Introduction to Discrete Phase Modeling (DPM) in Ansys Fluent

Discrete Phase Modeling | Ansys Fluent Webinar:

When attempting to analyze the behavior of the particles from both a Lagrangian and discrete perspective, a discrete phase modeling (DPM) is utilized by engineers.  Learn how to use Discrete Phase Modeling in Ansys Fluent through this on-demand webinar.

Discrete Phase Modeling (DPM): What Is It?

A division of multiphase flows is the discrete phase model. When attempting to analyze the behavior of the particles from both a Lagrangian and discrete perspective, a discrete phase model (DPM) is often employed via Ansys Software. The difference between the Lagrangian and Eulerian views is that the Eulerian perspective assumes a finite volume element in the fluid flow route while the Lagrangian view bases its analysis of fluid behavior on particle tracking of a particulate flow.

Navier-Stokes equations are used in the discrete phase model to solve the continuous phase. Countless particles, bubbles, or droplets are tracked at once to approximate the discrete phase.  Specifically, those that cross the predicted continuous flow field.

Notes on Continuous Change Analysis in Ansys

It should be emphasized that the discrete phase and the continuous phase can exchange momentum, mass, and energy. By eliminating the interactions between particles (as well as droplets and bubbles), this method can be made much simpler.

Of course, this is possible if the discrete phase has a volume that is significantly lower (less than 10%) than the continuous phase, despite having a substantial mass. The continuous phase computations are repeated after each iteration, and the particle trajectories are calculated and established individually.

Additionally, it should be mentioned that the DPM model is used in the simulations below to represent how well a dehumidifier performs:

Model for Dense Discrete Phases (DDPM)

The kinetic theory of granular flows is used to represent particle collisions and uncorrelated translations in the dense discrete phase model (DDPM), which is a Lagrangian parcel-based method (KTGF). Comparing this method to the well-known Eulerian two-fluid model has many benefits (TFM).

These include more natural incorporation of particle size distributions, improved management of crossing particle jets/clusters, and improved resolution of particle clusters and bubbles. In this work, a wide variety of fluidization velocities, particle sizes, and bed loadings are considered in comparison to data from the DDPM, dedicated experiments, and TFM simulations.

The frictional effects typical of such dense particle fluxes were specifically taken into consideration. The studies carried out by the TFM on a noticeably coarser mesh are demonstrated to be reproducible by the DDPM.

Discrete Phase Modeling (DPM) in Ansys

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Ansys Linear Dynamics & Mechanical Nonlinear Dynamics

Course Summary | Ansys Linear Dynamics

An in-depth review of the many dynamic analysis types accessible in Ansys Mechanical is provided in this training course. The ideas provided in this course will arm you with information essential to a successful design if your design involves any kind of motion.

Knowing what kind of analysis is required based on the data contained in a design or test specification can be difficult. This course will give you the confidence to understand the results and make design decisions based on them while allowing you to select the style of analysis that is most appropriate for your needs.

It does this by combining a mixture of theoretical background with practical guidance and examples.


Ansys Linear Dynamics | Engineering Training Outcome

After completing this program, the student will be capable of:

  • Recognize the variations among the various dynamic analysis types.
  • Select the appropriate dynamic analysis for your application.
  • For a specific dynamic analysis, be aware of when and how to apply damping.
  • To better comprehend how a structure might react to various applied forces, use modal analysis.
  • In order to tackle complicated dynamic issues within appropriate time restrictions, effective modeling and analysis approaches should be used.
  • With more assurance and comprehension, interpret the analysis’ findings.

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