Partially filled internal batch mixers are used for mixing of rubber compounds in the polymer industry. The use of mixing in such mixers equipped with a rotor is critical to the process itself, and hence, understanding of mixing is important in terms of evaluating how various operating parameters such as rpm, fill factor, and ram pressure affect distribution and dispersion of materials. The objective of the current study is to gain valuable insights on the influence of fill factor, which is the volume of the material relative to the volume of the chamber. Two‐dimensional (2D) computational fluid dynamics (CFD) simulations of rubber mixing in a 2‐wing rotor‐equipped chamber are presented here, for the first time, for fully‐filled/100% and partially‐filled/75% chambers. The volume‐of‐fluid (VOF) technique is employed to capture the interface between the rubber and air in partially filled isothermal simulations. Flow patterns are visualized to analyze the material movement. Massless particles are injected and various statistics are calculated from their positions in order to compare dispersive and distributive mixing characteristics between the fully‐filled and partially‐filled cases. Specifically, quantities such as mixing index and the maximum shear stress distribution history of particles are analyzed to obtain information about dispersive mixing, while length of stretch and cluster distribution index, also calculated from particles, are presented to investigate distributive mixing capabilities. All the results consistently demonstrated the superior effectiveness of partially‐filled mixing chambers in terms of their dispersive and distributive mixing characteristics in comparison to fully‐filled chambers. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 44250.

Three-dimensional, transient, isothermal, and incompressible computational fluid dynamics (CFD) simulations are carried out for rubber mixing with two counter-rotating rotors in a partially filled chamber in order to assess the effect of different speed ratios. The three different speed ratios that are investigated include 1.0, 1.125, and 1.5. In addition to the solution of the incompressible continuity and momentum equations, a Eulerian multiphase model is employed to simulate two phases, rubber and air, and the volume of fluid (VOF) technique is used to calculate the free surface flow between the phases. The Bird–Carreau model is used to characterize the non-Newtonian highly viscous rubber. Massless particles are injected in the simulations to obtain data required for statistical calculations related to dispersive and distributive mixing characteristics. Specifically, joint probability density functions of mixing index and shear rate, and cumulative distribution functions of maximum shear stress are calculated to assess dispersive mixing, while distributive mixing capabilities are evaluated using various quantities such as cluster distribution index, axial distribution, interchamber particle transfer, and segregation scale. Results showed the speed ratio 1.125 to be consistently superior to 1.5 and 1.0, in terms of both dispersive and distributive mixing performance. The large speed difference between the rotors in the case of 1.5 caused it to perform the worst.

Among several operational parameters such as rotor speed, fill factor and ram pressure, the orientation of the mixing rotors with respect to each other plays a significant role in the mixing performance. An understanding of the flow field and mixing characteristics associated with the orientations of the rotors will help in obtaining a final product with a better quality. For that purpose three phase angle orientations: 45°, 90° and 180° are investigated here in a 75% filled chamber with two rotors counter-rotating at an even speed of 20 min–1. Two dimensional, transient, isothermal, incompressible simulations are carried out using a CFD code. While an Eulerian multiphase method was used to solve for the transport variables in the two phases: rubber and air, the volume of fluid (VOF) method was used to solve for the interface between the two phases. A non-Newtonian Carreau-Yasuda model was used to characterize rubber. Massless particles were injected in the domain to calculate statistical quantities in order to assess dispersive and distributive mixing characteristics associated with rotor orientations. The flow field is analyzed via pressure and velocity contours. Dispersive mixing was analyzed through histograms of mixing index and cumulative probability distribution functions of maximum shear stress experienced by the particles. Distributive mixing was quantified statistically using cluster distribution index and interchamber material transfer. The phase angle of 180° was found to perform the best in terms of both dispersive and distributive mixing characteristics.

The industrial process of manufacturing tires brings together all the ingredients required to mix a batch of rubber compound in an operation called mixing. The development and use of mixing in a mixing chamber equipped with a rotor has a significant impact on the process itself, and understanding mixing is important in terms of evaluating how material, mixer design, and operating variables (e.g., rpm, temperature, ram pressure) affect distribution, dispersion, and coupling reaction. One of the most important factors to consider is the fill factor, which is the volume of the material relative to the volume of the chamber. It is critical to determine the operating regime in terms of the level of mixing material in the chamber to satisfy all the mixing requirements of the process. Furthermore, the availability of modern high-performance computing resources and accurate mathematical models makes computational fluid dynamics (CFD) an important and necessary tool in understanding some of the complex physical and chemical phenomena associated with such industrial manufacturing problems. The objective of this paper is to assess the effect of fill factor in a two-wing rotor geometry that is used for rubber compounds mixing in the tire manufacturing process and thereby determine the best fill factor with regard to providing the highest mixing efficiency. A series of 3D CFD simulations in a mixing chamber with fill factors of 45, 60, 75, 90, and 100%, stirred by counter-rotating rotors, were carried out using a CFD code. Flow patterns, mixing index, particle trajectories, and statistics such as segregation scale, length of stretch, and pairwise distribution are presented to understand the mixing process with a long-term goal of improving product quality and throughput. Results showed that the major mixing mechanism is shear for most of the fill factors and that the 75% fill factor has the best distributive mixing characteristics among the fill factors studied here.

A finite volume technique is used to analyze the isothermal and non-isothermal flow behavior for the rubber mixing process in a two-dimensional, partially filled (75%) internal mixer, which consists of two counterrotating rotors rotating at 20 rpm. In order to capture the interface between air and rubber, an Eulerian multiphase model called volume of fluid (VOF) has been employed here. The transient flow behavior was accomplished by a sliding mesh technique, and the highly viscous, non-Newtonian properties of the rubber have been characterized using the Bird–Carreau model. Most of the previous computational fluid dynamic (CFD)-based investigations of rubber mixing assumed isothermal flow, and consequently negligible viscous heat generation, temperature rise, and viscosity drop associated with heat generation. Hence, a non-isothermal simulation is carried out, and results are compared with those of an equivalent isothermal simulation. In addition, dispersive and distributive mixing characteristics are assessed using statistics calculated from particle tracks generated by a set of massless and neutral particles that have been injected in the simulation. For this purpose, quantities such as the cumulative distribution of maximum shear stress, length of stretch, and cluster distribution index are calculated and compared between isothermal and non-isothermal conditions. Results showed a significant difference between the isothermal and non-isothermal simulations, thus making the isothermal assumption critical. Also, the non-isothermal simulation predicted better mixing during the entire mixing cycle.