Description

This PR introduces a "virtual lattice" method, an acceleration technique designed to improve the performance and reduce the memory footprint of MC simulations for systems containing a large number of randomly dispersed particles.

Background

Simulating explicit, randomly distributed particles is computationally prohibitive due to the geometric complexity. Locating a neutron among hundreds of thousands of spheres is a major performance bottleneck.
The existing create_triso_lattice function implements a "physical lattice" approach. While this provides acceleration, it has several drawbacks:

• It modifies the model geometry by creating a large number of cuboid cells.
• This leads to a massive increase in the number of surfaces and cells, significantly inflating memory usage and input file size.
• It complicates post-processing, as tallies on particles (which are split by the lattice) must be manually recombined.
• The complex geometry can negatively impact parallel efficiency at large scales.

Implementation

The virtual lattice method decouples the acceleration structure from the actual CSG model. Instead of physically dividing the geometry, it works as follows:

• Overlay Mesh: A regular, hypothetical mesh is overlaid on the region containing the dispersed particles. This mesh exists only as an in-memory data structure and does not create new surfaces or cells in the OpenMC geometry.
• Pre-computation Map: During initialization, a map is built that associates each virtual lattice element with a list of the particles (e.g., TRISO spheres) that intersect it.
• Accelerated Search: During particle transport, the code first identifies which virtual lattice element the particle is in. It then only needs to test for intersections with the small subset of spheres associated with that element, dramatically reducing the search space for:
  • Finding the cell a particle is in.
  • Calculating the distance to the nearest surface.
  • Processing surface crossings.
    This approach retains the acceleration benefits of a lattice structure while avoiding the overhead of a physical one.

As demonstrated in the associated paper, the virtual lattice method provides substantial advantages over the physical lattice method.

• Reduced Time Consumption: Faster transport calculations due to a simpler geometry and more efficient search algorithm (up to ~36% reduction in simulation time for the HTR-PM model).
• Massively Lower Memory Footprint: The geometry is a single universe filled with particles, leading to dramatic reductions in memory (e.g., from >192 GB to <1 GB for the HTR-PM model).
• Improved Parallel Efficiency: Better load balancing leads to significantly higher parallel efficiency at large scales (e.g., 83% vs 56% weak scaling efficiency).
• Simplified Workflow: Tallying on whole particles is straightforward, as they are no longer geometrically split. This also greatly simplifies specifying burnable materials for large-scale depletion.

Usage

The feature is exposed to the user via a simple boolean flag in the Python API. To use the new method, a user simply sets virtual=True:

# Old physical lattice
lattice = openmc.model.create_triso_lattice(...)

# New virtual lattice
lattice = openmc.model.create_triso_lattice(..., virtual=True)

This implementation is based on the methodology and results presented in the following paper. For a detailed theoretical background, performance analysis, and validation, please refer to:
Liang, J., Li, R., & Liu, Z. (2024). Virtual lattice method for efficient Monte Carlo transport simulation of dispersion nuclear fuels. Computer Physics Communications, 295, 108985. [https://doi.org/10.1016/j.cpc.2023.108985]

Checklist

• I have performed a self-review of my own code
• I have run clang-format (version 15) on any C++ source files (if applicable)
• I have followed the style guidelines for Python source files (if applicable)
• I have made corresponding changes to the documentation (if applicable)
• I have added tests that prove my fix is effective or that my feature works (if applicable)