| Fast uncertainty quantification of spent nuclear fuel with neural networks
Albà, A., Adelmann, A., Münster, L., Rochman, D., & Boiger, R. (2024). Fast uncertainty quantification of spent nuclear fuel with neural networks. Annals of Nuclear Energy, 196, 110204 (8 pp.). https://doi.org/10.1016/j.anucene.2023.110204 |
| An iPWR MELCOR 2.2 study on the impact of the modeling parameters on code performance and accident progression
Malicki, M., Darnowski, P., & Lind, T. (2024). An iPWR MELCOR 2.2 study on the impact of the modeling parameters on code performance and accident progression. Energies, 17(13), 3279 (24 pp.). https://doi.org/10.3390/en17133279 |
| Accelerated Monte Carlo perturbation calculation by surface recovery
Berry, L., Truchet, G., & Bonaccorsi, T. (2023). Accelerated Monte Carlo perturbation calculation by surface recovery. Annals of Nuclear Energy, 185, 109716 (11 pp.). https://doi.org/10.1016/j.anucene.2023.109716 |
| The iPWR MELCOR 2.2 parametric sensitivity analysis
Malicki, M., Darnowski, P., & Lind, T. (2023). The iPWR MELCOR 2.2 parametric sensitivity analysis. In 20th international topical meeting on nuclear reactor thermal hydraulics (NURETH-20) (pp. 4220-4233). https://doi.org/10.13182/NURETH20-40213 |
| Probabilistic risk assessment for the piping of a nuclear power plant: uncertainty and sensitivity analysis by using SINTAP procedure
Mao, G., Niffenegger, M., & Mao, X. (2022). Probabilistic risk assessment for the piping of a nuclear power plant: uncertainty and sensitivity analysis by using SINTAP procedure. International Journal of Pressure Vessels and Piping, 200, 104791 (10 pp.). https://doi.org/10.1016/j.ijpvp.2022.104791 |
| Physics-based 0D-U-I-SoC cell performance model for aqueous organic redox flow batteries
Mourouga, G., Schaerer, R. P., Yang, X., Janoschka, T., Schmidt, T. J., & Schumacher, J. O. (2022). Physics-based 0D-U-I-SoC cell performance model for aqueous organic redox flow batteries. Electrochimica Acta, 415, 140185 (18 pp.). https://doi.org/10.1016/j.electacta.2022.140185 |
| Impact of various source of covariance information on integral parameters uncertainty during depletion calculations with CASMO-5
Hursin, M., Rochman, D., Vasiliev, A., Ferroukhi, H., & Pautz, A. (2021). Impact of various source of covariance information on integral parameters uncertainty during depletion calculations with CASMO-5. In M. Margulis & P. Blaise (Eds.), EPJ web of conferences: Vol. 247. PHYSOR2020 - international conference on physics of reactors: transition to a scalable nuclear future (p. 09005 (10 pp.). https://doi.org/10.1051/epjconf/202124709005 |
| A framework based on statistical analysis and stakeholders' preferences to inform weighting in composite indicators
Lindén, D., Cinelli, M., Spada, M., Becker, W., Gasser, P., & Burgherr, P. (2021). A framework based on statistical analysis and stakeholders' preferences to inform weighting in composite indicators. Environmental Modelling and Software, 145, 105208 (16 pp.). https://doi.org/10.1016/j.envsoft.2021.105208 |
| Uncertainty and sensitivity analysis of the chemistry of cesium sorption in deep geological repositories
Ayoub, A., Pfingsten, W., Podofillini, L., & Sansavini, G. (2020). Uncertainty and sensitivity analysis of the chemistry of cesium sorption in deep geological repositories. Applied Geochemistry, 117, 104607 (12 pp.). https://doi.org/10.1016/j.apgeochem.2020.104607 |
| On data assimilation with Monte-Carlo-calculated and statistically uncertain sensitivity coefficients
Siefman, D., Hursin, M., Aufiero, M., Bidaud, A., & Pautz, A. (2020). On data assimilation with Monte-Carlo-calculated and statistically uncertain sensitivity coefficients. Annals of Nuclear Energy, 135, 106951 (13 pp.). https://doi.org/10.1016/j.anucene.2019.106951 |
| On nonintrusive uncertainty quantification and surrogate model construction in particle accelerator modeling
Adelmann, A. (2019). On nonintrusive uncertainty quantification and surrogate model construction in particle accelerator modeling. SIAM-ASA Journal on Uncertainty Quantification, 7(2), 383-416. https://doi.org/10.1137/16M1061928 |
| Determination of sobol sensitivity indices for correlated inputs with SHARK-X
Hursin, M., Siefman, D., Perret, G., Rochman, D., Vasiliev, A., & Ferroukhi, H. (2018). Determination of sobol sensitivity indices for correlated inputs with SHARK-X. In Proceedings of the PHYSOR 2018 (p. (12 pp.). American Nuclear Society. |
| Convergence analysis and criterion for data assimilation with sensitivities from Monte Carlo neutron transport codes
Siefman, D., Hursin, M., Aufiero, M., Bidaud, A., & Pautz, A. (2018). Convergence analysis and criterion for data assimilation with sensitivities from Monte Carlo neutron transport codes. In Proceedings of the PHYSOR 2018 (p. (10 pp.). American Nuclear Society. |
| Ranking of uncertain parameters for dynamic event tree analysis: a case study based on a Station Black Out scenario
Rahman, S., Karanki, D. R., Epiney, A., Zerkak, O., & Dang, V. N. (2015). Ranking of uncertain parameters for dynamic event tree analysis: a case study based on a Station Black Out scenario. In 16th international topical meeting on nuclear reactor thermal hydraulics (NURETH-16) (pp. 5734-5747). American Nuclear Society. |
| Producing synthetic natural gas from microalgae via supercritical water gasification: a techno-economic sensitivity analysis
Brandenberger, M., Matzenberger, J., Vogel, F., & Ludwig, C. (2013). Producing synthetic natural gas from microalgae via supercritical water gasification: a techno-economic sensitivity analysis. Biomass and Bioenergy, 51, 26-34. https://doi.org/10.1016/j.biombioe.2012.12.038 |
| Probabilistic fracture assessment of piping systems based on FITNET FFS procedure
Qian, G., & Niffenegger, M. (2011). Probabilistic fracture assessment of piping systems based on FITNET FFS procedure. Nuclear Engineering and Design, 241(3), 714-722. https://doi.org/10.1016/j.nucengdes.2011.01.019 |
| Two techniques of sensitivity and uncertainty analysis of fuzzy expert systems
Baraldi, P., Librizzi, M., Zio, E., Podofillini, L., & Dang, V. N. (2009). Two techniques of sensitivity and uncertainty analysis of fuzzy expert systems. Expert Systems with Applications, 36(10), 12461-12471. https://doi.org/10.1016/j.eswa.2009.04.036 |
| Charge, mass and heat transfer interactions in solid oxide fuel cells operated with different fuel gases - a sensitivity analysis
Nagel, F. P., Schildhauer, T. J., Biollaz, S. M. A., & Stucki, S. (2008). Charge, mass and heat transfer interactions in solid oxide fuel cells operated with different fuel gases - a sensitivity analysis. Journal of Power Sources, 184(1), 129-142. https://doi.org/10.1016/j.jpowsour.2008.05.044 |
