This program was contributed by Bruno Blais (Polytechnique Montréal), Toni El Geitani Nehme (Polytechnique Montréal), Rene Gassmöller (University of California Davis), and Peter Munch (Technical University of Munich and Helmholtz-Zentrum Geesthacht). Bruno Blais was supported by NSERC Discovery grant RGPIN-2020-04510, by Compute Canada and Calcul Québec.
Introduction
Simulation of the motion of massless tracer particles in a vortical flow
Particles play an important part in numerical models for a large number of applications. Particles are routinely used as massless tracers to visualize the dynamic of a transient flow. They can also play an intrinsic role as part of a more complex finite element model, as is the case for the Particle-In-Cell (PIC) method [93] or they can even be used to simulate the motion of granular matter, as in the Discrete Element Method (DEM) [33]. In the case of DEM, the resulting model is not related to the finite element method anymore, but just leads to a system of ordinary differential equation which describes the motion of the particles and the dynamic of their collisions. All of these models can be built using deal.II's particle handling capabilities.
In the present step, we use particles as massless tracers to illustrate the dynamic of a vortical flow. Since the particles are massless tracers, the position of each particle \(i\) is described by the following ordinary differential equation (ODE):
where \(\textbf{x}_i\) is the position of particle \(i\) and \(\textbf{u}(\textbf{x}_i)\) the flow velocity at its position. In the present step, this ODE is solved using the explicit Euler method. The resulting scheme is:
\[
\textbf{x}_{i}^{n+1} = \textbf{x}_{i}^{n} + \Delta t \; \textbf{u}(\textbf{x}_{i}^{n})
\]
where \(\textbf{x}_{i}^{n+1}\) and \(\textbf{x}_{i}^{n}\) are the position of particle \(i\) at time \(t+\Delta t\) and \(t\), respectively and where \(\Delta t\) is the time step. In the present step, the velocity at the location of particles is obtained in two different fashions:
By evaluating the velocity function at the location of the particles;
By evaluating the velocity function on a background triangulation and, using a finite element support, interpolating at the position of the particle.
The first approach is not practical, since the velocity profile is generally not known analytically. The second approach, based on interpolating a solution at the position of the particles, mimics exactly what would be done in a realistic computational fluid dynamic simulation, and this follows the way we have also evaluated the finite element solution at particle locations in step-19. In this step, we illustrate both strategies.
We note that much greater accuracy could be obtained by using a fourth order Runge-Kutta method or another appropriate scheme for the time integration of the motion of the particles. Implementing a more advanced time-integration scheme would be a straightforward extension of this step.
In deal.II, Particles::Particle are very simple and flexible entities that can be used to build PIC, DEM or any type of particle-based models. Particles have a location in real space, a location in the reference space of the element in which they are located and a unique ID. In the majority of cases, simulations that include particles require a significant number of them. Thus, it becomes interesting to handle all particles through an entity which agglomerates all particles. In deal.II, this is achieved through the use of the Particles::ParticleHandler class.
By default, particles do not have a diameter, a mass or any other physical properties which we would generally expect of physical particles. However, through a ParticleHandler, particles have access to a Particles::PropertyPool. This PropertyPool is an array which can be used to store an arbitrary number of properties associated with the particles. Consequently, users can build their own particle solver and attribute the desired properties to the particles (e.g., mass, charge, diameter, temperature, etc.). In the present tutorial, this is used to store the value of the fluid velocity and the process id to which the particles belong.
Challenges related to distributed particle simulations
Although the present step is not computationally intensive, simulations that include many particles can be computationally demanding and require parallelization. The present step showcases the distributed parallel capabilities of deal.II for particles. In general, there are three main challenges that specifically arise in parallel distributed simulations that include particles:
Generating the particles on the distributed triangulation;
Exchanging the particles that leave local domains between the processors;
Load balancing the simulation so that every processor has a similar computational load. These challenges and their solution in deal.II have been discussed in more detail in [93], but we will summarize them below.
There are of course also questions on simply setting up a code that uses particles. These have largely already been addressed in step-19. Some more advanced techniques will also be discussed in step-70.
Parallel particle generation
Generating distributed particles in a scalable way is not straightforward since the processor to which they belong must first be identified before the cell in which they are located is found. deal.II provides numerous capabilities to generate particles through the Particles::Generator namespace. Some of these particle generators create particles only on the locally owned subdomain. For example, Particles::Generators::regular_reference_locations() creates particles at the same reference locations within each cell of the local subdomain and Particles::Generators::probabilistic_locations() uses a globally defined probability density function to determine how many and where to generate particles locally.
In other situations, such as the present step, particles must be generated at specific locations on cells that may be owned only by a subset of the processors. In most of these situations, the insertion of the particles is done for a very limited number of time-steps and, consequently, does not constitute a large portion of the computational cost. For these occasions, deal.II provides convenient Particles::Generators that can globally insert the particles even if the particle is not located in a cell owned by the parallel process on which the call to create the particle is initiated. The generators first locate on which subdomain the particles are situated, identify in which cell they are located and exchange the necessary information among the processors to ensure that the particle is generated with the right properties. Consequently, this type of particle generation can be communication intensive. The Particles::Generators::dof_support_points and the Particles::Generators::quadrature_points generate particles using a triangulation and the points of an associated DoFHandler or quadrature respectively. The triangulation that is used to generate the particles can be the same triangulation that is used for the background mesh, in which case these functions are very similar to the Particles::Generators::regular_reference_locations() function described in the previous paragraph. However, the triangulation used to generate particles can also be different (non-matching) from the triangulation of the background grid, which is useful to generate particles in particular shapes (as in this example), or to transfer information between two different computational grids (as in step-70). Furthermore, the Particles::ParticleHandler class provides the Particles::ParticleHandler::insert_global_particles() function which enables the global insertion of particles from a vector of arbitrary points and a global vector of bounding boxes. In the present step, we use the Particles::Generators::quadrature_points() function on a non-matching triangulation to insert particles located at positions in the shape of a disk.
Particle exchange
As particles move around in parallel distributed computations they may leave the locally owned subdomain and need to be transferred to their new owner processes. This situation can arise in two very different ways: First, if the previous owning process knows the new owner of the particles that were lost (for example because the particles moved from the locally owned cell of one processor into an adjacent ghost cells of a distributed triangulation) then the transfer can be handled efficiently as a point-to-point communication between each process and the new owners. This transfer happens automatically whenever particles are sorted into their new cells. Secondly, the previous owner may not know to which process the particle has moved. In this case the particle is discarded by default, as a global search for the owner can be expensive. step-19 shows how such a discarded particle can still be collected, interpreted, and potentially reinserted by the user. In the present example we prevent the second case by imposing a CFL criterion on the timestep to ensure particles will at most move into the ghost layer of the local process and can therefore be send to neighboring processes automatically.
Balancing mesh and particle load
The last challenge that arises in parallel distributed computations using particles is to balance the computational load between work that is done on the grid, for example solving the finite-element problem, and the work that is done on the particles, for example advecting the particles or computing the forces between particles or between particles and grid. By default, for example in step-40, deal.II distributes the background mesh as evenly as possible between the available processes, that is it balances the number of cells on each process. However, if some cells own many more particles than other cells, or if the particles of one cell are much more computationally expensive than the particles in other cells, then this problem no longer scales efficiently (for a discussion of what we consider "scalable" programs, see this glossary entry). Thus, we have to apply a form of "load balancing", which means we estimate the computational load that is associated with each cell and its particles. Repartitioning the mesh then accounts for this combined computational load instead of the simplified assumption of the number of cells [93].
In this section we only discussed the particle-specific challenges in distributed computation. Parallel challenges that particles share with finite-element solutions (parallel output, data transfer during mesh refinement) can be addressed with the solutions found for finite-element problems already discussed in other examples.
The testcase
In the present step, we use particles as massless tracers to illustrate the dynamics of a particular vortical flow: the Rayleigh–Kothe vortex. This flow pattern is generally used as a complex test case for interface tracking methods (e.g., volume-of-fluid and level set approaches) since it leads to strong rotation and elongation of the fluid [32].
The stream function \(\Psi\) of this Rayleigh-Kothe vortex is defined as:
The velocity profile is illustrated in the following animation:
It can be seen that this velocity reverses periodically due to the term \(\cos \left( \pi \frac{t}{T} \right)\) and that material will end up at its starting position after every period of length \(t=2T\). We will run this tutorial program for exactly one period and compare the final particle location to the initial location to illustrate this flow property. This example uses the testcase to produce two models that handle the particles slightly differently. The first model prescribes the exact analytical velocity solution as the velocity for each particle. Therefore in this model there is no error in the assigned velocity to the particles, and any deviation of particle positions from the analytical position at a given time results from the error in solving the equation of motion for the particle inexactly, using a time stepping method. In the second model the analytical velocity field is first interpolated to a finite-element vector space (to simulate the case that the velocity was obtained from solving a finite-element problem, in the same way as the ODE for each particle in step-19 depends on a finite element solution). This finite-element "solution" is then evaluated at the locations of the particles to solve their equation of motion. The difference between the two cases allows to assess whether the chosen finite-element space is sufficiently accurate to advect the particles with the optimal convergence rate of the chosen particle advection scheme, a question that is important in practice to determine the accuracy of the combined algorithm (see e.g. [94]).
From the following include file we import the ParticleHandler class that allows you to manage a collection of particles (objects of type Particles::Particle), representing a collection of points with some attached properties (e.g., an id) floating on a parallel::distributed::Triangulation. The methods and classes in the namespace Particles allows one to easily implement Particle-In-Cell methods and particle tracing on distributed triangulations:
#include <deal.II/particles/particle_handler.h>
We import the particles generator which allow us to insert the particles. In the present step, the particle are globally inserted using a non-matching hyper-shell triangulation:
#include <deal.II/particles/generators.h>
Since the particles do not form a triangulation, they have their own specific DataOut class which will enable us to write them to commonly used parallel vtu format (or any number of other file formats):
Similarly to what is done in step-60, we set up a class that holds all the parameters of our problem and derive it from the ParameterAcceptor class to simplify the management and creation of parameter files.
The ParameterAcceptor paradigm requires all parameters to be writable by the ParameterAcceptor methods. In order to avoid bugs that would be very difficult to track down (such as writing things like if (time = 0) instead of if(time == 0)), we declare all the parameters in an external class, which is initialized before the actual ParticleTracking class, and pass it to the main class as a const reference.
The constructor of the class is responsible for the connection between the members of this class and the corresponding entries in the ParameterHandler. Thanks to the use of the ParameterHandler::add_parameter() method, this connection is trivial, but requires all members of this class to be writable.
This class consists largely of member variables that describe the details of the particle tracking simulation and its discretization. The following parameters are about where output should written to, the spatial discretization of the velocity (the default is \(Q_1\)), the time step and the output interval (how many time steps should elapse before we generate graphical output again):
std::string output_directory = "./";
unsignedint velocity_degree = 1;
double time_step = 0.002;
double final_time = 4.0;
unsignedint output_interval = 10;
unsignedint repartition_interval = 5;
We allow every grid to be refined independently. In this tutorial, no physics is resolved on the fluid grid, and its velocity is calculated analytically.
unsignedint fluid_refinement = 4;
unsignedint particle_insertion_refinement = 3;
};
There remains the task of declaring what run-time parameters we can accept in input files. Since we have a very limited number of parameters, all parameters are declared in the same section.
This function is responsible for the initial generation of the particles on top of the background grid.
void generate_particles();
When the velocity profile is interpolated to the position of the particles, it must first be stored using degrees of freedom. Consequently, as is the case for other parallel case (e.g. step-40) we initialize the degrees of freedom on the background grid.
void setup_background_dofs();
In one of the test cases, the function is mapped to the background grid and a finite element interpolation is used to calculate the velocity at the particle location. This function calculates the value of the function at the support point of the triangulation.
void interpolate_function_to_field();
The next two functions are responsible for carrying out step of explicit Euler time integration for the cases where the velocity field is interpolated at the positions of the particles or calculated analytically, respectively.
void euler_step_interpolated(constdouble dt);
void euler_step_analytical(constdouble dt);
The cell_weight() function indicates to the triangulation how much computational work is expected to happen on this cell, and consequently how the domain needs to be partitioned so that every MPI rank receives a roughly equal amount of work (potentially not an equal number of cells). While the function is called from the outside, it is connected to the corresponding signal from inside this class, therefore it can be private.
The following two functions are responsible for outputting the simulation results for the particles and for the velocity profile on the background mesh, respectively.
void output_particles(constunsignedint it);
void output_background(constunsignedint it);
The private members of this class are similar to other parallel deal.II examples. The parameters are stored as a const member. It is important to note that we keep the Vortex class as a member since its time must be modified as the simulation proceeds.
The constructors and destructors are rather trivial. They are very similar to what is done in step-40. We set the processors we want to work on to all machines available (MPI_COMM_WORLD) and initialize the pcout variable to only allow processor zero to output anything to the standard output.
This function is the key component that allow us to dynamically balance the computational load for this example. The function attributes a weight to every cell that represents the computational work on this cell. Here the majority of work is expected to happen on the particles, therefore the return value of this function (representing "work for this cell") is calculated based on the number of particles in the current cell. The function is connected to the weight signal inside the triangulation, and will be called once per cell, whenever the triangulation repartitions the domain between ranks (the connection is created inside the generate_particles() function of this class).
First, we introduce a base weight that will be assigned to every cell.
constunsignedint base_weight = 1;
The following variable then determines how important particle work is compared to cell work. We set the weight per particle much higher to indicate that the particle load is the only one that is important to distribute the cells in this example. The optimal value of this number depends on the application and can range from 0 (cheap particle operations, expensive cell operations) to much larger than the base weight of 1 (expensive particle operations, cheap cell operations, like presumed in this example).
constunsignedint particle_weight = 10;
This example does not use adaptive refinement, therefore every cell should have the status CellStatus::cell_will_persist. However this function can also be used to distribute load during refinement, therefore we consider refined or coarsened cells as well.
In order to consider the particles when repartitioning the triangulation the algorithm needs to know how much weight to assign to each cell (how many particles are in there).
We attach a weight function to the signal inside parallel::distributed::Triangulation. This signal will be called every time the repartition() function is called. This connection only needs to be created once, so we might as well have set it up in the constructor of this class, but for the purpose of this example we want to group the particle related instructions.
We create a particle triangulation which is solely used to generate the points which will be used to insert the particles. This triangulation is a hyper shell which is offset from the center of the simulation domain. This will be used to generate a disk filled with particles which will allow an easy monitoring of the motion due to the vortex.
We generate the necessary bounding boxes for the particles generator. These bounding boxes are required to quickly identify in which process's subdomain the inserted particle lies, and which cell owns it.
This function sets up the background degrees of freedom used for the velocity interpolation and allocates the field vector where the entire solution of the velocity field is stored.
This function takes care of interpolating the vortex velocity field to the field vector. This is achieved rather easily by using the VectorTools::interpolate() function.
We store the processor id (a scalar) and the particle velocity (a vector) in the particle properties. In this example, this is done purely for visualization purposes.
In contrast to the previous function in this function we integrate the particle trajectories by interpolating the value of the velocity field at the degrees of freedom to the position of the particles. This is achieved using the FEPointEvaluation object.
We loop over all the local particles. Although this could be achieved directly by looping over all the cells, this would force us to loop over numerous cells which do not contain particles. Rather, we loop over all the particles, but, we get the reference of the cell in which the particle lies and then loop over all particles within that cell. This enables us to gather the values of the velocity out of the velocity_field vector once and use them for all particles that lie within the cell.
Next, compute the velocity at the particle locations by evaluating the finite element solution at the position of the particles. This is achieved using FEPointEvaluation object.
The next two functions take care of writing both the particles and the background mesh to vtu with a pvtu record. This ensures that the simulation results can be visualized when the simulation is launched in parallel.
This function orchestrates the entire simulation. It is very similar to the other time dependent tutorial programs – take step-21 or step-26 as an example. Note that we use the DiscreteTime class to monitor the time, the time-step and the step-number. This function is relatively straightforward.
We set the initial property of the particles by doing an explicit Euler iteration with a time-step of 0 both in the case of the analytical and the interpolated approach.
After the particles have been moved, it is necessary to identify in which cell they now reside. This is achieved by calling sort_particles_into_subdomains_and_cells
The remainder of the code, the main() function, is standard. We note that we run the particle tracking with the analytical velocity and the interpolated velocity and produce both results
The directory in which this program is run contains an example parameter file by default. If you do not specify a parameter file as an argument on the command line, the program will try to read the file "parameters.prm" by default, and will execute the code.
On any number of cores, the simulation output will look like:
bash@f$ mpirun -np 4 ./step-68 parameters.prm
Number of particles inserted: 606
Repartitioning triangulation after particle generation
We note that, by default, the simulation runs the particle tracking with an analytical velocity for 2000 iterations, then restarts from the beginning and runs the particle tracking with velocity interpolation for the same duration. The results are written every 10th iteration.
Motion of the particles
The following animation displays the trajectory of the particles as they are advected by the flow field. We see that after the complete duration of the flow, the particle go back to their initial configuration as is expected.
Dynamic load balancing
The following animation shows the impact of dynamic load balancing. We clearly see that the subdomains adapt themselves to balance the number of particles per subdomain. However, a perfect load balancing is not reached, in part due to the coarseness of the background mesh.
Possibilities for extensions
This program highlights some of the main capabilities for handling particles in deal.II, notably their capacity to be used in distributed parallel simulations. However, this step could be extended in numerous manners:
High-order time integration (for example using a Runge-Kutta 4 method) could be used to increase the accuracy or allow for larger time-step sizes with the same accuracy.
The full equation of motion (with inertia) could be solved for the particles. In this case the particles would need to have additional properties such as their mass, as in step-19, and if one wanted to also consider interactions with the fluid, their diameter.
Coupling to a flow solver. This step could be straightforwardly coupled to any parallel program in which the Stokes (step-32, step-70) or the Navier-Stokes equations are solved (e.g., step-57).
Computing the difference in final particle positions between the two models would allow to quantify the influence of the interpolation error on particle motion.
1.11.0