OpenMC Guide

❮ Lattices & UniversesSource Definition ❯

Settings Parameters in OpenMC

Understanding Settings

Now that we understand materials and geometry, let's learn how to control our simulations using OpenMC's Settings class.

What Settings Control

  • Number of particles
  • Type of calculation
  • Source definition
  • Physics options

Common Uses

  • Reactor criticality
  • Shielding analysis
  • Dose calculations
  • Source problems

Basic Settings

Let's start with the most common settings you'll need:

Essential Settings

python
# Create settings
settings = openmc.Settings()

# Basic parameters
settings.batches = 100     # Total batches
settings.inactive = 20     # Initial batches to skip
settings.particles = 10000 # Particles per batch

# Set calculation type
settings.run_mode = 'eigenvalue'  # For reactors
# settings.run_mode = 'fixed source'  # For shielding

Tip: Start with fewer particles/batches for testing, then increase for production runs. More particles = better statistics but longer runtime.

Source Definition

Next, let's define where particles start in our model:

Reactor Source

python
# Source in fuel regions
lower_left = (-10, -10, -10)
upper_right = (10, 10, 10)
source = openmc.Source(
    space=openmc.stats.Box(lower_left, upper_right)
)
settings.source = source

External Source

python
# Point source for shielding
source = openmc.Source(
    space=openmc.stats.Point((0,0,-50)),
    energy=openmc.stats.Discrete([14e6], [1.0])
)
settings.source = source

Advanced Options

Physics Options

python
# Energy settings
settings.cutoff = {
    'energy': 1.0e-11  # 0.01 eV cutoff
}

# Temperature handling
settings.temperature = {
    'method': 'nearest'
}

Convergence

python
# Run until error < 0.1%
trigger = openmc.Trigger('std_dev', 0.001)
settings.trigger_active = True
settings.trigger_max_batches = 500

Best Practices

Tips for Good Results

  • Use enough inactive batches (20-50) for source convergence
  • Check convergence with more particles before production
  • Save settings to XML with settings.export_to_xml()
  • Use triggers for precise error control
  • Match source to your problem type

Additional Resources

Related Topics

❮ Lattices & UniversesSource Definition ❯