditions. However, for the crystallizations under It is well known that polymer is one of the most important non-isothermal conditions, the phase-field methods have not technical materials in our daily life. The cooling conditions yet been applied. In this paper, we are aim to develop a imposed during polymer processing affect the crystallization phase-field method to simulate the crystal growth of morphologies of semi-crystalline polymers, thus determining semi-crystalline polymers during cooling stage by the final properties of polymeric products. Therefore, the considering the effect of temperature on the nucleation prediction of microstructure formation in semi-crystalline density. polymers during cooling stage is of great importance. For doing this, it is essential to establish an effective numerical model to predict the crystallization morphologies. The II. THEORETICAL MODEL simulated results may supply theoretical basis for controlling A. Modeling of Nucleation or optimizing the fabrication procedures. Earlier approaches to model the crystallization Nucleation is the first stage of polymer crystallization. It morphologies of semi-crystalline polymers during provides the template (nucleus) for further crystal growth. It solidifications were discussed in detail by researchers. For starts with nanometer-sized areas where some chains or their instance, Micheletti and Burger [1] developed an efficient segments occur parallel as a result of heat motion. Those algorithm for simulating the stochastic birth-and-growth seeds can either dissociate, if thermal motion destroys the process of non-isothermal crystallization of polymers. They molecular order, or grow further, if the grain size exceeds a showed how non-isothermal crystallization can be simulated certain critical value. The number of nuclei can be modeled either by a stochastic or a deterministic approach. Raabe and by some empirical nucleation models in the cases of both Godara [2] studied the kinetics and topology of spherulite heterogeneous nucleation and homogeneous nucleation [10], growth during crystallization of isotactic polypropylene (iPP) [11]. In this work, we assume that the nucleation mechanism by using a three-dimensional cellular automaton model. The is heterogeneous. The following equation reported in use of experimental input data for polypropylene was helpful literature [10] is used to describe the relationship between the for giving quantitative simulations. Huang and Kamal [3] nucleation density and the temperature: proposed a physical model for multiple domain nucleation 0 N(T) N0 exp((Tm T)) , (1) Manuscript received May 31, 2014; revised July 7, 2014. This work was where N(T) is the temperature dependent nucleation supported in part by supported by the National key Basic Research Program 0 of China (973 Program) (No: 2012CB025903), the Northwestern density, Tm is the equilibrium melting temperature, N0 and Polytechnical University Foundation for Fundamental Research are empirical parameters. (JCY20130141), the Doctorate Foundation of Northwestern Polytechnical University (No: cx201019) and the Ministry of Education Fund for Doctoral Students Newcomer Awards of china. B. Modeling of Crystal Growth X. D. Wang and J. Ouyang are with the Department of Applied According to our previous work [12], [13], the crystal Mathematics, Northwestern Polytechnical University, Xi’an, 710129, China (e-mail: [email protected], [email protected]). growth of polymer can be modeled by a modified phase-field DOI: 10.7763/IJCEA.2015.V6.445 28 International Journal of Chemical Engineering and Applications, Vol. 6, No. 1, February 2015 model, which uses a non-conserved crystal order parameter 1/ 2 2 to indicate whether the material is solid or liquid. In phase 0 6 , (10) field modeling, the value of is usually bound between 0 nRT W and 1, where 0 represents the melt and 1 the crystal at where Tm is the supercooling-dependent melting temperature, equilibrium. The temporal evolution of the crystal order Tc is the experimental temperature of crystallization, H is parameter can be modeled by the following non-conserved phase-field equation: the latent heat, R is the gas constant, is the surface free energy per unit area, and n is the amount of substance of (x,t) F( ) polymer monomers per unit volume. , (2) t (x,t) C. Computational Form In computations, dimensionless form is usually considered where (x,t) representing the crystal order parameter at to be more convenient for applications. In order to write the time t and position x . is the mobility which is inversely phase field equation (6) in dimensionless form, the temporal proportional to the viscosity of the melt. F( ) is the total and spatial variables are rescaled to Dt / d 2 , ~x x / d , free energy of the crystallizing system defined as ~y y / d . Here d is the characteristic length for single crystals, D is the diffusion coefficient. The mobility can F( ) [ flocal ( ) f grad ( )] dV , (3) be estimated from and as D / d 2 . With these where the local free energy density flocal () and nonlocal dimensionless variables, the final governing equations can be represented in the following dimensionless form: free energy density fgrad () are 4 W( )( ) 2 , (11) 0023 00 flocal (,), T W (4) 2 3 4 where and 2 ~ ~2 0 1 ( , ) , 0 . (12) f ()() 22 , (5) ~x ~y d 2 grad 2 0 respectively. Here, 0 and correspond to the stable solidification potential and unstable energy barrier, W is a III. RESULTS AND DISCUSSION dimensionless coefficient describing the height of energy Simulations are carried out in two-dimension for the barrier for surface nucleation, 0 is the coefficient of models presented above. The model parameters for 93 interface gradient. Substituting equations (3)-(5) into nucleation are Nm0 1.74 10 / and 0.155 [10]. equation (2), one obtains For crystal growth, the model parameters can be calculated using a set of material parameters of isotactic polystyrene (iPS) (,)()x tF from references [12], [13]. According to these data, a set of model parameters are calculated and specified in Table I. In tt (,)x (6) the simulations, a square lattice of size 512512 is employed. 2 W( )( 00 ) The dimensionless temporal step size and spatial step size are chosen as =0.2 and xy 2.0 , respectively. From references [5], [12], [13], the model parameters can be calculated by TABLE I: MODEL PARAMETERS OF IPS AT A GIVEN EXPERIMENTAL TEMPERATURE OF 200 °C Tm 0 0 , (7) Symbol Data set Symbol Data set Tm 0 o Tm 242 C 0 0.95 o 0.167 2 Tm 230 C 40 ˆ 3 ˆ 2.0 TTm o 1.0 arctan(k ) , (8) 200 C 15.43 64 ˆ TT 5 ~2 0 mc 10 0 0.916 T 0 T In this work, the material temperature is assumed to be (here ˆ m m ) 0 0 uniformly reduced from to with a constant cooling Tm T rate c . That is the temperature at time instant can be 1 directly calculated by T T ct . In simulations, the H T T m W 6 m 0 , (9) 3 0 initial data of and T are prescribed as 0 and , RT 0 Tm 2 respectively. The simulations include repeating the following 29 International Journal of Chemical Engineering and Applications, Vol. 6, No. 1, February 2015 three steps before the temperature reaches to a given produces small crystals and slower cooling produces larger ones. What’s more, the larger the cooling rate, the narrower experimental temperature Tc and only the third step after the the range of crystal size distribution. Besides, the growth rate temperature reaches to . increases as the cooling rate increases. This can be seen more Step 1: calculate the temperature by . clearly from Fig. 3. It is also illustrated that the crystallinity is Step 2: calculate the nucleus number by equation (1) and a decreasing function of cooling rate. create new nuclei randomly in the material domain ( is prescribed as 0 at nuclei). Step 3: solve equation (11) over the rectangular material domain with homogeneous Neumann boundary conditions 0 . (a) (b) cC12o / min ts120 ts100 (a) ts 20 (b) ts 40 (c) cC18o / min (d) cC 24o / min ts 90 ts 70 (c) ts 60 (d) ts 80 (e) cC 30o / min (f) cC 60o / min Fig. 2. Crystalline morphologies of different cooling rates. (e) ts100 (f) ts120 Fig. 1.
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