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Serpent 2: Advanced Neutron Transport Code

Updated 18 December 2025
  • Neutron Transport Code Serpent 2 is a Monte Carlo simulation tool that provides high-fidelity reactor core modeling with emphasis on accuracy and computational efficiency.
  • It employs advanced constructive solid geometry techniques to accurately represent complex reactor configurations, such as the TRIGA Mark II core.
  • The code integrates detailed material definitions and temperature-dependent cross-section treatments to deliver precise criticality and reactivity calculations, benchmarked against MCNP.

Serpent 2 is a Monte Carlo neutron transport code designed for high-fidelity reactor core modeling, featuring advanced geometry handling, robust material definition, and integrated support for temperature-dependent cross-section treatment. It has been applied to the simulation and validation of complex reactor systems, such as the TRIGA Mark II reactor at Pavia, with detailed three-dimensional geometries and comprehensive treatment of operational scenarios encompassing both low and full power conditions. Serpent 2 is capable of accurate criticality calculations, supports sophisticated control-rod modeling and calibration, and enables direct benchmarking against established neutronics codes such as MCNP, exhibiting both accuracy and computational efficiency (Castagna et al., 2017).

1. Three-Dimensional Geometry Representation

Serpent 2 employs a flexible geometry modeling framework with its input language centered on constructive solid geometry (CSG). For the TRIGA Mark II reactor, the geometric origin is positioned at the active core’s center. The primary core is modeled as a right circular cylinder (radius R=0.223R=0.223 m, half-height H=0.324H=0.324 m), bounded axially by two aluminum grids (z=±0.324z = \pm 0.324 m), declared as:

rjr_j2

The “circular cluster” array structure natively supports the placement of 91 core sites (fuel, dummies, control rods, irradiation thimbles, source channels) in concentric rings. For example:

rjr_j3

uses the parameters: total locations, ring count, and pitch. Positioning of each element utilizes rotation (θi\theta_i) and radial offset (rjr_j) transformations. Fuel elements and absorbers are defined through cell cards such as:

rjr_j4

Movable control rods (Ag–In–Cd absorber in Al) are realized as cylinders with user-defined translations representing insertion steps (Δz=step×0.054\Delta z = \text{step} \times 0.054 cm). The 30 cm–thick graphite reflector, as well as the surrounding water pool (radius 150 cm, height 300 cm), are constructed as additional cylinders and cells.

2. Materials, Cross Sections, and Temperature Treatment

Material specification in Serpent 2 is granular, allowing precise assignment of physical and isotopic properties to each system component. For TRIGA core fuel, each element contains zirconium hydride (ZrH) and uranium metal at 8 wt% (20% 235^{235}U enrichment). Each fuel rod is individually characterized to replicate historical U-mass data (from 1965):

rjr_j5

Cladding, water, graphite, and control rods use similarly detailed cards; for instance, Al-6061 at 2.70 g/cm³ (cladding), H2_2O at 0.998 g/cm³ (pool), graphite at 1.70 g/cm³, and Ag-In-Cd control rods at 7.8 g/cm³. Cross-section treatment utilizes JEFF-3.1 data for most reactions; thermal scattering for ZrH and water employs ENDF/B-VII.1 S(α,β)(\alpha,\beta) data. Material temperature assignments are scenario-dependent: low-power runs use T=300T=300 K across all materials; full-power conditions require individualized Doppler-broadened cross sections—generated at variable H=0.324H=0.3240 with MAKXSF and linked to spatial regions in the fuel. In full-power scenarios, the core is divided into five axial layers and five concentric radial rings, with local temperature H=0.324H=0.3241 (from coupled neutronics–thermal-hydraulics analysis) directly assigned.

3. Serpent 2 Input Syntax and Key Directives

The Serpent 2.1.19 input for a comprehensive TRIGA model consists of initialization, geometry, materials, and calculation modes. Core elements include:

rjr_j6

“mode k” specifies eigenvalue search mode; “ksrc” and “pop” define initial source and population/sample cycle configuration (500 cycles × H=0.324H=0.3242 histories, skipping the first 50 cycles for source convergence). The geometry and materials are set as illustrated in previous sections. Energy-integrated detectors can be defined (e.g., “det eflux 4”) for flux tallying, though not obligatory for H=0.324H=0.3243 determination.

4. Criticality and Reactivity Calculation Algorithms

Serpent 2 solves the H=0.324H=0.3244-eigenvalue problem with iterative source convergence. Beginning with a trial source, each cycle simulates H=0.324H=0.3245 neutrons, advancing the fission source and estimating H=0.324H=0.3246. The mean H=0.324H=0.3247 is computed over active cycles:

  • Statistical uncertainty: H=0.324H=0.3248, H=0.324H=0.3249
  • Reactivity: z=±0.324z = \pm 0.3240
  • Reactivity in dollars: z=±0.324z = \pm 0.3241) = \frac{\rho}{\beta_\text{eff}}, \ \beta_\text{eff} = 0.0073z=±0.324z = \pm 0.3242\sigma(\rho) \simeq \sigma(k_\text{eff}) / k_\text{eff}2 / 0.0073</li></ul><p>Thesemetricsarecriticalforvalidationandforcomparisonwithbenchmarkdataandalternativecodes.</p><h2class=paperheadingid=modelingpowereffectsandtemperaturefeedback>5.ModelingPowerEffectsandTemperatureFeedback</h2><p>Underfullpoweroperation(250kW),theTRIGAcoreexhibitssignificantthermalfeedback,manifestedinfueltemperatureincreasesandalteredneutronmoderationviaZrH.Serpent2modelsthisbysubdividingthecoreinto5axial×5radialregions(</li> </ul> <p>These metrics are critical for validation and for comparison with benchmark data and alternative codes.</p> <h2 class='paper-heading' id='modeling-power-effects-and-temperature-feedback'>5. Modeling Power Effects and Temperature Feedback</h2> <p>Under full-power operation (250 kW), the TRIGA core exhibits significant thermal feedback, manifested in fuel temperature increases and altered neutron moderation via ZrH. Serpent 2 models this by subdividing the core into 5 axial × 5 radial regions (z = \pm 0.324$3, $z = \pm 0.324$4), matching local fuel temperatures from thermal-hydraulics calculations. Each region is assigned a unique material definition with the corresponding temperature, e.g.:

    $r_j$7

    Serpent’s internal logic selects T-indexed cross-sections in 10 K intervals (generated via MAKXSF), providing accurate Doppler and S$z = \pm 0.324$5 treatment. Water and graphite structures remain at baseline 300 K in these models.

    6. Control Rod Modeling and Calibration

    Control rods, constructed as Ag–In–Cd alloy cylinders within aluminum tubes, are modeled as movable absorbers. The spatial positioning uses direct translation via z-axis displacement according to the mechanical step calibration (one step: 0.054 cm). For the Shim rod:

    $r_j$8

    where $z = \pm 0.324$6. Calibration proceeds by running simulations at baseline ($z = \pm 0.324$7), then for incremental rod movements, and evaluating reactivity shifts:

    $z = \pm 0.324$8

    These differential worths are compared to experimental in-hour data for validation. Simulation reproduces experimental rod calibration curves within 5–10% (systematic offset $z = \pm 0.324$9$ for the Transient rod).

    7. Benchmarking, Validation, and Code Comparison

    A rigorous benchmark suite was executed against archival TRIGA Mark II data and MCNP models:

    • Low-power benchmarks: 26 critical configurations at θi\theta_i0 K. For each configuration, θi\theta_i1 and θi\theta_i2)θi\theta_i3(0.08 \pm 0.02)$\theta_i$4 versus experimental reactivity loss $\theta_i$5$ when transitioning from low to high power.
    • Comparison with MCNP: With geometry, materials, and cross-section libraries matched, Serpent 2 systematically gives θi\theta_i6 values θi\theta_i70.06$\theta_i8(α,β)8(\alpha,\beta) cross sections streamline the workflow and reduce computation times by a factor of 2–3 on equivalent hardware (Castagna et al., 2017).

    In summary, Serpent 2 delivers θi\theta_i90.1$r_j00\simr_j$1, and exhibits workflow and runtime efficiencies suitable for advanced performance benchmarking and integration with multiphysics applications.

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