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Journal articles on the topic 'Uncertainties propagation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Dachs, Edgar. "Uncertainties in the activities of garnets and their propagation into geothermobarometry." European Journal of Mineralogy 6, no. 2 (1994): 291–96. http://dx.doi.org/10.1127/ejm/6/2/0291.

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2

Matzke, Manfred. "Propagation of uncertainties in unfolding procedures." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 476, no. 1-2 (2002): 230–41. http://dx.doi.org/10.1016/s0168-9002(01)01438-3.

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3

Dong, W. M., W. L. Chiang, and F. S. Wong. "Propagation of uncertainties in deterministic systems." Computers & Structures 26, no. 3 (1987): 415–23. http://dx.doi.org/10.1016/0045-7949(87)90041-1.

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4

Frosio, Thomas, Thomas Bonaccorsi, and Patrick Blaise. "Manufacturing Data Uncertainties Propagation Method in Burn-Up Problems." Science and Technology of Nuclear Installations 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/7275346.

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A nuclear data-based uncertainty propagation methodology is extended to enable propagation of manufacturing/technological data (TD) uncertainties in a burn-up calculation problem, taking into account correlation terms between Boltzmann and Bateman terms. The methodology is applied to reactivity and power distributions in a Material Testing Reactor benchmark. Due to the inherent statistical behavior of manufacturing tolerances, Monte Carlo sampling method is used for determining output perturbations on integral quantities. A global sensitivity analysis (GSA) is performed for each manufacturing parameter and allows identifying and ranking the influential parameters whose tolerances need to be better controlled. We show that the overall impact of some TD uncertainties, such as uranium enrichment, or fuel plate thickness, on the reactivity is negligible because the different core areas induce compensating effects on the global quantity. However, local quantities, such as power distributions, are strongly impacted by TD uncertainty propagations. For isotopic concentrations, no clear trends appear on the results.

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5

Hauptmanns, Ulrich. "Analytical propagation of uncertainties through fault trees." Reliability Engineering & System Safety 76, no. 3 (2002): 327–29. http://dx.doi.org/10.1016/s0951-8320(02)00016-9.

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6

van der Drift, J. H. M., and C. J. M. Heemskerk. "Propagation of Spatial Uncertainties Between Assembly Primitives." IFAC Proceedings Volumes 23, no. 3 (1990): 677–81. http://dx.doi.org/10.1016/s1474-6670(17)52638-5.

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7

TANSEL, BERRIN. "PROPAGATION OF PARAMETER UNCERTAINTIES TO SYSTEM DEPENDABILITY." Civil Engineering and Environmental Systems 16, no. 1 (1999): 19–35. http://dx.doi.org/10.1080/02630259908970249.

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8

Butler, T., C. Dawson, and T. Wildey. "Propagation of Uncertainties Using Improved Surrogate Models." SIAM/ASA Journal on Uncertainty Quantification 1, no. 1 (2013): 164–91. http://dx.doi.org/10.1137/120888399.

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9

Wiwatanadate, Phongtape, and H. Gregg Claycamp. "Exact propagation of uncertainties in multiplicative models." Human and Ecological Risk Assessment: An International Journal 6, no. 2 (2000): 355–68. http://dx.doi.org/10.1080/10807030009380068.

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10

Keey, R. B., and C. H. Smith. "The propagation of uncertainties in failure events." Reliability Engineering 10, no. 2 (1985): 105–27. http://dx.doi.org/10.1016/0143-8174(85)90004-6.

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11

Karmakar, Subhankar. "Propagation of uncertainties in water distribution systems modeling." Desalination and Water Treatment 33, no. 1-3 (2011): 107–17. http://dx.doi.org/10.5004/dwt.2011.2633.

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12

Mac, Duy-Hung, and Paul Sicsic. "Uncertainties Propagation within Offshore Flexible Pipes Risers Design." Procedia Engineering 213 (2018): 708–19. http://dx.doi.org/10.1016/j.proeng.2018.02.067.

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13

Kortelainen, Markus. "Propagation of uncertainties in the nuclear DFT models." Journal of Physics G: Nuclear and Particle Physics 42, no. 3 (2015): 034021. http://dx.doi.org/10.1088/0954-3899/42/3/034021.

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14

Vaezi, P., C. Holland, B. A. Grierson, G. M. Staebler, S. P. Smith, and O. Meneghini. "Propagation of input parameter uncertainties in transport models." Physics of Plasmas 25, no. 10 (2018): 102309. http://dx.doi.org/10.1063/1.5053906.

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15

Wang, C. M., and Hari K. Iyer. "Propagation of uncertainties in measurements using generalized inference." Metrologia 42, no. 2 (2005): 145–53. http://dx.doi.org/10.1088/0026-1394/42/2/010.

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16

Carpentier, Vincent, Mohamed Megharfi, Jacques Quint, Marc Priel, Michèle Desenfant, and Ronan Morice. "Estimation of hygrometry uncertainties by propagation of distributions." Metrologia 41, no. 6 (2004): 432–38. http://dx.doi.org/10.1088/0026-1394/41/6/011.

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17

Rouxelin, Pascal, Andrea Alfonsi, Gerhard Strydom, Maria Avramova, and Kostadin Ivanov. "Propagation of VHTRC manufacturing uncertainties with RAVEN/PHISICS." Annals of Nuclear Energy 165 (January 2022): 108667. http://dx.doi.org/10.1016/j.anucene.2021.108667.

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18

Lindley, Ben, Brendan Tollit, Peter Smith, et al. "FAST REACTOR MULTIPHYSICS AND UNCERTAINTY PROPAGATION WITHIN WIMS." EPJ Web of Conferences 247 (2021): 06002. http://dx.doi.org/10.1051/epjconf/202124706002.

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For liquid metal-cooled fast reactors (LMFRs), improved predictive modelling is desirable to facilitate reactor licensing and operation and move towards a best estimate plus uncertainty (BEPU) approach. A key source of uncertainty in fast reactor calculations arises from the underlying nuclear data. Addressing the propagation of such uncertainties through multiphysics calculations schemes is therefore of importance, and is being addressed through international projects such as the Sodium-cooled Fast Reactor Uncertainty Analysis in Modelling (SFR-UAM) benchmark. In this paper, a methodology for propagation of nuclear data uncertainties within WIMS is presented. Uncertainties on key reactor physics parameters are calculated for selected SFR-UAM benchmark exercises, with good agreement with previous results. A methodology for coupled neutronic-thermal-hydraulic calculations within WIMS is developed, where thermal feedback is introduced to the neutronic solution through coupling with the ARTHUR subchannel code within WIMS and applied to steady-state analysis of the Horizon 2020 ESFR-SMART project reference core. Finally, integration of reactor physics and fuel performance calculations is demonstrated through linking of the WIMS reactor physics code to the TRAFIC fast reactor fuel performance code, through a Fortran-C-Python (FCP) interface. Given the 3D multiphysics calculation methodology, thermal-hydraulic and fuel performance uncertainties can ultimately be sampled alongside the nuclear data uncertainties. Together, these developments are therefore an important step towards enabling propagation of uncertainties through high fidelity, multiphysics SFR calculations and hence facilitate BEPU methodologies.

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19

Mezian, c., Bruno Vallet, Bahman Soheilian, and Nicolas Paparoditis. "UNCERTAINTY PROPAGATION FOR TERRESTRIAL MOBILE LASER SCANNER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 331–35. http://dx.doi.org/10.5194/isprsarchives-xli-b3-331-2016.

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Laser scanners are used more and more in mobile mapping systems. They provide 3D point clouds that are used for object reconstruction and registration of the system. For both of those applications, uncertainty analysis of 3D points is of great interest but rarely investigated in the literature. In this paper we present a complete pipeline that takes into account all the sources of uncertainties and allows to compute a covariance matrix per 3D point. The sources of uncertainties are laser scanner, calibration of the scanner in relation to the vehicle and direct georeferencing system. We suppose that all the uncertainties follow the Gaussian law. The variances of the laser scanner measurements (two angles and one distance) are usually evaluated by the constructors. This is also the case for integrated direct georeferencing devices. Residuals of the calibration process were used to estimate the covariance matrix of the 6D transformation between scanner laser and the vehicle system. Knowing the variances of all sources of uncertainties, we applied uncertainty propagation technique to compute the variance-covariance matrix of every obtained 3D point. Such an uncertainty analysis enables to estimate the impact of different laser scanners and georeferencing devices on the quality of obtained 3D points. The obtained uncertainty values were illustrated using error ellipsoids on different datasets.

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20

Mezian, c., Bruno Vallet, Bahman Soheilian, and Nicolas Paparoditis. "UNCERTAINTY PROPAGATION FOR TERRESTRIAL MOBILE LASER SCANNER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 331–35. http://dx.doi.org/10.5194/isprs-archives-xli-b3-331-2016.

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Laser scanners are used more and more in mobile mapping systems. They provide 3D point clouds that are used for object reconstruction and registration of the system. For both of those applications, uncertainty analysis of 3D points is of great interest but rarely investigated in the literature. In this paper we present a complete pipeline that takes into account all the sources of uncertainties and allows to compute a covariance matrix per 3D point. The sources of uncertainties are laser scanner, calibration of the scanner in relation to the vehicle and direct georeferencing system. We suppose that all the uncertainties follow the Gaussian law. The variances of the laser scanner measurements (two angles and one distance) are usually evaluated by the constructors. This is also the case for integrated direct georeferencing devices. Residuals of the calibration process were used to estimate the covariance matrix of the 6D transformation between scanner laser and the vehicle system. Knowing the variances of all sources of uncertainties, we applied uncertainty propagation technique to compute the variance-covariance matrix of every obtained 3D point. Such an uncertainty analysis enables to estimate the impact of different laser scanners and georeferencing devices on the quality of obtained 3D points. The obtained uncertainty values were illustrated using error ellipsoids on different datasets.

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21

(Stacey) Gu, Xiaoyu, John E. Renaud, and Charles L. Penninger. "Implicit Uncertainty Propagation for Robust Collaborative Optimization." Journal of Mechanical Design 128, no. 4 (2006): 1001–13. http://dx.doi.org/10.1115/1.2205869.

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In this research we develop a mathematical construct for estimating uncertainties within the bilevel optimization framework of collaborative optimization. The collaborative optimization strategy employs decomposition techniques that decouple analysis tools in order to facilitate disciplinary autonomy and parallel execution. To ensure consistency of the physical artifact being designed, interdisciplinary consistency constraints are introduced at the system level. These constraints implicitly enforce multidisciplinary consistency when satisfied. The decomposition employed in collaborative optimization prevents the use of explicit propagation techniques for estimating uncertainties of system performance. In this investigation, we develop and evaluate an implicit method for estimating system performance uncertainties within the collaborative optimization framework. The methodology accounts for both the uncertainty associated with design inputs and the uncertainty of performance predictions from other disciplinary simulation tools. These implicit uncertainty estimates are used as the basis for a new robust collaborative optimization (RCO) framework. The bilevel robust optimization strategy developed in this research provides for disciplinary autonomy in system design, while simultaneously accounting for performance uncertainties to ensure feasible robustness of the resulting system. The method is effective in locating a feasible robust optimum in application studies involving a multidisciplinary aircraft concept sizing problem. The system-level consistency constraint formulation used in this investigation avoids the computational difficulties normally associated with convergence in collaborative optimization. The consistency constraints are formulated to have the inherent properties necessary for convergence of general nonconvex problems when performing collaborative optimization.

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22

CABELLOS, O., E. CASTRO, C. AHNERT, and C. HOLGADO. "PROPAGATION OF NUCLEAR DATA UNCERTAINTIES FOR PWR CORE ANALYSIS." Nuclear Engineering and Technology 46, no. 3 (2014): 299–312. http://dx.doi.org/10.5516/net.01.2014.709.

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23

Dossantos-Uzarralde, P., H. P. Jacquet, G. Dejonghe, and I. Kodeli. "Methodology investigations on uncertainties propagation in nuclear data evaluation." Nuclear Engineering and Design 246 (May 2012): 49–57. http://dx.doi.org/10.1016/j.nucengdes.2011.10.007.

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24

Diamant, Roee, and Lutz Lampe. "Underwater Localization with Time-Synchronization and Propagation Speed Uncertainties." IEEE Transactions on Mobile Computing 12, no. 7 (2013): 1257–69. http://dx.doi.org/10.1109/tmc.2012.100.

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25

Sjöstrand, Henrik, Sean Conroy, Petter Helgesson, et al. "Propagation of nuclear data uncertainties for fusion power measurements." EPJ Web of Conferences 146 (2017): 02034. http://dx.doi.org/10.1051/epjconf/201714602034.

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26

Barchiesi, Dominique, and Thomas Grosges. "Propagation of uncertainties and applications in numerical modeling: tutorial." Journal of the Optical Society of America A 34, no. 9 (2017): 1602. http://dx.doi.org/10.1364/josaa.34.001602.

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27

Camporeale, Enrico, Yuri Shprits, Mandar Chandorkar, Alexander Drozdov, and Simon Wing. "On the propagation of uncertainties in radiation belt simulations." Space Weather 14, no. 11 (2016): 982–92. http://dx.doi.org/10.1002/2016sw001494.

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28

Tan, Lisha, Zhongmin Deng, and Benke Shi. "PROPAGATION OF HYBRID UNCERTAINTIES IN TRANSIENT HEAT CONDUCTION PROBLEMS." International Journal for Uncertainty Quantification 9, no. 6 (2019): 543–68. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2019030736.

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29

Funtowicz, S. O., S. M. Macgill, and J. R. Ravetz. "The propagation of parameter uncertainties in radiological assessment models." Journal of Radiological Protection 9, no. 4 (1989): 271–80. http://dx.doi.org/10.1088/0952-4746/9/4/007.

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30

Gyasi-Agyei, Yeboah. "Propagation of uncertainties in interpolated rainfields to runoff errors." Hydrological Sciences Journal 64, no. 5 (2019): 587–606. http://dx.doi.org/10.1080/02626667.2019.1593989.

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31

Heslop, David, and Andrew P. Roberts. "Estimation and propagation of uncertainties associated with paleomagnetic directions." Journal of Geophysical Research: Solid Earth 121, no. 4 (2016): 2274–89. http://dx.doi.org/10.1002/2015jb012544.

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32

Sargeni, A., F. Fouet, E. Ivanov, and P. Probst. "Uncertainties propagation in the UAM numerical rod ejection benchmark." Annals of Nuclear Energy 141 (June 2020): 107339. http://dx.doi.org/10.1016/j.anucene.2020.107339.

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33

Badalyan, Alexander, and Phillip Pendleton. "Analysis of Uncertainties in Manometric Gas-Adsorption Measurements. I: Propagation of Uncertainties in BET Analyses." Langmuir 19, no. 19 (2003): 7919–28. http://dx.doi.org/10.1021/la020985t.

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34

Campolina, Daniel, and Jan Frybort. "UNCERTAINTY PROPAGATION FOR LWR BURNUP BENCHMARK USING SAMPLING BASED CODE SCALE/SAMPLER." Acta Polytechnica CTU Proceedings 14 (May 17, 2018): 8. http://dx.doi.org/10.14311/app.2018.14.0008.

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Sampling based method is adopted in many fields of engineering and it is currently used to propagate uncertainties from physical parameters and from nuclear data, to integral indicators of nuclear systems. The total uncertainty associated with a model simulation is of major importance for safety analysis and to guide vendors about acceptable tolerance limits for nuclear installations parts. This work presents some calculations to propagate uncertainties for a nuclear reactor fuel element modeled in SCALE/TRITON, using the sampling tool SCALE/SAMPLER. Results showed that that the influence of input uncertainties on kinf is more pronounced in the fresh core other than the depleted core and the contribution from studied manufacturing uncertainties is smaller than the contribution of nuclear data uncertainties.

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35

Ball, M. R., C. McEwan, D. R. Novog, and J. C. Luxat. "The Dilution Dependency of Multigroup Uncertainties." Science and Technology of Nuclear Installations 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/306406.

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The propagation of nuclear data uncertainties through reactor physics calculation has received attention through the Organization for Economic Cooperation and Development—Nuclear Energy Agency’s Uncertainty Analysis in Modelling (UAM) benchmark. A common strategy for performing lattice physics uncertainty analysis involves starting with nuclear data and covariance matrix which is typically available at infinite dilution. To describe the uncertainty of all multigroup physics parameters—including those at finite dilution—additional calculations must be performed that relate uncertainties in an infinite dilution cross-section to those at the problem dilution. Two potential methods for propagating dilution-related uncertainties were studied in this work. The first assumed a correlation between continuous-energy and multigroup cross-sectional data and uncertainties, which is convenient for direct implementation in lattice physics codes. The second is based on a more rigorous approach involving the Monte Carlo sampling of resonance parameters in evaluated nuclear data using the TALYS software. When applied to a light water fuel cell, the two approaches show significant differences, indicating that the assumption of the first method did not capture the complexity of physics parameter data uncertainties. It was found that the covariance of problem-dilution multigroup parameters for selected neutron cross-sections can vary significantly from their infinite-dilution counterparts.

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36

Holthuijsen, L. H., N. Booij, and L. Bertotti. "The Propagation of Wind Errors Through Ocean Wave Hindcasts." Journal of Offshore Mechanics and Arctic Engineering 118, no. 3 (1996): 184–89. http://dx.doi.org/10.1115/1.2828832.

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To estimate uncertainties in wave forecast and hindcasts, computations have been carried out for a location in the Mediterranean Sea using three different analyses of one historic wind field. These computations involve a systematic sensitivity analysis and estimated wind field errors. This technique enables a wave modeler to estimate such uncertainties in other forecasts and hindcasts if only one wind analysis is available.

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37

Hamburger, T., F. Gering, Y. Yevdin, S. Schantz, G. Geertsema, and H. de Vries. "Uncertainty propagation from ensemble dispersion simulations through a terrestrial food chain and dose model." Radioprotection 55 (May 2020): S69—S74. http://dx.doi.org/10.1051/radiopro/2020014.

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In the framework of the European project CONFIDENCE, Work Package 1 (WP1) focused on the uncertainties in the pre- and early phase of a radiological emergency. One subtask was to analyse the propagation of uncertainties from ensemble dispersion simulations through a terrestrial food chain and dose model. Uncertainties that may occur in the modelling of radioactivity in the food chain were added to previously defined meteorological and source term uncertainties. Endpoints of the ensemble calculations within the food chain model included activity concentrations in the food chain, i.e. feedstuffs and foodstuffs, as well as the internal dose through ingestion. This paper describes the uncertainty propagation through a terrestrial food chain and dose model and presents some illustrations of the results.

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38

Sarkar, Arnab. "Uncertainty propagation in Pu isotopic composition calculation by gamma spectrometry: theory versus experiment." Radiochimica Acta 109, no. 4 (2021): 301–10. http://dx.doi.org/10.1515/ract-2020-0085.

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Abstract Calculation and reporting of total uncertainties are essential criteria in the nuclear industry since measured results are used in decision making. Plutonium isotopic compositions (Pu IC) in different matrices are required at various stages of a close-loop nuclear fuel cycle. Under- and/or over-estimating uncertainties in Pu IC may result in avoidable radiological emergencies. In this work, we present the uncertainty budget for Pu IC determination using 120–450 keV gamma emission lines recorded with an HPGe. Detailed uncertainty propagation equations based on the propagation of partial derivatives are constructed and solved. Effects of the individual uncertainties on the total uncertainty are studied for different counting durations starting from 5 min up to 24 h. Results are compared with TIMS results and the theoretically calculated uncertainties were verified with multiple experimental data.

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39

Zhou, Chang Cong, Zhen Zhou Lu, and Qi Wang. "Iteration Algorithm for Propagation of Mixed Uncertainties in Reliability Analysis." Advanced Materials Research 199-200 (February 2011): 569–74. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.569.

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For structural reliability problems simultaneously involving random variables, interval variables and fuzzy variables, an iteration algorithm is proposed to deal with the propagation of uncertainties. Corresponding to assumed membership value in the membership level interval [0,1], the membership interval of fuzzy variables can be obtained. After the fuzzy variables and the interval variables’ effects on the extreme value of performance function are alternately and iteratively analyzed with the random variables’ effects on statistical properties of the performance function, converged design point can be calculated, and the reliability can be as well calculated by the fourth moment algorithm based on the point estimate method. Finally the membership function of the reliability can be solved. Owing to the faster convergence of the iteration algorithm, the efficiency of the proposed algorithm is highly improved compared to the conventional numerical simulation method. And the adoption of fourth moment method proves much better in accuracy than only applying first order reliability method in the iteration algorithm. After the basic concept and process of the proposed algorithm is detailed, several numerical and engineering examples are studied to demonstrate its advantage both in efficiency and accuracy.

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40

Sjöstrand, H., E. Alhassan, J. Duan, et al. "Propagation of Nuclear Data Uncertainties for ELECTRA Burn-up Calculations." Nuclear Data Sheets 118 (April 2014): 527–30. http://dx.doi.org/10.1016/j.nds.2014.04.125.

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41

Guillard, V., C. Guillaume, and S. Destercke. "Parameter uncertainties and error propagation in modified atmosphere packaging modelling." Postharvest Biology and Technology 67 (May 2012): 154–66. http://dx.doi.org/10.1016/j.postharvbio.2011.12.014.

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42

Jean, Cyrille De Saint, Gilles Noguere, Benoit Habert, and Bertrand Iooss. "A Monte Carlo Approach to Nuclear Model Parameter Uncertainties Propagation." Nuclear Science and Engineering 161, no. 3 (2009): 363–70. http://dx.doi.org/10.13182/nse161-363.

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43

Ikelheimer, Bruce, Micah Downing, and Michael James. "Study of the impact of input uncertainties on acoustic propagation." Journal of the Acoustical Society of America 125, no. 4 (2009): 2631. http://dx.doi.org/10.1121/1.4784042.

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44

Gabriele, Pierantoni, Coghlan Brian, Kenny Eamonn, Gallagher Peter, and Perez-Suarez David. "EXTENDING THE SHEBA PROPAGATION MODEL TO REDUCE PARAMETER-RELATED UNCERTAINTIES." Computer Science 14, no. 2 (2013): 253. http://dx.doi.org/10.7494/csci.2012.14.2.253.

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45

Gabriele, Pierantoni, Coghlan Brian, Kenny Eamonn, Gallagher Peter, and Perez-Suarez David. "EXTENDING THE SHEBA PROPAGATION MODEL TO REDUCE PARAMETER-RELATED UNCERTAINTIES." Computer Science 14, no. 3 (2013): 253. http://dx.doi.org/10.7494/csci.2013.14.2.253.

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46

Cattania, Camilla, Sebastian Hainzl, Lifeng Wang, Frank Roth, and Bogdan Enescu. "Propagation of Coulomb stress uncertainties in physics-based aftershock models." Journal of Geophysical Research: Solid Earth 119, no. 10 (2014): 7846–64. http://dx.doi.org/10.1002/2014jb011183.

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47

Gustafsson, Stefan, Borje Nilsson, Sven Nordebo, and Mats Sjoberg. "Wave Propagation Characteristics and Model Uncertainties for HVDC Power Cables." IEEE Transactions on Power Delivery 30, no. 6 (2015): 2527–34. http://dx.doi.org/10.1109/tpwrd.2015.2456237.

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48

Tellinghuisen, Joel. "Statistical uncertainties in RKR potentials: An exercise in error propagation." Journal of Molecular Spectroscopy 141, no. 2 (1990): 258–64. http://dx.doi.org/10.1016/0022-2852(90)90162-j.

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49

Schiemenz, Fabian, Jens Utzmann, and Hakan Kayal. "Propagation of grid-scale density model uncertainty to orbital uncertainties." Advances in Space Research 65, no. 1 (2020): 407–18. http://dx.doi.org/10.1016/j.asr.2019.10.013.

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50

Trivedi, Ishita, Jason Hou, Giacomo Grasso, Kostadin Ivanov, and Fausto Franceschini. "Nuclear Data Uncertainty Quantification and Propagation for Safety Analysis of Lead-Cooled Fast Reactors." Science and Technology of Nuclear Installations 2020 (August 12, 2020): 1–14. http://dx.doi.org/10.1155/2020/3961095.

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In this study, the Best Estimate Plus Uncertainty (BEPU) approach is developed for the systematic quantification and propagation of uncertainties in the modelling and simulation of lead-cooled fast reactors (LFRs) and applied to the demonstration LFR (DLFR) initially investigated by Westinghouse. The impact of nuclear data uncertainties based on ENDF/B-VII.0 covariances is quantified on lattice level using the generalized perturbation theory implemented with the Monte Carlo code Serpent and the deterministic code PERSENT of the Argonne Reactor Computational (ARC) suite. The quantities of interest are the main eigenvalue and selected reactivity coefficients such as Doppler, radial expansion, and fuel/clad/coolant density coefficients. These uncertainties are then propagated through safety analysis, carried out using the MiniSAS code, following the stochastic sampling approach in DAKOTA. An unprotected transient overpower (UTOP) scenario is considered to assess the effect of input uncertainties on safety parameters such as peak fuel and clad temperatures. It is found that in steady state, the multiplication factor shows the most sensitivity to perturbations in 235U fission, 235U ν, and 238U capture cross sections. The uncertainties of 239Pu and 238U capture cross sections become more significant as the fuel is irradiated. The covariance of various reactivity feedback coefficients is constructed by tracing back to common uncertainty contributors (i.e., nuclide-reaction pairs), including 238U inelastic, 238U capture, and 239Pu capture cross sections. It is also observed that nuclear data uncertainty propagates to uncertainty on peak clad and fuel temperatures of 28.5 K and 70.0 K, respectively. Such uncertainties do not impose per se threat to the integrity of the fuel rod; however, they sum to other sources of uncertainties in verifying the compliance of the assumed safety margins, suggesting the developed BEPU method necessary to provide one of the required insights on the impact of uncertainties on core safety characteristics.

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