1 |
H. Cramer, Mathematical Methods of Statistics, vol. 43, Princeton university press, 1999.
|
2 |
J.C. Helton, J.D. Johnson, C.J. Sallaberry, C.B. Storlie, Survey of sampling-based methods for uncertainty and sensitivity analysis, Reliab. Eng. Syst. Saf. 91 (10-11) (2006) 1175-1209.
DOI
|
3 |
M.B. Chadwick, M. Herman, P. Oblozinsky, M.E. Dunn, Y. Danon, A. Kahler, D.L. Smith, B. Pritychenko, G. Arbanas, R. Arcilla, et al., ENDF/B-VII. 1 nuclear data for science and technology: cross sections, covariances, fission product yields and decay data, Nucl. Data Sheets 112 (12) (2011) 2887-2996.
DOI
|
4 |
J. Meija, T.B. Coplen, M. Berglund, W.A. Brand, P. De Bievre, M. Groning, N.E. Holden, J. Irrgeher, R.D. Loss, T. Walczyk, et al., Atomic weights of the elements 2013 (IUPAC technical report), Pure Appl. Chem. 88 (3) (2016) 265-291.
DOI
|
5 |
J.B. Briggs, L. Scott, A. Nouri, The international criticality safety benchmark evaluation project, Nucl. Sci. Eng. 145 (1) (2003) 1-10.
DOI
|
6 |
N.K.M. Faber, Uncertainty estimation for multivariate regression coefficients, Chemometr. Intell. Lab. Syst. 64 (2) (2002) 169-179.
DOI
|
7 |
D. Hamby, A comparison of sensitivity analysis techniques, Health Phys. 68 (2) (1995) 195-204.
DOI
|
8 |
R. Vallat, Pingouin: statistics in Python, J. Open Source Software 3 (31) (2018) 1026.
DOI
|
9 |
D. Price, M.I. Radaideh, D. O'Grady, T. Kozlowski, Advanced BWR criticality safety part II: cask criticality, burnup credit, sensitivity, and uncertainty analyses, Prog. Nucl. Energy 115 (2019) 126-139.
DOI
|
10 |
B.T. Rearden, M.L. Williams, M.A. Jessee, D.E. Mueller, D.A. Wiarda, Sensitivity and uncertainty analysis capabilities and data in SCALE, Nucl. Technol. 174 (2) (2011) 236-288.
DOI
|
11 |
M.I. Radaideh, M.I. Radaideh, Efficient analysis of parametric sensitivity and uncertainty of fuel cell models with application to SOFC, Int. J. Energy Res. 44 (4) (2020) 2517-2534.
DOI
|
12 |
T. Sutton, T. Donovan, T. Trumbull, P. Dobreff, E. Caro, D. Griesheimer, L. Tyburski, D. Carpenter, H. Joo, The MC21 Monte Carlo Transport Code, Tech. Rep, Knolls Atomic Power Laboratory (KAPL), Niskayuna, NY, 2007.
|
13 |
S. Kim, B. Schnitzler, Advanced Test Reactor: Serpentine Arrangement of Highly Enriched Water-Moderated Uranium-Aluminide Fuel Plates Reflected by Beryllium, Tech. Rep. HEU-MET-THERM-022, Idaho National Laboratory, 2005.
|
14 |
M.I. Radaideh, D. Price, D. O'Grady, T. Kozlowski, Advanced BWR criticality safety part I: model development, model benchmarking, and depletion with uncertainty analysis, Prog. Nucl. Energy 113 (2019) 230-246.
DOI
|
15 |
H. Janssen, Monte-Carlo based uncertainty analysis: sampling efficiency and sampling convergence, Reliab. Eng. Syst. Saf. 109 (2013) 123-132.
DOI
|
16 |
G. Ilas, H. Liljenfeldt, Decay heat uncertainty for BWR used fuel due to modeling and nuclear data uncertainties, Nucl. Eng. Des. 319 (2017) 176-184.
DOI
|
17 |
R.J. McConn, C.J. Gesh, R.T. Pagh, R.A. Rucker, R. Williams III, Compendium of Material Composition Data for Radiation Transport Modeling, Tech. Rep, Pacific Northwest National Lab.(PNNL), Richland, WA (United States), 2011.
|
18 |
R.C. Smith, Uncertainty Quantification: Theory, Implementation, and Applications, vol. 12, Siam, 2013.
|
19 |
A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, S. Tarantola, Global Sensitivity Analysis: the Primer, John Wiley & Sons, 2008.
|
20 |
M.I. Radaideh, T. Kozlowski, Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling, Int. J. Energy Res. 43 (14) (2019) 7866-7890.
DOI
|
21 |
D. Rochman, S. van der Marck, A. Koning, H. Sjostrand, W. Zwermann, Uncertainty propagation with fast Monte Carlo techniques, Nucl. Data Sheets 118 (2014) 367-369.
DOI
|