Sum of expression `expr` over active, stuck, and frozen particles
This allows you to state the level of accuracy you want, without having to expend computational resources to complete a fixed number of iterations after this criterion is satisfied. For example, when modeling bidirectionally coupled particle-field interactions, you can terminate the study when the relative error in the electron or ion current is sufficiently small. The weights for the space charge density in each iteration of the Bidirectionally Coupled Particle Tracing study can be uniform, an arithmetic sequence (shown above), or a geometric sequence.Ĭonvergence-Based Termination Criteria for Bidirectionally Coupled Modelsįor models that use the Bidirectionally Coupled Particle Tracing study step to iterate between stationary and time-dependent solutions, it is now possible to terminate the solver loop based on a convergence criterion instead of a fixed number of iterations. This can lead to faster convergence of bidirectionally coupled models in which the electric field and the trajectories of charged particles strongly influence each other. There are built-in options to make these weights remain constant (the default) or increase them in an arithmetic or geometric sequence. When using the Bidirectionally Coupled Particle Tracing study step to model electric particle-field interactions, it is now possible to assign different weights to the space charge density computed during different iterations of the solver loop. In each rectangle, the source boundary is on the side of the lighter-colored mesh.Īlternative Way to Assign Weights in Bidirectionally Coupled Space Charge Models Mesh-based release of particles from the source boundary (left), the destination boundary (middle), or both source and destination (right).
The specialized Wall induced formulation is available for neutrally buoyant particles in channels. The Saffman formulation for lift force is applicable to inertial particles in a shear flow that are an appreciable distance away from boundaries. Two different formulations of the lift force are available: Saffman and Wall induced. The drag force acts in parallel with the fluid velocity with respect to the particle, whereas the lift force typically acts normal to it. Lift forces are relevant when particles move in a nonuniform fluid velocity field. The distribution of particle velocity directions has been left unchanged it is an isotropic circle for both releases.Ī dedicated Lift Force feature is now available for the Particle Tracing for Fluid Flow interface. Particles with uniform speed (left) or a pseudorandom distribution of different speeds (right). This latter additional feature is activated in the Advanced Settings section by selecting the Subtract moving frame velocity from initial particle velocity check box. Particle tracing in rotating frames allows for easier modeling of particle motion in rotating machinery, such as mixers and turbomolecular pumps, because the particle trajectories can be computed in a frame of reference that is attached to the moving geometry.īy adding this feature to a model, release-based features will include an option to specify whether the initial particle velocity is defined with respect to the rotating frame or with respect to the inertial (nonrotating) frame. When you specify the center of rotation, direction of rotation, and angular velocity magnitude of the frame, the centrifugal, Coriolis, and Euler forces that are exerted on the particles are automatically applied. The Rotating Frame feature in particle tracing is now available for rotating frames of reference. Thanks in advance for any suggestions.Particles, colored by their unique particle index, traveling through a domain with sector symmetry. When I run into memory barriers, I usually switch to GMRES and the SSOR Vector preconditioner, which is usually stable, but is also pretty slow. Do you expect that this should be benefitting from the improvements you mentioned, in Comsol version 5.4? Would you possibly now recommend a different approach in regard to solver (or other) choices, in regard to optimizing speed of computation and efficient use of memory? I know the iterative solvers use less memory, but they also tend to be less stable (especially BiCGSTab) for the kinds of problems I do. I tend to run a lot of large 3D RF models and typically use the PARDISO solver with linear discretization.
#Comsol 5.3 stokes windows 10
But then again, I was already seeing pretty good utilization, or so I thought, of my 36 cores (18 per CPU) on a Dell T7910 Windows 10 based, 64-bit workstation with 256 GB of RAM.
Can you comment on ways to best take advantage of this memory allocator? I'm not complaining, but I don't think I'm seeing anywhere near the "7X faster" improvement that BP noted above.
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