The interaction between liquids, solids and gases must be taken into account in chemical reactions calculation. In these wide variety of multiphase flow applications, each type of simulation requires a different modeling method. Scientists therefore have developed multiple simulation tools that can accurately simulate multiphase flow. The development of computational fluid dynamics provides a basis for further understanding of the dynamic characteristics of multiphase flow. At Alfa Chemistry, we provide Euler multiphase flow models: volume of fluid (VOF) model, mixture model, Eulerian model, dense discrete phase model and discrete element model.
Figure 1. Phase fraction results of CFD, neural network and error distribution. (Cuzman, O. A. 2015)
Scope of Applications
- Investigate the effective area and the created liquid film in the structured packings.
- Study the mixing process in different types of solids stirred with multiple solutions.
Our accurate multiphase flow simulation relies on accurate prediction of the mechanical, thermal, and chemical interactions between the phases. Our teams have rich experience in establishing various models to carry out high-quality simulation. Our reliable and high-quality services include the following:
- VOF model
VOF model is suitable for dealing with two or more immiscible fluids. Our teams focus on the position of the interface between the fluids. In the VOF model, fluids can share a single set of momentum equations. The VOF model can be applied to stratified flow, free-surface flow, filling, and so on.
- Mixture model
A mixed model is suitable for two or more phases (fluid or particles). The phases are regarded as interpenetrating continuums in the Euler model. We develop a mixed model to solve the mixed momentum equation, and use relative velocity to describe the dispersed phase. The mixed model can be applied to low-loading particle flow, bubble flow, sedimentation and cyclone separators.
- Eulerian model
A Euler model is a complex multiphase flow model. Coupling through the pressure and interphase exchange coefficients is achieved. Types of involving phases effect on the manner in which coupling is handled. Handling of granular (fluid-solid) flows are different than non-granular (fluid-fluid) flows. For granular flow, we use kinetic theory to get the properties of granular flow. The momentum exchange between the two phases also depends on the type of mixture being simulated. We use different functions to define the calculated momentum exchange. The Euler multiphase flow model can be applied to: bubble tower, riser, particle suspension and fluidized bed.
- Dense discrete phase model (DDPM) and discrete element model (DEM)
In DDPM, we use the theory of particle flow dynamics to simulate large-scale particle systems. In addition, it can also be coupled with Euler models to simulate various multiphase flow processes. Our groups apply DEM to model the interaction between particles, simulating particle shape, rotation, collision and so on.
- Perform the VOF model for layered or free surface flow.
- Perform Mixing and Euler models for phase mixing or separation or flow where the volume fraction of the dispersed phase exceeds 10%.
- If the dispersed phase is widely distributed, the mixing model is developed for simulation.
- If the dispersed phase is only concentrated in a part of the domain, the Euler model is chosen for simulation.
Computational fluid dynamics multiphase flow simulation provides an effective way to optimize the chemical process. Our computational fluid dynamics multiphase flow simulation services remarkably reduce the cost, promote further experiments, and enhance the understanding of chemical process for customers worldwide. Our personalized and all-around services will satisfy your innovative study demands. If you are interested in our services, please don't hesitate to contact us. We are glad to cooperate with you and witness your success!
- Hosseini Boosari, S. Predicting the Dynamic Parameters of Multiphase Flow in CFD (Dam-Break Simulation) Using Artificial Intelligence-(Cascading Deployment). Fluids, 2019, 4(44).