Uncertainty quantification propagation in FNG HCPB-tritium breeding module using OpenMC-SANDY workflow

Nuclear data (ND) uncertainty quantification (UQ) supports the establishment of safety margins for fusion neutronic designs. This study applies an open-source, stochastic UQ workflow — coupling the continuous-energy Monte Carlo transport code OpenMC with the SANDY nuclear-data sampling toolkit — to the Frascati Neutron Generator (FNG) Helium-Cooled Pebble Bed (HCPB) Tritium Breeding Module (TBM) mock-up benchmark. ENDF/B-VIII.0 and JEFF-3.3 serve as the transport base libraries, with SANDY generating 400 random samples from its covariance evaluation to propagate within-library ND uncertainty to the Tritium Production Rate ( T P R ) in the L i 2 C O 3 detector pellets and to activation reaction rates at increasing shielding depth. A prior sensitivity screening identified the 9 ⁢ B e ⁡ ( 𝑛 , t o t a l ) and ( 𝑛 , 2 ⁢ 𝑛 ) channels and 6 ⁢ L i ⁡ ( 𝑛 , 𝛼 ) ⁢ t as the leading contributors. 9 ⁢ B e cross-section uncertainty dominates the computational variance. The ND contribution to calculated T P R is approximately 4% ( 2 ⁢ 𝜎 coverage), with per-run Monte Carlo statistical error held below 0.5%. Measured TPR carries an experimental uncertainty of approximately 7.8% ( 2 ⁢ 𝜎 ). Rather than summing these into a single figure, calculation and measurement are compared as C/E ratios per pellet stack, which show reasonable agreement with the uncertainty band. A spatial gradient in ND uncertainty is observed, the calculated spread widening from 2 – 3 % at the front stack to 3 – 5 % at the rear. Calculated activation and TPR profiles show reasonable agreement with experiment, with the exception of a consistent underprediction of low-energy monitors, most notably 1 9 7 ⁢ A u ⁡ ( 𝑛 , 𝛾 ) . It is attributed to an under-moderated calculated flux at depth, implicating the thermal-scattering ( 𝑆 ⁡ ( 𝛼 , 𝛽 ) ) and elastic-scattering data of the beryllium multiplier — rather than to the Au evaluation itself, and is identified for further investigation on that basis. Because the same effect implies that parasitic low-energy capture may be underestimated in the model, its impact on the tritium breeding ratio (TBR) is not assumed to be conservative; the directional effect is quantified directly rather than asserted.

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Publication Details

Journal
Fusion Engineering and Design
Published
2026-09-12
DOI
https://doi.org/10.1016/j.fusengdes.2026.116030
Primary Topic
Nuclear reactor physics and engineering
Type
article
Field-Weighted Citation Impact
0.00

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Uncertainty quantification propagation in FNG HCPB-tritium breeding module using OpenMC-SANDY workflow

Yogendra Singh Panchal, G. Ivan Maldonado
Fusion Engineering and Design
Nuclear reactor physics and engineering
article

Uncertainty quantification propagation in FNG HCPB-tritium breeding module using OpenMC-SANDY workflow

Yogendra Singh Panchal, G. Ivan Maldonado
article en

Abstract

Nuclear data (ND) uncertainty quantification (UQ) supports the establishment of safety margins for fusion neutronic designs. This study applies an open-source, stochastic UQ workflow — coupling the continuous-energy Monte Carlo transport code OpenMC with the SANDY nuclear-data sampling toolkit — to the Frascati Neutron Generator (FNG) Helium-Cooled Pebble Bed (HCPB) Tritium Breeding Module (TBM) mock-up benchmark. ENDF/B-VIII.0 and JEFF-3.3 serve as the transport base libraries, with SANDY generating 400 random samples from its covariance evaluation to propagate within-library ND uncertainty to the Tritium Production Rate ( T P R ) in the L i 2 C O 3 detector pellets and to activation reaction rates at increasing shielding depth. A prior sensitivity screening identified the 9 ⁢ B e ⁡ ( 𝑛 , t o t a l ) and ( 𝑛 , 2 ⁢ 𝑛 ) channels and 6 ⁢ L i ⁡ ( 𝑛 , 𝛼 ) ⁢ t as the leading contributors. 9 ⁢ B e cross-section uncertainty dominates the computational variance. The ND contribution to calculated T P R is approximately 4% ( 2 ⁢ 𝜎 coverage), with per-run Monte Carlo statistical error held below 0.5%. Measured TPR carries an experimental uncertainty of approximately 7.8% ( 2 ⁢ 𝜎 ). Rather than summing these into a single figure, calculation and measurement are compared as C/E ratios per pellet stack, which show reasonable agreement with the uncertainty band. A spatial gradient in ND uncertainty is observed, the calculated spread widening from 2 – 3 % at the front stack to 3 – 5 % at the rear. Calculated activation and TPR profiles show reasonable agreement with experiment, with the exception of a consistent underprediction of low-energy monitors, most notably 1 9 7 ⁢ A u ⁡ ( 𝑛 , 𝛾 ) . It is attributed to an under-moderated calculated flux at depth, implicating the thermal-scattering ( 𝑆 ⁡ ( 𝛼 , 𝛽 ) ) and elastic-scattering data of the beryllium multiplier — rather than to the Au evaluation itself, and is identified for further investigation on that basis. Because the same effect implies that parasitic low-energy capture may be underestimated in the model, its impact on the tritium breeding ratio (TBR) is not assumed to be conservative; the directional effect is quantified directly rather than asserted.

Fusion Engineering and DesignVol. 233
Knoxville College (US), University of Tennessee at Knoxville (US)
Fusion Energy Sciences
Openalex Percentile: Top 7%
Nuclear reactor physics and engineering
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