sandy 1.2.0

SANDY: sampling of nuclear data and uncertainty

SANDY

Sampling tool for nuclear data

SANDY is a python package that can read, write and perform a set of operations on nuclear data files in ENDF-6 format.

Stochastic sampling of nuclear data

The primary objective of the code, as it was originally conceived, is to produce perturbed files containing sampled parameters that represent the information stored in the evaluated nuclear data covariances. Such files can be ultimately used to propagate uncertainties through any given compatible system using a brute force technique.

Currently, SANDY can draw samples for:

• cross sections;
• energy distrbutions of outgoing particles;
• fission neutron multiplicities;
• fission yields;
• radioactive decay data.

API for ENDF-6 files

The recent development on SANDY extended the original goal and focused on providing a simple interface for nuclear data files in ENDF-6 format. Nuclear data such as cross sections, fission yields, radioactive decay constants and so on can be imported into tabulated dataframes (making extensive use of pandas) for further post-processing, analysis, plotting, ...

Examples are available here.

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:wrench: Installation

SANDY can be installed both on Linux (recommended) or Windows. The installation instructions are available here.

:exclamation: Try the installation procedure on a dedicated environment by clicking the badge below (see also this github repository).

:hourglass: Development history and releases

The latest and older releases of SANDY are available here.

For a detailed list of changes across versions, please refer to the CHANGELOG file.

:computer: Try out the SANDY workshop

The materials for a hands‑on workshop given at PHYSOR2026 was collected in a github repository.

To try it out, click the badge below and launch the full environment in your browser — no installation needed.

:notebook_with_decorative_cover: Documentation and how to use SANDY

The official SANDY documentation can be found here.

The primary use for SANDY is to produce perturbed nuclear data files that statistically represent the covariance information found in evaluated libraries. This can be done using a command line interface.

Example for cross sections, nubar and pfns sampling

# Produce ENDF-6 / PENDF perturbed files
python -m sandy.sampling  U235.jeff33  --processes 20  --samples 200

# Produce ACE perturbed files
python -m sandy.sampling  U235.jeff33  --processes 20  --samples 200  --acer  --temperatures 900 

For a more advanced use, look at these notebooks:

• Direct sampling procedure: produce random ENDF-6 / ACE files
• 2-step sampling procedure

Example for radioactive decay data sampling

python -m sandy.sampling  decay_data.jeff33  --processes 20  --samples 200

Example for fission yield data sampling

# Only variance
python -m sandy.sampling nfy.jeff33 --processes 20 --samples 200

# With CEA covariance matrices for U235th and Pu239th 
python -m sandy.sampling nfy.jeff33 --processes 20 --samples 200  --fycov

For a more advanced use, look at these notebooks:

• FY Sampling: create perturbed FY files (only variance, all energies)
• FY sampling: create perturbed FY files with ad-hoc covariance matrix (all energies)
• FY sampling: create perturbed FY files using CEA conservative evaluation for U-235 thermal fission
• FY sampling: create perturbed FY files using CEA conservative evaluation for Pu-239 thermal fission

:video_game: Jupyter notebooks

Here you can find some cool Jupyter notebooks that kind of give an idea of what one can do with SANDY.

:telephone_receiver: Contacts

Luca Fiorito

• [e-mail]
• [e-mail]

:bookmark: Acknowledgments

SANDY was conceived and developed as a part of the PhD thesis on Nuclear data uncertainty propagation and uncertainty quantification in nuclear codes in the framework of a collaboration between SCK CEN and ULB.

:clipboard: Reference

Among the publications about SANDY, please use the following as references for citation.

Burnup analysis

• L. Fiorito, L. Engelen, F. Grimaldi, P. Romojaro, Nuclear data uncertainty propagation in the ARIANE GU3 burnup model using SANDY: Comparison between a FA and a pincell model, Annals of Nuclear Energy, Volume 218, 2025, 111423, ISSN 0306-4549.

Criticality

• L. Fiorito, J. Dyrda and M. Fleming, JEFF-3.3 covariance application to ICSBEP using SANDY and NDAST, EPJ Web of Conferences 211, 07003 (2019)

Original publication

• L. Fiorito, G. Žerovnik, A. Stankovskiy, G. Van den Eynde, P.E. Labeau, Nuclear data uncertainty propagation to integral responses using SANDY, Annals of Nuclear Energy, Volume 101, 2017, Pages 359-366, ISSN 0306-4549.

:earth_africa: Publications

This is a (incomplete) list of scientific studies citing SANDY.

• Ebiwonjumi, Bamidele. Uncertainty Analyses of Tritium Production and Gamma Heating Rates in. FUSION SCIENCE AND TECHNOLOGY, 2025. Article; Early Access, Vol. N/A. DOI
• Delipei, Gregory K.. Uncertainty Quantification Framework for High-Temperature Gas-Cooled. NUCLEAR SCIENCE AND ENGINEERING, 2025. Article; Early Access, Vol. N/A. DOI
• Yaseen, Mahmoud. Sensitivity and uncertainty analysis in pebble-bed reactors: A study. ANNALS OF NUCLEAR ENERGY, 2025. Article, Vol. 219. DOI
• Fiorito, Luca. Nuclear data uncertainty propagation in the ARIANE GU3 burnup model. ANNALS OF NUCLEAR ENERGY, 2025. Article, Vol. 218. DOI
• Ryzhkov, Alexander A.. A review of the current nuclear data performance assessments in advanced. ANNALS OF NUCLEAR ENERGY, 2025. Review, Vol. 212. DOI
• Lovecky, M.. OPOS-1000: Advancing the efficiency of VVER-1000 spent nuclear fuel cask. NUCLEAR ENGINEERING AND DESIGN, 2025. Article, Vol. 431. DOI
• Dagan, R.. Investigation of nuclide inventory of cladding material irradiated in. ANNALS OF NUCLEAR ENERGY, 2025. Article, Vol. 212. DOI
• Lovecky, M.. Optimizing spent nuclear fuel cask loading for VVER-440 fuel. NUCLEAR ENGINEERING AND TECHNOLOGY, 2024. Article, Vol. 56. DOI
• Osman, W.. AN INTEGRATED FRAMEWORK FOR UNCERTAINTY QUANTIFICATION IN HIGH. Arxiv, 2024. preprint, Vol. N/A. DOI
• Jo, YuGwon. Uncertainty quantification based on similarity analysis of reactor. NUCLEAR ENGINEERING AND TECHNOLOGY, 2024. Article, Vol. 56. DOI
• Zu, Tiejun. Development and Verification of Sampling Code NECP-SOUL for Evaluated. NUCLEAR SCIENCE AND ENGINEERING, 2025. Article, Vol. 199. DOI
• Fiorito, Luca. Uncertainty Quantification for the Doppler Reactivity Feedback. NUCLEAR SCIENCE AND ENGINEERING, 2025. Review, Vol. 199. DOI
• Belfiore, Enrica. On the Assumptions Behind Statistical Sampling: A ²³⁵U. NUCLEAR SCIENCE AND ENGINEERING, 2025. Article, Vol. 199. DOI
• Lovecky, Martin. TOTAL MONTE CARLO UNCERTAINTY ANALYSIS OF VVER-440 SPENT NUCLEAR FUEL IN. PROCEEDINGS OF 2024 31ST INTERNATIONAL CONFERENCE ON NUCLEAR, 2024. Proceedings Paper, Vol. N/A. DOI
• Belanger, Hunter. Comparison and evaluation of resolved resonance region covariance. JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY, 2024. Article, Vol. 61. DOI
• Schnabel, Georg. How to explain ENDF-6 to computers: A formal ENDF format description. Arxiv, 2023. preprint, Vol. N/A. DOI
• Tada, Kenichi. Development of nuclear data processing code FRENDY version 2. JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY, 2024. Article, Vol. 61. DOI
• Lovecky, M.. Radiation damage analysis of the first generation VVER spent nuclear. ANNALS OF NUCLEAR ENERGY, 2024. Article, Vol. 196. DOI
• Martin-Hernandez, Guido. Device and method for low-uncertainty and high-efficiency neutron. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS, 2023. Article, Vol. 1057. DOI
• Lindley, B. A.. Ranking of nuclear data contributions to uncertainties in core physics. ANNALS OF NUCLEAR ENERGY, 2023. Article, Vol. 194. DOI
• Kleedtke, Noah. Utilization of ACE nuclear data file toolkit ACEtk to calculate relative. ANNALS OF NUCLEAR ENERGY, 2023. Article, Vol. 193. DOI
• Abrate, Nicolo. Nuclear Data Uncertainty Propagation for the Molten Salt Fast Reactor. NUCLEAR SCIENCE AND ENGINEERING, 2023. Article, Vol. 197. DOI
• Grimaldi, Federico. Nuclear data uncertainty quantification on PWR spent nuclear fuel as a. FRONTIERS IN ENERGY RESEARCH, 2023. Article, Vol. 11. DOI
• Aimetta, Alex. A Nonintrusive Nuclear Data Uncertainty Propagation Study for the ARC. NUCLEAR SCIENCE AND ENGINEERING, 2023. Article, Vol. 197. DOI
• Tada, Kenichi. Development of ACE file perturbation tool using FRENDY. JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY, 2023. Article, Vol. 60. DOI
• Ebiwonjumi, Bamidele. Propagation of radiation source uncertainties in spent fuel cask. NUCLEAR ENGINEERING AND TECHNOLOGY, 2022. Article, Vol. 54. DOI
• Salino, Vivian. Incertitudes et ajustements de données nucléaires au moyen de méthodes. , 2022. Dissertation/Thesis, Vol. N/A. DOI
• Yamano, Naoki. Crucial importance of correlation between cross sections and angular. JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY, 2022. Article, Vol. 59. DOI
• Neudecker, Denise. Informing nuclear physics via machine learning methods with differential. PHYSICAL REVIEW C, 2021. Article, Vol. 104. DOI
• Patel, Vishal. An uncertainty quantification method relevant to material test reactors. ANNALS OF NUCLEAR ENERGY, 2022. Article, Vol. 165. DOI
• Abrate, Nicolo. Generalized perturbation techniques for uncertainty quantification in. ANNALS OF NUCLEAR ENERGY, 2021. Article, Vol. 164. DOI
• Gray, Ander. Uncertainty Propagation in SINBAD Fusion Benchmarks with Total Monte. FUSION SCIENCE AND TECHNOLOGY, 2021. Article, Vol. 77. DOI
• Kos, Bor. ASUSD nuclear data sensitivity and uncertainty program package:. NUCLEAR ENGINEERING AND TECHNOLOGY, 2021. Article, Vol. 53. DOI
• Skarbeli, Aris V.. Comparison of nuclear data uncertainties with other nuclear fuel cycle. INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2021. Article, Vol. 45. DOI
• Fiorito, L.. On the use of criticality and depletion benchmarks for verification of. ANNALS OF NUCLEAR ENERGY, 2021. Article, Vol. 161. DOI
• Park, Jin Hun. Statistical Analysis of Tritium Breeding Ratio Deviations in the DEMO. APPLIED SCIENCES-BASEL, 2021. Article, Vol. 11. DOI
• Neudecker, Denise. Which nuclear data can be validated with LLNL pulsed-sphere experiments?. ANNALS OF NUCLEAR ENERGY, 2021. Article, Vol. 159. DOI
• Skarbeli, Aris, V. Uncertainty and Optimization Analysis of Advanced Nuclear Fuel Cycles. 30TH INTERNATIONAL CONFERENCE NUCLEAR ENERGY FOR NEW EUROPE (NENE 2021), 2021. Proceedings Paper, Vol. N/A. DOI
• Fleming, M.. The High-Energy Intra-Nuclear Cascade Liege-based Residual (HEIR). ND 2019: INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND, 2020. Proceedings Paper, Vol. 239. DOI
• Fleming, M.. New features and improvements in the NEA nuclear data tool suite. ND 2019: INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND, 2020. Proceedings Paper, Vol. 239. DOI
• Solis, Augusto Hernandez. Depletion uncertainty analysis to the MYRRHA fuel assembly model. ND 2019: INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND, 2020. Proceedings Paper, Vol. 239. DOI
• Sui, Zhuojie. Covariance-oriented sample transformation: A new sampling method for. ANNALS OF NUCLEAR ENERGY, 2019. Article, Vol. 134. DOI
• Romojaro, P.. Sensitivity methods for effective delayed neutron fraction and neutron. ANNALS OF NUCLEAR ENERGY, 2019. Article; Proceedings Paper, Vol. 126. DOI
• Goricanec, Tanja. Evaluation of the criticality and reaction rate benchmark experiments. PROGRESS IN NUCLEAR ENERGY, 2019. Article, Vol. 111. DOI
• Cabellos, Oscar. Examples of Monte Carlo techniques applied for nuclear data uncertainty. 5TH INTERNATIONAL WORKSHOP ON NUCLEAR DATA EVALUATION FOR REACTOR, 2019. Proceedings Paper, Vol. 211. DOI
• Fiorito, Luca. JEFF-3.3 covariance application to ICSBEP using SANDY and NDAST. 5TH INTERNATIONAL WORKSHOP ON NUCLEAR DATA EVALUATION FOR REACTOR, 2019. Proceedings Paper, Vol. 211. DOI
• Sjostrand, Henrik. Monte Carlo integral adjustment of nuclear data libraries - experimental. 5TH INTERNATIONAL WORKSHOP ON NUCLEAR DATA EVALUATION FOR REACTOR, 2019. Proceedings Paper, Vol. 211. DOI
• Kos, Bor. Validation and Use of Coupling SUSD3D with Denovo for Complex. 28TH INTERNATIONAL CONFERENCE NUCLEAR ENERGY FOR NEW EUROPE (NENE 2019), 2019. Proceedings Paper, Vol. N/A. DOI
• Stankovskiy, Alexey. High-energy nuclear data uncertainties propagated to MYRRHA safety. ANNALS OF NUCLEAR ENERGY, 2018. Article, Vol. 120. DOI
• Trottier, Alexandre. Nuclear data sensitivity for reactor physics parameters in a lead-cooled. ANNALS OF NUCLEAR ENERGY, 2018. Article, Vol. 120. DOI
• Castro, E.. Impact of the homogenization level, nodal or pin-by-pin, on the. PROGRESS IN NUCLEAR ENERGY, 2018. Article, Vol. 104. DOI
• Iwamoto, Hiroki. Monte Carlo uncertainty quantification of the effective delayed neutron. JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY, 2018. Article, Vol. 55. DOI
• Calic, Dusan. Nuclear Data Uncertainties of the BEAVRS Benchmark Core. 27TH INTERNATIONAL CONFERENCE NUCLEAR ENERGY FOR NEW EUROPE (NENE 2018), 2018. Proceedings Paper, Vol. N/A. DOI
• Kos, Bor. Coupling of the SUSD3D S/U Code With the Denovo Deterministic Transport. 27TH INTERNATIONAL CONFERENCE NUCLEAR ENERGY FOR NEW EUROPE (NENE 2018), 2018. Proceedings Paper, Vol. N/A. DOI
• Ambrozic, K.. Computational analysis of the dose rates at JSI TRIGA reactor. APPLIED RADIATION AND ISOTOPES, 2017. Article, Vol. 130. DOI
• Griseri, M.. Nuclear data uncertainty propagation on a sodium fast reactor. NUCLEAR ENGINEERING AND DESIGN, 2017. Article, Vol. 324. DOI
• Stankovskiy, Alexey. Impact of intermediate and high energy nuclear data on the neutronic. ND 2016: INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND, 2017. Proceedings Paper, Vol. 146. DOI