By name: radiation-transport-simulation description: "Workflow guidance for Monte Carlo and deterministic radiation transport simulations (EM and particle transport), from geometry setup to validation and uncertainty reporting."
Radiation transport simulation
Scope
Use this skill when work involves numerical simulation of radiation or EM-related transport phenomena, including Monte Carlo particle transport and coupled source-term studies.
Typical tools and ecosystems:
- OpenMC
- MCNP
- Geant4
- FLUKA
- Serpent
- PHITS
- Deterministic solvers used for cross-checks (when available)
Use for
- Problem setup for radiation transport studies (source, geometry, materials, tallies)
- EM or radiation dose/deposition studies where transport assumptions matter
- Uncertainty quantification for Monte Carlo outputs
- Variance reduction strategy selection and documentation
- Convergence diagnostics and statistical quality checks
- Benchmarking against reference problems or published validation cases
- Reproducible simulation workflows (inputs, seeds, versions, metadata)
Do not use for
- Pure symbolic particle-physics matrix-element generation (use HEP workflow skills)
- Generic cloud deployment unless transport workflows are the core focus
- Regulatory or clinical claims without external domain review and approved protocols
Standard workflow
- Define objective and observable:
- Primary question (for example dose, flux, heating, detector response)
- Acceptance criterion and required precision
- Define model assumptions:
- Geometry fidelity level and simplifications
- Material compositions, densities, temperature where relevant
- Physics models and energy cutoffs
- Define source and boundary conditions:
- Source type, spectrum, angular distribution, normalization
- Time dependence (steady-state vs transient approximation)
- Plan tallies and diagnostics:
- Tally regions and mesh resolution
- Relative error targets and confidence requirements
- Figure-of-merit targets where available
- Execute in staged runs:
- Pilot run for sanity checks
- Production runs with explicit random seed strategy
- Optional independent replicate runs for robustness
- Verify and validate:
- Conservation checks (energy/particle balance)
- Sensitivity checks to key assumptions
- Comparison to benchmark or analytic limits
- Report with uncertainty:
- Mean, variance, confidence intervals
- Dominant uncertainty sources (statistical vs model)
- Reproducibility metadata (tool version, inputs, seed policy)
Quality checklist
- Units are consistent and explicitly documented
- Geometry/material definitions are versioned and reviewable
- Physics list/cross-section library is recorded
- Statistical uncertainty is reported with each key metric
- Variance reduction methods are justified and documented
- Any non-convergence is surfaced, not hidden
Deliverables
- Reproducible input set and run configuration
- Short methods note (assumptions, solver settings, validation checks)
- Results table with uncertainty and confidence reporting
- Follow-up list for model risks and sensitivity gaps