Product:Particle Tracing Module
Product:Particle Tracing Module
Analyze the Behavior of Particles with the Particle Tracing Module
Extend the Functionality of the COMSOL Environment with Particle Tracing
The Particle Tracing Module extends the functionality of the COMSOL environment for computing the trajectory of particles in a fluid or electromagnetic field, including particle-particle, fluid-particle, and particle-field interactions. You can seamlessly combine any application-specific module with the Particle Tracing Module for computing the fields that drive particle motion. Particles can have mass or be mass-less. The movement is governed by either the Newtonian, Lagrangian, or Hamiltonian formulations from classical mechanics. Boundary conditions can be imposed on the particles on the walls of the geometry to allow particles to freeze, stick, bounce, disappear, or reflect diffusely. User-defined wall conditions may also be specified, where the post collision particle velocity is typically a function of the incoming particle velocity and the wall normal vector. Secondary particles released when an incoming particle strikes a wall can be included. The number of secondary particles and their velocity distribution function can be functions of the primary particle velocity and the wall geometry. Particles can also stick to the wall according to an arbitrary expression or a sticking probability. Additional dependent variables can be added to the model which allows you to compute quantities like particle mass, temperature, or spin.
Particles can be released on boundaries and domains uniformly, according to the underlying mesh, as defined by a grid or according to an arbitrary expression. A wide range of predefined forces is available to describe specifically how the particles interact with the fields. You can then add arbitrary forces as defined by a suitable expression. It is also possible to model the two-way interaction between the particles and the fields (particle-field interaction), as well as the interaction of particles between each other (particle-particle interaction).
Powerful Processing Tools
Powerful processing tools allow for sophisticated visualization of the computed particle trajectories. Particle trajectories can be represented by points, comet tails, arrows, lines, tubes, or ribbons. Animations can easily be created and viewed directly in the graphical user interface (GUI) or exported to file. The particle trajectories can be colored with arbitrary expressions that can depend on the particles, the fields, or any combination of the two. In cases where the trajectory of many particles are simulated, it is possible to filter out specific particle trajectories according to a logical expression. The group behavior of the particles can be projected onto a lower dimension and visualized using Poincaré maps or phase portraits. It is also possible to perform operations on the particles to compute and plot the maximum, minimum, average, or integral of some quantity over all the particles. The particle trajectory data itself can be evaluated and written to the Results table or exported to a file. You can conveniently visualize the velocity and energy distributions of the particles, using 1D or 2D histograms.
Charged Particles in Electric and Magnetic Fields
Charged particles, such as electrons, individual ions, or small ion clusters, are affected by three primary forces in electric and magnetic fields:
- The electric force, which arises either due to a gradient in the electric potential or due to a time-varying magnetic vector potential. Particles with negative charge move in the opposite direction to the electric field, and particles with positive charge move in the same direction as the electric field. The electric force does work on these particles.
- The magnetic force, which does no work on the charged particles but can significantly alter their trajectory. The magnetic force often results in “banana” orbits for charged particles, causing them to orbit around magnetic field lines with a distance proportional to their mass.
- Collisional forces, which occur when charged particles collide with a background gas. The higher the background pressure, the more important the collisional forces.
If the number density of charged species is less than around 1013 1/m3, the effect of the particles on the fields can be neglected. This allows you to compute the fields independently from the particle trajectories. The fields are then used to compute the electric, magnetic, and collisional forces on the particles. The fact that the particle trajectories can be computed in their own study allows efficient and computationally inexpensive iterative solvers to be used.
Solving for Particle Tracing
For each particle, an ordinary differential equation is solved for each component of the position vector. This means that three ordinary differential equations are solved for each particle in 3D, and two in 2D. At each time step, the forces acting on each particle are queried from the computed fields at the current particle position. If particle-particle interaction forces are included in the model, they are added to the total force. The particle position is then updated, and the process repeats until the specified end time for the simulation is reached. Since the Particle Tracing Module uses a very general formulation for computing particle trajectories, the Particle Tracing interfaces can be used to model charged particle motion in electromagnetic fields, large scale planetary and galactic movement, and particle motion in laminar, turbulent, and two-phase fluid systems.
Studying Particle Tracking in a Fluid
The motion of microscopic- and macroscopic-sized particles is typically dominated by the drag force acting on particles immersed in a fluid. There are two phases in the system: a discrete phase consisting of bubbles, particles, or droplets, and a continuous phase in which the particles are immersed. In order for the particle tracking approach to be valid, the system should be a dilute or dispersed flow. This means that the volume fraction of the discrete phase should be much smaller than the volume fraction of the continuous phase (generally less than 1%). When the volume fraction of the particles is not small, the fluid system is categorized as a dense flow and you are required to take a different modeling approach. It is important to realize that, with the particle tracking approach, particles do not displace the fluid they occupy.
In a sparse flow, the continuous phase affects the motion of the particles, but not vice versa. This is often referred to as “one-way coupling”. When modeling such a system, it is usually most efficient to solve for the continuous phase first, then to compute the trajectories of the dispersed phases.
In a dilute flow, the continuous phase affects the motion of the particles, and the particle motion in turn disrupts the continuous phase. This is often referred to as “two-way coupling”. In order to model this effect, you must compute the continuous phase and disperse phase simultaneously. Thus, the computational demand is significantly higher when modeling dilute flows than when modeling sparse flows.
Particle Tracing Module
- Charged Particle Tracing interface to model ion and electron trajectories in electric and magnetic fields including elastic collisions with a background gas
- Particle Tracing for Fluid Flow interface to model the motion of microscopic and macroscopic particles in a fluid
- Mathematical Particle Tracing interface, which offers complete freedom over the equations solved
- Massless, Newtonian, Lagrangian, and Hamiltonian formulations
- Predefined forces to facilitate model set-up
- User-defined forces
- Fictitious forces for rotating frames
- Particle-field interactions
- Particle-particle interactions
- Reinitialization of the particle velocity vector based on some logical expression allows for general purpose Monte Carlo modeling
- Particle release mechanisms
- Mesh-based where a specific number of particles are released in each mesh element
- Uniform distribution of particles on a given boundary
- Expression based which allows the density of particles to be greater in specific locations
- Thermionic emission of electrons
- Particle trajectory plots (lines, tubes, points and comet tails)
- Color trajectories with arbitrary expressions
- Filter particles to plot
- Poincaré sections and maps
- Phase portraits
- Compute maximum, minimum, average and integrals over all particles
- Write particle data to tables
- Export particle data
- 1D and 2D histograms
- Transmission probabilities
- Mass spectrometry
- Beam physics
- Brownian motion
- Ion optics
- Ion mobility spectrometry
- Fluid flow visualization
- Aerosol dynamics
- Secondary emission
- Separation and filtration
- Ion energy distribution function visualization
- Classical mechanics
Modeling of Laminar Flow Static Mixers
Nagi Elabbasi, Xiaohu Liu, & Stuart Brown Veryst Engineering LLC, Needham, MA, USA Mike Vidal & Matthew Pappalardo Nordson EFD, East Providence, RI, USA
Veryst Engineering, a company that provides consulting in engineering design and product manufacturing, has collaborated with Nordson EFD, one of the leading manufacturers of precision dispensing systems, to optimize their static mixers. Static mixers are inexpensive and efficient mechanisms for mixing laminar viscous fluids, where molecular ...
A Smooth Optical Surface in Minutes
Anthony Beaucamp Zeeko Ltd Leicestershire, UK
Zeeko Ltd is a technology company that manufactures corrective polishing machines for optics and other surfaces. They are presently researching fluid jet polishing (FJP), which pumps a mixture of water and abrasive particles through a nozzle onto a work piece. Unfortunately, FJP tends to introduce waveforms on to the polished surfaces, which in ...
Modeling Inertial Focusing in Straight and Curved Microfluidic Channels
J. Martel and M. Toner Biomems Resource Center Massachusetts General Hospital USA N. Elabbasi, D. Quinn, and J. Bergstrom Veryst Engineering USA
In many medical procedures and tests, it is necessary to isolate cells of interest for further analysis. Microfluidics has revolutionized the way in which these tests are conducted enabling, for example, the ability to detect cancer from a blood sample. One of the most promising microfluidic techniques to separate and concentrate cells is called ...
Floating on Sound Waves with Acoustic Levitation
K. Suthar, C. Benmore Argonne, IL, USA
As part of an industry that saves lives on a regular basis, pharmaceutical companies are in need of systems that can be used to deliver high-quality medicines. One method utilizes sound waves generated by an acoustic levitator with foam-coated transducers. Using the acoustic force from the sound waves, chemical particles are suspended between the ...
Optimizing Hematology Analysis: When Physical Prototypes Fail, Simulation Provides the Answers
D. Isèbe HORIBA Medical, France
Hematology analysis, the analysis of a blood sample to determine a variety of hematological parameters, is a major factor in diagnostic and treatment decisions for blood diseases. Accurate blood analysis requires counting and sorting different cells in a sample to measure their sizes and distributions. HORIBA Medical, a company that supplies ...
Dielectrophoretic Separation of Platelets from Red Blood Cells
Dielectrophoresis (DEP) occurs when a force is exerted on a dielectric particle as it is subjected to a nonuniform electric field. DEP has many applications in the field of biomedical devices used for biosensors, diagnostics, particle manipulation and filtration (sorting), particle assembly, and more. The DEP force is sensitive to the size, ...
Particle Trajectories in a Laminar Static Mixer
In static mixers, also called motionless or in-line mixers, a fluid is pumped through a pipe containing stationary blades. This mixing technique is particularly well suited for laminar flow mixing because it generates only small pressure losses in this flow regime. This example studies the flow in a twisted-blade static mixer. It evaluates the ...
Molecular Flow Through an RF Coupler
This model computes the transmission probability through an RF coupler using both the angular coefficient method available in the Free Molecular Flow interface and a Monte Carlo method using the Mathematical Particle Tracing interface. The computed transmission probability determined by the two methods is in excellent agreement with less than a ...
Laminar Static Particle Mixer Designer
In static mixers, a fluid is pumped through a pipe containing stationary mixing blades. This mixing technique is well suited for laminar flow mixing, because it generates only small pressure losses in this flow regime. When a fluid is pumped through the channel, the alternating directions of the cross-sectional blades mix the fluid as it passes ...
Charge Exchange Cell Simulator
A charge exchange cell consists of a region of gas at an elevated pressure within a vacuum chamber. When an ion beam interacts with the higher-density gas, the ions undergo charge exchange reactions with the gas, creating energetic neutral particles. It is likely that only a fraction of the beam ions will undergo charge exchange reactions. ...
An Einzel lens is an electrostatic device used for focusing charged particle beams. It may be found in cathode ray tubes, ion beam and electron beam experiments, and ion propulsion systems. This particular model consists of three axially aligned cylinders. The outer cylinders are grounded, while the cylinder in the middle is held at a fixed ...
Electron Beam Divergence Due to Self Potential
When modeling the propagation of charged particle beams at high currents, the space charge force generated by the beam significantly affects the trajectories of the charged particles. Perturbations to these trajectories, in turn, affect the space charge distribution. The Charged Particle Tracing interface can use an iterative procedure to ...
Ion Cyclotron Motion
This model computes the trajectory of an ion in a uniform magnetic field using the Newtonian, Lagrangian and Hamiltonian formulations available in the Mathematical Particle Tracing interface.
Transport which is purely diffusive in nature can be modeled using a Brownian force. This model shows how to add such a force in the Particle Tracing for Fluid Flow physics interface. Particle diffusion in a fluid is modeled with the diffusion equation and the Particle Tracing for Fluid flow interfaces and the results are compared.
This model demonstrates the use of optical tracing for studying optically large gradient-index structures with anisotropic optical properties. Additionally, the model introduces a smoothing technique for handling discontinuities of refractive index on curved surfaces, which are typical in conventional optical devices such as lenses.
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