SCONE Simulation Settings

Configuring a SCONE simulation requires careful consideration of parameters that control the Monte Carlo calculation. These settings work together to determine the simulation's accuracy, efficiency, and output characteristics. Understanding how to properly configure these parameters is crucial for obtaining reliable results while maintaining computational efficiency.

SCONE supports two primary simulation modes. Eigenvalue (k-eigenvalue) calculations determine multiplication factors and flux distributions in fissile systems, commonly used in reactor analysis. Fixed-source calculations model systems with known neutron sources, ideal for shielding or radiation transport problems.

type eigenPhysicsPackage;

pop      100000;   // particles per cycle
active   500;      // active cycles for statistics
inactive 100;      // inactive cycles for source convergence
XSdata ce;
dataType ce;

collisionOperator { neutronCE { type neutronCEstd; } }
transportOperator { type transportOperatorDT; }

Use type eigenPhysicsPackage; for criticality calculations or type fixedSourcePhysicsPackage; for fixed-source runs. The physics package type must be declared at the top of the input.

type fixedSourcePhysicsPackage;

pop 100000;   // particles per batch
cycles 200;   // number of batches (fixed-source uses cycles, not active/inactive)
XSdata ce;
dataType ce;

collisionOperator { neutronCE { type neutronCEstd; } }
transportOperator { type transportOperatorDT; }

The accuracy and efficiency of your simulation depend heavily on core control parameters. For eigenvalue calculations, pop sets particles per cycle, active sets the number of cycles used for statistics, and inactive sets cycles for source convergence before tallying begins.

pop      100000;   // particles per cycle (higher = better statistics)
active   300;     // active cycles (more = smaller k-eff uncertainty)
inactive 200;    // inactive cycles (more = better source convergence)

// For eigenvalue: total cycles = inactive + active
// Statistics improve with sqrt(pop * active)

For eigenvalue calculations, additional parameters control source convergence and iteration behavior. Tune pop, active, and inactive first; then adjust tally-map resolution as needed.

activeTally {
  fissionRate {
    type collisionClerk;
    response (fission);
    fission { type macroResponse; MT -6; }
    map {
      type multiMap;
      maps (xax yax);
      xax { type spaceMap; axis x; grid lin; N 255; min -161.2773; max 161.2773; }
      yax { type spaceMap; axis y; grid lin; N 255; min -161.2773; max 161.2773; }
    }
  }
}

// Tune pop/active/inactive first; then tune tally-map resolution.

SCONE offers flexible output options. The outputFormat directive controls how tally results are written. Use asciiMATLAB (default) for MATLAB-compatible output or asciiJSON for structured JSON output.

// Output format: asciiMATLAB (default) or asciiJSON
outputFormat asciiJSON;

// Default is asciiMATLAB if outputFormat is omitted

The viz block configures visualization output. You can define bitmap images of the geometry (e.g., material map, flux) at specified planes. Each viz entry specifies the output file, view centre, width, axis, and resolution.

viz {
  bmpZ {
    type bmp;
    output imgZ;
    what material;
    centre (0.0 0.0 422.5);
    width (500.0 500.0);
    axis z;
    res (5000 5000);
  }
}

When configuring SCONE, start with conservative settings during testing. Begin with smaller particle populations and fewer cycles to quickly identify input errors. Monitor source convergence through Shannon entropy before collecting statistics. For complex geometries like full-core models, plan for more inactive cycles to ensure proper source distribution.

Use fixed random seeds during development to ensure reproducible results. Consider your output needs carefully. Remember that simulation settings involve tradeoffs between accuracy, performance, and resource usage.