In this paper, a computational fluid dynamics (CFD) model to predict the transient temperature distributions of a prismatic lithium-ion polymer battery (LiPo) cooled by natural convection at various discharge cycles is developed. The thermal behavior of a lithium-ion (Li-ion) battery cell is important for its safety, performance and degradation, and it requires both measurement and modeling. However, most existing thermal models for Li-ion battery cells only account for steady-state temperature fields, while the exercise of a Li-ion battery cell is usually transitory. The Newman’s pseudo-2D approach was used to perform an electrochemical CFD analysis. This approach treats the porous electrode as a collection of equal-sized, isotropic, homogeneous spherical particles. This simplifies the electrode microstructure and assumes a smooth and uniform lithium insertion/extraction in the electrode. The model has been validated through variable discharge rate experimental tests in a controlled chamber. Additionally, infrared images of the battery cell during discharging are taken and the experimental numerical gradient temperature was compared. We have validated the CFD simulations by comparing the temperature, state of charge and voltage curves with experimental data. The model predictions match the experimental data very well. The difficulty in CFD battery simulations with an electrochemical approach lies in the setting of many physical parameters that are difficult to find. In this work, the parameters’ characteristics of the simulated battery are assumed and validated; these can be useful for modeling batteries of the same type. Consequently, the model developed in this work can be applied to predict the temperature distribution of the LiPo prismatic battery and can be used by the battery designers and by the designers of all systems that include batteries.

In this work, a statistical method is applied to a multiphase CFD simulation of concrete mixing performed in a truck mixer. The numerical model is based on an Eulerian–Eulerian approach in a transient regime. The aggregate materials are simulated as dispersed solid particles of various diameters, while the cement paste is simulated as a non-Newtonian continuous fluid. The first ten drum revolutions are analyzed from the condition of the completely segregated materials. The cell mixing index, defined by a statistical method in terms of mean, variance, and density probability function, is applied to the analysis of the simulation results. The statistical variables are implemented using the fluid dynamics code in the post-processing result analyses. The method predicts the distribution efficiency of the materials within a truck mixer as a function of its internal geometry, rotation speed, and mixture composition. As the number of revolutions increases, the distribution qualitatively improves, as shown by the motion fields, velocities, and vortices of the various materials, quantified through the calculation of the mixing index. The illustrated method can be used to predictively calculate the distribution effectiveness of new truck mixer designs before prototyping them and can be applied to other types of mixers. Furthermore, this study can be applied to liquid–solid mixing processes analyzed via the Eulerian multiphase numerical approach.