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Статті в журналах з теми "Propagation uncertainties"

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Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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1

Demeyer, Séverine, Samuel K. Kristoffersen, Alexis Le Pichon, Franck Larsonnier, and Nicolas Fischer. "Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems." Remote Sensing 15, no. 7 (March 31, 2023): 1892. http://dx.doi.org/10.3390/rs15071892.

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To improve the confidence and quality of measurements produced by regional and international infrasound monitoring networks, this work investigates a methodology for propagating uncertainty associated with on-site measurement systems. We focus on the propagation of sensor calibration uncertainties. The proposed approach is applied to synthetic infrasound signals with known back azimuth and trace velocity, recorded at the array elements. Relevant input uncertainties are investigated for propagation targeting the incoming signals (noise), instrumentation (microbarometers, calibration system, wind noise reduction system), and the time-delay-of-arrival (TDOA) model (frequency band). Uncertainty propagation is performed using the Monte Carlo method to obtain the corresponding uncertainties of the relevant output quantities of interest, namely back azimuth and trace velocity. The results indicate that, at high frequencies, large sensor uncertainties are acceptable. However, at low frequencies (<0.1 Hz), even a 2∘ sensor phase uncertainty can lead to errors in the back azimuth of up to 5∘ and errors in the trace velocity of 20 m/s.

2

Dachs, Edgar. "Uncertainties in the activities of garnets and their propagation into geothermobarometry." European Journal of Mineralogy 6, no. 2 (March 31, 1994): 291–96. http://dx.doi.org/10.1127/ejm/6/2/0291.

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3

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 (January 2002): 230–41. http://dx.doi.org/10.1016/s0168-9002(01)01438-3.

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4

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

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5

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.

6

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

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7

TANSEL, BERRIN. "PROPAGATION OF PARAMETER UNCERTAINTIES TO SYSTEM DEPENDABILITY." Civil Engineering and Environmental Systems 16, no. 1 (March 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 (January 2013): 164–91. http://dx.doi.org/10.1137/120888399.

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9

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

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10

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

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11

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

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12

Wilson, D. Keith, Chris L. Pettit, Vladimir E. Ostashev, and Matthew J. Kamrath. "Signal power distributions for simulated outdoor sound propagation in varying refractive conditions." Journal of the Acoustical Society of America 151, no. 6 (June 2022): 3895–906. http://dx.doi.org/10.1121/10.0011640.

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Probability distributions of acoustic signals propagating through the near-ground atmosphere are simulated by the parabolic equation method. The simulations involve propagation at four angles relative to the mean wind, with frequencies of 100, 200, 400, and 800 Hz. The environmental representation includes realistic atmospheric refractive profiles, turbulence, and ground interactions; cases are considered with and without parametric uncertainties in the wind velocity and surface heat flux. The simulated signals are found to span a broad range of scintillation indices, from near zero to exceeding ten. In the absence of uncertainties, the signal power (or intensity) is fit well by a two-parameter gamma distribution, regardless of the frequency and refractive conditions. When the uncertainties are included, three-parameter distributions, namely, the compound gamma or generalized gamma, are needed for a good fit to the simulation data. The compound gamma distribution appears preferable because its parameters have a straight forward interpretation related to the saturation and modulation of the signal by uncertainties.

13

Lindley, Ben, Brendan Tollit, Peter Smith, Alan Charles, Robert Mason, Tim Ware, Ray Perry, Jean Lavarenne, Una Davies, and Robert Gregg. "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.

14

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

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15

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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16

Kortelainen, Markus. "Propagation of uncertainties in the nuclear DFT models." Journal of Physics G: Nuclear and Particle Physics 42, no. 3 (February 5, 2015): 034021. http://dx.doi.org/10.1088/0954-3899/42/3/034021.

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17

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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18

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 (October 2018): 102309. http://dx.doi.org/10.1063/1.5053906.

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19

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 (November 17, 2004): 432–38. http://dx.doi.org/10.1088/0026-1394/41/6/011.

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20

Wang, C. M., and Hari K. Iyer. "Propagation of uncertainties in measurements using generalized inference." Metrologia 42, no. 2 (March 22, 2005): 145–53. http://dx.doi.org/10.1088/0026-1394/42/2/010.

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21

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.

22

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.

23

(Stacey) Gu, Xiaoyu, John E. Renaud, and Charles L. Penninger. "Implicit Uncertainty Propagation for Robust Collaborative Optimization." Journal of Mechanical Design 128, no. 4 (January 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.

24

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.

25

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.

26

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.

27

Martin, Peter E., James R. Metcalf, and Rebecca M. Flowers. "Calculation of uncertainty in the (U–Th) ∕ He system." Geochronology 5, no. 1 (February 7, 2023): 91–107. http://dx.doi.org/10.5194/gchron-5-91-2023.

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Abstract. Although rigorous uncertainty reporting on (U–Th) / He dates is key for interpreting the expected distributions of dates within individual samples and for comparing dates generated by different labs, the methods and formulae for calculating single-grain uncertainty have never been fully described and published. Here we publish two procedures to derive (U–Th) / He single-grain date uncertainty (linear and Monte Carlo uncertainty propagation) based on input 4He, radionuclide, and isotope-specific FT (alpha-ejection correction) values and uncertainties. We also describe a newly released software package, HeCalc, that performs date calculation and uncertainty propagation for (U–Th) / He data. Propagating uncertainties in 4He and radionuclides using a compilation of real (U–Th) / He data (N=1978 apatites and 1753 zircons) reveals that the uncertainty budget in this dataset is dominated by uncertainty stemming from the radionuclides, yielding median relative uncertainty values of 2.9 % for apatite dates and 1.7 % for zircon dates (1 s equivalent). When uncertainties in FT of 2 % or 5 % are assumed and additionally propagated, the median relative uncertainty values increase to 3.5 % and 5.8 % for apatite dates and 2.6 % and 5.2 % for zircon dates. The potentially strong influence of FT on the uncertainty budget underscores the importance of ongoing efforts to better quantify and routinely propagate FT uncertainty into (U–Th) / He dates. Skew is generally positive and can be significant, with ∼ 17 % of apatite dates and ∼ 6 % of zircon dates in the data compilation characterized by skewness of 0.25 or greater assuming 2 % uncertainty in FT. This outcome indicates the value of applying Monte Carlo uncertainty propagation to identify samples with substantially asymmetric uncertainties that should be considered during data interpretation. The formulae published here and the associated HeCalc software can aid in more consistent and rigorous (U–Th) / He uncertainty reporting, which is also a key first step in quantifying whether multiple aliquots from a sample are over-dispersed, with dates that differ beyond what is expected from analytical and FT uncertainties.

28

Tailpied, Dorianne, Alexis Le Pichon, and Benoit Taisne. "Assessing uncertainties in infrasound network performance modelling: application to the Euro-Mediterranean and Southeast Asian region." Geophysical Journal International 228, no. 2 (October 1, 2021): 1324–45. http://dx.doi.org/10.1093/gji/ggab399.

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SUMMARY We propose a modelling technique to confidently estimate and optimize the performance of any infrasound network to remotely monitor sources of interest such as volcanic eruptions, while considering realistic atmospheric specifications along the propagation path, source frequency and noise levels at the station. To provide a more realistic picture of the network performance, we define a confidence level accounting for propagation and atmospheric uncertainties. Therefore, we consider ‘numerical’ uncertainties linked to the approximations made in the used propagation model, errors of the developed mathematical model and atmospheric uncertainties derived from measurement campaigns. In parallel, we perform a sensitivity analysis to determine how each input parameter contributes to the developed mathematical model output as well as to the attenuation model output. Such study is helpful for model simplification and uncertainty reduction by identifying, and thus paying more attention to the most influential model inputs. Below 1 Hz, the effect of ‘numerical’ errors on network performance modelling dominates. The same situation is observed during strong and stable downwind stratospheric winds along propagation paths. Conversely, when propagation occurs upwind, atmospheric uncertainties become predominant as the frequency increases. This method is then applied to assess the performance of the International Monitoring System (IMS) infrasound network in the Euro-Mediterranean and the Southeast Asian regions. We highlight a frequency, seasonal and spatial dependence of uncertainties in the modelling. Below 1 Hz, large errors are predicted in the shadow zone but the overall error is less than 20 dB. Above 1 Hz, errors with same order of magnitude are also observed, when strong stratospheric jets prevail. But during weak stratospheric duct, uncertainties associated to the modelled attenuation may exceed 30 dB. Such studies lead to significant improvement in assessing detection capability of infrasound network, which is of great interest for monitoring artificial or natural explosive sources like volcanic eruption. In particular this work will contribute into designing and prioritizing maintenance of any given infrasound network, in order to provide even better and more accurate predictions.

29

Sarkar, Arnab. "Uncertainty propagation in Pu isotopic composition calculation by gamma spectrometry: theory versus experiment." Radiochimica Acta 109, no. 4 (January 28, 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.

30

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 (August 1, 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.

31

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 (June 2014): 299–312. http://dx.doi.org/10.5516/net.01.2014.709.

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32

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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33

Sjöstrand, Henrik, Sean Conroy, Petter Helgesson, Solis Augusto Hernandez, Arjan Koning, Stephan Pomp, and Dimitri Rochman. "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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34

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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35

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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36

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

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37

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 (August 21, 2017): 1602. http://dx.doi.org/10.1364/josaa.34.001602.

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38

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 (December 1, 1989): 271–80. http://dx.doi.org/10.1088/0952-4746/9/4/007.

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39

Gyasi-Agyei, Yeboah. "Propagation of uncertainties in interpolated rainfields to runoff errors." Hydrological Sciences Journal 64, no. 5 (April 4, 2019): 587–606. http://dx.doi.org/10.1080/02626667.2019.1593989.

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40

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

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41

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 (November 2016): 982–92. http://dx.doi.org/10.1002/2016sw001494.

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42

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

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43

Kros, J., G. B. M. Heuvelink, G. J. Reinds, J. P. Lesschen, V. Ioannidi, and W. de Vries. "Uncertainties in model predictions of nitrogen fluxes from agro-ecosystems in Europe." Biogeosciences Discussions 9, no. 5 (May 29, 2012): 6051–94. http://dx.doi.org/10.5194/bgd-9-6051-2012.

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Abstract. To assess the responses of nitrogen and greenhouse gas emissions to pan-European changes in land cover, land management and climate, an integrated dynamic model, INTEGRATOR, has been developed. This model includes both simple process-based descriptions and empirical relationships, and uses detailed GIS-based environmental and farming data in combination with various downscaling methods. This paper analyses the propagation of uncertainties in model inputs and model parameters to outputs of INTEGRATOR, using a Monte Carlo analysis. Uncertain model inputs and parameters were represented by probability distributions, while spatial correlation in these uncertainties was taken into account by assigning correlation coefficients at various spatial scales. The uncertainty propagation was analysed for the emissions of NH3, N2O and NOx and N leaching to groundwater and N surface runoff to surface water for the entire EU27 and for individual countries. Results show large uncertainties for N leaching and N runoff (relative errors of ~19 % for Europe as a whole), and smaller uncertainties for emission of N2O, NH3 and NOx (relative errors of ~12 %). Uncertainties for Europe as a whole were much smaller compared to uncertainties at Country level, because errors partly cancelled out due to spatial aggregation.

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Kros, J., G. B. M. Heuvelink, G. J. Reinds, J. P. Lesschen, V. Ioannidi, and W. De Vries. "Uncertainties in model predictions of nitrogen fluxes from agro-ecosystems in Europe." Biogeosciences 9, no. 11 (November 20, 2012): 4573–88. http://dx.doi.org/10.5194/bg-9-4573-2012.

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Abstract. To assess the responses of nitrogen and greenhouse gas emissions to pan-European changes in land cover, land management and climate, an integrated dynamic model, INTEGRATOR, has been developed. This model includes both simple process-based descriptions and empirical relationships and uses detailed GIS-based environmental and farming data in combination with various downscaling methods. This paper analyses the propagation of uncertainties in model inputs and parameters to outputs of INTEGRATOR, using a Monte Carlo analysis. Uncertain model inputs and parameters were represented by probability distributions, while spatial correlation in these uncertainties was taken into account by assigning correlation coefficients at various spatial scales. The uncertainty propagation was analysed for the emissions of NH3, N2O and NOx, N leaching to groundwater and N runoff to surface water for the entire EU27 and for individual countries. Results show large uncertainties for N leaching and runoff (relative errors of ∼ 19% for Europe as a whole), and smaller uncertainties for emission of N2O, NH3 and NOx (relative errors of ∼ 12%). Uncertainties for Europe as a whole were much smaller compared to uncertainties at country level, because errors partly cancelled out due to spatial aggregation.

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Gilsinn, David E., and Alice V. Ling. "Comparative Statistical Analysis of Test Parts Manufactured in Production Environments." Journal of Manufacturing Science and Engineering 126, no. 1 (February 1, 2004): 189–99. http://dx.doi.org/10.1115/1.1645876.

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Estimating error uncertainties arising in production parts is not a well-understood process. An approach to estimate these uncertainties was developed in this study. Machine tool error components were measured on a three-axis vertical machining center. Multiple parts were produced on the measured machining center then measured on a coordinate measuring machine. Uncertainty models for hole-center to hole-center lengths and orthogonalities were developed using measured machine tool errors. These estimated uncertainties were compared against measured uncertainties. The main conclusion from the study is that the Law of Propagation of Uncertainties can be used to estimate machining uncertainties.

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Jalid, Abdelilah, Said Hariri, and Jean Paul Senelaer. "Estimation of form deviation and the associated uncertainty in coordinate metrology." International Journal of Quality & Reliability Management 32, no. 5 (May 5, 2015): 456–71. http://dx.doi.org/10.1108/ijqrm-06-2012-0087.

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Purpose – The uncertainty evaluation for coordinate measuring machine metrology is problematic due to the diversity of the parameters that can influence the measurement result. From discrete coordinate data taken on curve (or surface) the software of these machines proceeds to an identification of the measured feature, the parameters of the substitute feature serve in the phase of calculation to estimate the form error of form, and the decisions made based on the result measurement may be outliers when the uncertainty associated to the measurement result is not taken into account. The paper aims to discuss these issues. Design/methodology/approach – The authors relied on the orthogonal distance regression (ODR) algorithm to estimate the parameters of the substitute geometrical elements and their uncertainties. The solution of the problem is resolved by an iterative calculation according to the Levenberg Marquard optimization method. The authors have also presented in this paper the propagation model of uncertainties to the circularity error. This model is based on the law of propagation of the uncertainties defined in the GUM. Findings – This work proposes a model based on ODR to estimates parameters of the substitute geometrical elements and their uncertainties. This contribution allows us to estimate the uncertaintof the form error by applying the law of propagation of uncertainties. An example of calculating the circularity error and the associated uncertainty is explained. This method can be applied to others geometry type: line, plan, sphere, cylinder and cone. Practical implications – This work interested manufacturing firms by allowing them: to meet the normative, which requires that each measurement must be accompanied by its uncertainty-in conformity assessment, the decision-making must take account of this uncertainty to avoid the aberrant decisions. Informing the operators on the capability of their measurement process Originality/value – This work proposes a model based on ODR to estimates parameters of the substitute geometrical elements and its uncertainties. without the hypothesis of small displacements torsor, this method integrates the uncertainty on the coordinates of points and can be applied in any reference placemark. This contribution allows us also to estimate the uncertainty of the form error by applying the law of propagation of uncertainties.

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Popp, Thomas, and Jonathan Mittaz. "Systematic Propagation of AVHRR AOD Uncertainties—A Case Study to Demonstrate the FIDUCEO Approach." Remote Sensing 14, no. 4 (February 12, 2022): 875. http://dx.doi.org/10.3390/rs14040875.

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The AVHRR aerosol optical depth (AOD) is inverted from measured reflectances in the red band using a statistical correlation of surface reflectance with mid-infrared channel reflectances and a modelling climatology of the aerosol type. For such a sensor not specifically designed for AOD retrieval, propagating uncertainties is crucial because the sensitivity of the retrieved AOD to the measured signal varies largely with retrieval conditions (AOD itself, surface brightness, aerosol optical properties/aerosol type, observing geometry). In order to quantify the different contributions to the AOD uncertainties, we have undertaken a thorough analysis of the retrieval operator and its sensitivities to the used input and auxiliary variables. Uncertainties are then propagated from measured reflectances to geophysical retrieved AOD datasets at the super-pixel level and further to gridded daily and monthly products. The propagation uses uncertainty correlations of separate uncertainty contributions from the FIDUCEO easyFCDR level1b products (common fully correlated, independent random, and structured parts) and estimated uncertainty correlation structures of other major effects in the retrieval (surface brightness, aerosol type ensemble, cloud mask). The pixel-level uncertainties are statistically validated against true error estimates versus AERONET ground-based AOD measurements. It is shown that a 10-year time record over Europe compares well to a merged multi-satellite record and that pixel-level uncertainties provide a meaningful representation of error distributions. The study demonstrates the benefits of new recipes for uncertainty characterization from the Horizon-2020 project FIDUCEO (“Fidelity and uncertainty in climate data records from Earth Observations”) and extends them further with recent additions developed within the ESA Climate Change Initiative.

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Geyh, Mebus A. "The handling of numerical ages and their random uncertainties." E&G Quaternary Science Journal 57, no. 1/2 (August 1, 2008): 239–52. http://dx.doi.org/10.3285/eg.57.1-2.10.

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Abstract. The correct handling of numerical ages and their standard deviations and a proper introduction to error propagation or propagation of uncertainty and statistical evaluation are important to avoid misleading chronological conclusions and statements even though based on properly determined and reliable numerical dates. The conclusions may also be erroneous if dates were taken from databases without sufficient background information on the origin of the dated material and the applied analytical techniques. This paper is an introduction into the field of mathematical handling and testing of numerical ages. The most common and simple calculations and statistical tests that are needed are described and the steps involved are demonstrated on examples. The problems involved in the visualization of numerical dates in the form of normal histograms and dispersion histograms are discussed.

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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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Slavickas, Andrius, Raimondas Pabarčius, Aurimas Tonkūnas, and Eugenijus Ušpuras. "Uncertainty and Sensitivity Analysis of Void Reactivity Feedback for 3D BWR Assembly Model." Science and Technology of Nuclear Installations 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9894727.

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Uncertainty and sensitivity analysis of void reactivity feedback for 3D BWR fuel assembly model is presented in this paper. Uncertainties in basic input data, such as the selection of different cross section library, manufacturing uncertainties in material compositions, and geometrical dimensions, as well as operating data are considered. An extensive modelling of different input data realizations associated with their uncertainties was performed during sensitivity analysis. The propagation of uncertainties was analyzed using the statistical approach. The results revealed that important information on the code predictions can be obtained by analyzing and comparing the codes estimations and their associated uncertainties.