Comprehensive Overview of Particulate Flow Modeling in ANSYS FLUENT This educational resource serves as the second installment in our Discrete Phase Model (DPM) training series, providing an extensive examination of particulate flow simulation capabilities within ANSYS FLUENT. The presentation methodically explores the full spectrum of DPM functionality, covering essential configuration options and specialized modeling approaches. Key Topics Covered: Core Configuration Elements DPM Interface Components: Settings for phase interaction and particle treatment methodologies Trajectory Calculation Parameters: Controls governing the numerical tracking process Advanced Physical Phenomena Environmental Interactions: Radiation effects, thermophoresis, and various force models (Saffman lift, virtual mass, pressure gradient) Surface Interactions: Erosion/accretion modeling capabilities Phase Change Dynamics: Pressure-influenced boiling and temperature-dependent latent heat models Particle Interaction Mechanisms: Two-way turbulence coupling, DEM collision modeling, stochastic collision simulation, coalescence, and breakup processes Particle Introduction Methods Injection Configurations: Single-point, grouped, surface-based, and conical release patterns Particle Classification Options: Massless tracers, inert particles, liquid droplets, combusting particles, and multi-component formulations Particle Property Distributions Size Distribution Models: Linear, uniform, Rosin-Rammler, and logarithmic Rosin-Rammler approaches Specialized Physics Models Drag Formulations: Spherical, non-spherical, Stokes-Cunningham, high-Mach-number, and dynamic drag laws Droplet Breakup Models: TAB and Wave methodologies Turbulent Transport Models: Stochastic tracking and cloud tracking approaches Computational Efficiency: Parcel modeling techniques Boundary Condition Treatments Particle-Boundary Interactions: Reflection, trapping, escape, wall-jet, and wall-film behaviors

This research examined snowfall patterns within a park environment utilizing the Discrete Phase Material (DPM) approach. The simulation incorporated two material types: air as the continuous phase and discrete snow particles. Particle movement trajectories throughout the park space were tracked and analyzed using Ansys Fluent computational software. Modeling Approach The three-dimensional geometric model was developed through Spaceclim software. For computational analysis, an unstructured mesh containing 1,553,972 elements was created in the Ansys meshing module, with the Curvature Method applied to enhance resolution in areas requiring greater computational precision. Simulation Parameters The computational model operated under several key assumptions: Flow equations were not solved Time-dependent (transient) simulation approach Gravitational acceleration of 9.81 m/s² applied downward along the y-axis The DPM configuration included: Surface velocity inlet injection Rosin-Rammler diameter distribution Particle diameter range: 1×10⁻⁴m (minimum, mean, and maximum) Mass flow rate: 1×10⁻²⁰ kg/s Material properties included air and inert particles with density of 1550 kg/m³ Boundary Conditions Inlet: Velocity inlet (0 m/s) with DPM escape condition Symmetry conditions applied to symmetrical boundaries Wall conditions (stationary, no-slip, wall film) applied to bench, ground, leaves, road, and wood elements Standard initialization method implemented The simulation results yielded particle tracking data throughout the park environment, with accompanying snowfall animation documentation.

This study examines the transport and deposition patterns of dust particles entering a room through windows using ANSYS Fluent computational fluid dynamics (CFD) software. Model Development The three-dimensional model was created using Design Modeler software, representing a room configuration with two windows and a chimney. The computational mesh was generated in ANSYS Meshing with 42,061 elements. Given the time-dependent nature of particle movement, a transient solver approach was implemented. Simulation Methodology The analysis focused on dust particles entering the room at a velocity of 0.25 m/s, with particular emphasis on tracking their movement patterns and sedimentation behavior throughout the interior space. Key simulation parameters included: Discrete Phase Model (DPM) implementation to capture particle sedimentation Two-way coupling between particles and airflow to accurately represent particle-fluid interactions Laminar flow model for solving fluid equations The boundary conditions established air entering through the two windows (inlets) and exiting via the chimney (pressure outlet), creating a realistic flow path through the room. Results and Findings The simulation produced two-dimensional visualization outputs including: Velocity contours Velocity vector fields Flow streamlines These results demonstrated how airflow patterns directly influence dust particle transport within the room. Notably, areas with vortex formation showed greater potential for dust accumulation and sedimentation, as particles became trapped in recirculation zones rather than following the main flow path toward the chimney outlet. The analysis provides valuable insights into indoor air quality dynamics and potential dust accumulation zones within residential spaces.

This study presents a computational fluid dynamics simulation of a particle trapping mechanism known as the discrete phase trap (TRAPPER), analyzing its efficiency and flow characteristics using ANSYS Fluent software. Background and Significance Discrete phase flows are increasingly prevalent across mechanical and engineering applications. As a subset of multiphase flow systems, dispersed multiphase flows—including bubble, droplet, and particle flows—require thorough understanding for optimal system design. In these systems, a carrier phase transports dispersed elements (particles, bubbles, or droplets) throughout the domain. CFD simulation provides critical insights for optimizing such systems. Model Development The computational model was developed using ANSYS design modeler and meshed with ANSYS meshing software. Key specifications included: Unstructured mesh configuration 420,485 elements for computational accuracy Inlet flow velocity of 5 m/s containing both continuous and dispersed phases Simulation Methodology The discrete phase model (DPM) was employed to capture particle behavior with several key physical phenomena incorporated: Saffman lift force to account for shear-induced lateral movement Pressure gradient forces affecting particle trajectories Gravitational effects, which play a central role in the trapping mechanism Results and Analysis The simulation produced comprehensive visualization outputs including: Pressure distribution contours Velocity fields Particle tracking pathlines Analysis of the results demonstrated that the TRAPPER mechanism successfully captured 45.26% of the particles entering the system. The simulation also revealed significant interaction between the fluid and particle phases, with notable velocity increases observed in regions of higher particle concentration, confirming the importance of two-way coupling in accurately modeling such systems. This analysis provides valuable design insights for improving particle separation efficiency in industrial applications utilizing gravity-based trapping mechanisms.

This study presents a computational fluid dynamics simulation examining the effectiveness of face shields in preventing coronavirus particle transmission during conversation between individuals at close proximity. Model Development A three-dimensional computational domain (1.6m × 2m × 2.6m) was created using Design Modeler software, representing two individuals facing each other at a distance of 80cm—below recommended social distancing guidelines. One individual was designated as infected, with their mouth serving as the source of viral particles during speech. The computational mesh was generated using ANSYS Meshing software with 724,076 elements. Given the time-dependent nature of particle dispersion, a transient solver approach was implemented with time steps of 0.001 seconds. Simulation Methodology The Discrete Phase Model (DPM) was employed to track individual virus particles within the continuous airflow medium. Key simulation parameters included: Particle classification: Inert type Injection method: Surface-based emission from patient’s mouth Particle diameter: 1 micrometer (10⁻⁶ m) Particle temperature: 310K (body temperature) Emission duration: 0-20 seconds Velocity profile: Sinusoidal function with 0.33 m/s maximum velocity Flow rate: Proportionally linked to particle velocity Boundary conditions were configured with: “Escape” condition at patient’s mouth (allowing particle emission) “Trap” condition at mask/shield surfaces (capturing particles) The RNG k-epsilon turbulence model was utilized alongside the energy equation to accurately capture flow dynamics and temperature distribution within the domain. Results and Analysis Particle tracking visualizations were generated at various time intervals throughout the 20-second simulation period, with particles colored according to residence time and velocity. The results clearly demonstrated that protective face shields effectively intercepted viral particles expelled during speech, preventing their transmission to the healthy individual. The periodic emission pattern of particles from the infected individual’s mouth was successfully captured, showing how particles accumulated on the shield’s inner surface rather than reaching the second person. This confirms the effectiveness of face shields as a protective barrier in close-proximity interactions, supporting medical recommendations for their use when maintaining proper social distance is challenging.

This study presents a computational fluid dynamics simulation examining coronavirus particle transmission during face-to-face conversation at sub-optimal social distancing without protective equipment. Model Development A three-dimensional computational domain (1.6m × 2m × 2.6m) was created using Design Modeler software, representing two individuals facing each other at a distance of 80cm—below recommended social distancing guidelines. One individual was designated as infected, with their mouth serving as the source of viral particles during speech. The computational mesh was generated using ANSYS Meshing software with 724,076 elements. Given the time-dependent nature of particle dispersion, a transient solver approach was implemented with time steps of 0.001 seconds. Simulation Methodology The Discrete Phase Model (DPM) was employed to track individual virus particles within the continuous airflow medium. Key simulation parameters included: Particle classification: Inert type Injection method: Surface-based emission from patient’s mouth Particle diameter: 1 micrometer (10⁻⁶ m) Particle temperature: 310K (body temperature) Emission duration: 0-20 seconds Velocity profile: Sinusoidal function with 0.33 m/s maximum velocity Flow rate: Proportionally linked to particle velocity The RNG k-epsilon turbulence model was utilized alongside the energy equation to accurately capture flow dynamics and temperature distribution within the domain. Results and Analysis Particle tracking visualizations were generated at various time intervals throughout the simulation period, with particles colored according to residence time and velocity. The simulation captured two distinct phases: Initial 20 seconds: Active emission of viral particles from the infected person’s mouth during speech Subsequent 20 seconds: Continued movement of suspended particles through the air space between individuals The results clearly demonstrated that without protective barriers, viral particles emitted during a 20-second conversation reached the healthy individual within 40 seconds of the interaction beginning. This confirms the high transmission risk during unprotected face-to-face conversations at distances below recommended social distancing guidelines. These findings support public health recommendations regarding both social distancing and the use of protective barriers during interpersonal interactions, particularly in indoor environments where airflow patterns may contribute to particle transmission.

This study presents a computational fluid dynamics simulation of color spray application on a wall surface using a conical injection method, analyzed through Ansys Fluent software. Model Development The three-dimensional computational domain was created using SpaceClaim software, consisting of a 3m × 3m × 4m rectangular space. The computational mesh was generated in Ansys Meshing with an unstructured grid configuration comprising 254,934 cells to ensure accurate resolution of the spray dynamics. Simulation Methodology A one-way Discrete Phase Model (DPM) approach was implemented to simulate the paint particles, with the following key parameters: Injection type: Conical pattern Particle velocity: 10 m/s Cone angle: 30 degrees Particle material: Color Spray Particle classification: Inert type The simulation was configured with several fundamental assumptions: Pressure-based solver implementation Unsteady (transient) time treatment Negligible gravitational effects Laminar flow regime Boundary Conditions and Solution Setup The boundary conditions were defined as: Primary wall and back wall: Stationary with “escape” condition for particles Top wall: Stationary with “trap” condition for particles The numerical solution utilized: SIMPLE pressure-velocity coupling Second-order pressure discretization Second-order upwind momentum discretization First-order upwind modified turbulent viscosity Standard initialization method Results and Analysis The simulation successfully captured the conical spray pattern of paint particles as they traveled from the injection source toward the target wall surface. The 30-degree cone angle effectively determined the spray coverage area on the wall, demonstrating how injection parameters directly influence the paint distribution pattern. This analysis provides valuable insights for industrial coating applications, allowing optimization of spray parameters for desired coverage characteristics and material efficiency in painting processes.

This study presents a computational fluid dynamics simulation examining the delivery and distribution of asthma medication spray within a simplified human lung model using ANSYS Fluent software. Model Development A three-dimensional lung model was created using SpaceClaim software, featuring a simplified representation of human pulmonary airways with an inlet diameter of 50cm. The computational mesh was generated using ANSYS Meshing software with 3,734,238 elements to ensure accurate resolution of both airflow patterns and particle trajectories throughout the complex airway geometry. Given the time-dependent nature of inhaler spray delivery and particle transport, a transient solver approach was implemented. Simulation Methodology The one-way Discrete Phase Model (DPM) was employed to track medication particles within the continuous airflow medium. Key simulation parameters included: Continuous phase: Air Discrete phase: Medication particles Air inlet velocity: 5 m/s Gravitational acceleration: -9.81 m/s² (along z-axis) Particle diameter: 100 microns Injection method: Surface velocity injection Turbulence model: Realizable k-epsilon The one-way coupling approach was selected as appropriate for this application, as the relatively low concentration of medication particles would have minimal impact on the overall airflow patterns, while the airflow significantly influences particle transport. Results and Analysis The simulation produced comprehensive visualization outputs including: Two-dimensional velocity contours Three-dimensional velocity fields Pressure distribution throughout the airways Particle tracking animations showing medication transport The results demonstrated how the airflow patterns within the bronchial tree significantly influence the distribution and deposition of medication particles. Areas of flow recirculation, velocity gradients, and geometric features (such as bifurcations) were shown to affect particle trajectories and potential deposition sites. This analysis provides valuable insights for pulmonary drug delivery optimization, potentially informing the design of inhalation devices and delivery protocols to maximize therapeutic efficacy for asthma patients by ensuring medication reaches intended target regions within the lungs.