Auswahl der wissenschaftlichen Literatur zum Thema „Propagation uncertainties“
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Zeitschriftenartikel zum Thema "Propagation uncertainties"
Demeyer, Séverine, Samuel K. Kristoffersen, Alexis Le Pichon, Franck Larsonnier und Nicolas Fischer. „Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems“. Remote Sensing 15, Nr. 7 (31.03.2023): 1892. http://dx.doi.org/10.3390/rs15071892.
Der volle Inhalt der QuelleDachs, Edgar. „Uncertainties in the activities of garnets and their propagation into geothermobarometry“. European Journal of Mineralogy 6, Nr. 2 (31.03.1994): 291–96. http://dx.doi.org/10.1127/ejm/6/2/0291.
Der volle Inhalt der QuelleMatzke, Manfred. „Propagation of uncertainties in unfolding procedures“. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 476, Nr. 1-2 (Januar 2002): 230–41. http://dx.doi.org/10.1016/s0168-9002(01)01438-3.
Der volle Inhalt der QuelleDong, W. M., W. L. Chiang und F. S. Wong. „Propagation of uncertainties in deterministic systems“. Computers & Structures 26, Nr. 3 (Januar 1987): 415–23. http://dx.doi.org/10.1016/0045-7949(87)90041-1.
Der volle Inhalt der QuelleFrosio, Thomas, Thomas Bonaccorsi und 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.
Der volle Inhalt der QuelleWiwatanadate, Phongtape, und H. Gregg Claycamp. „Exact propagation of uncertainties in multiplicative models“. Human and Ecological Risk Assessment: An International Journal 6, Nr. 2 (April 2000): 355–68. http://dx.doi.org/10.1080/10807030009380068.
Der volle Inhalt der QuelleTANSEL, BERRIN. „PROPAGATION OF PARAMETER UNCERTAINTIES TO SYSTEM DEPENDABILITY“. Civil Engineering and Environmental Systems 16, Nr. 1 (März 1999): 19–35. http://dx.doi.org/10.1080/02630259908970249.
Der volle Inhalt der QuelleButler, T., C. Dawson und T. Wildey. „Propagation of Uncertainties Using Improved Surrogate Models“. SIAM/ASA Journal on Uncertainty Quantification 1, Nr. 1 (Januar 2013): 164–91. http://dx.doi.org/10.1137/120888399.
Der volle Inhalt der QuelleHauptmanns, Ulrich. „Analytical propagation of uncertainties through fault trees“. Reliability Engineering & System Safety 76, Nr. 3 (Juni 2002): 327–29. http://dx.doi.org/10.1016/s0951-8320(02)00016-9.
Der volle Inhalt der Quellevan der Drift, J. H. M., und C. J. M. Heemskerk. „Propagation of Spatial Uncertainties Between Assembly Primitives“. IFAC Proceedings Volumes 23, Nr. 3 (September 1990): 677–81. http://dx.doi.org/10.1016/s1474-6670(17)52638-5.
Der volle Inhalt der QuelleDissertationen zum Thema "Propagation uncertainties"
Stanton, Richard. „Robust acoustic beamforming in the presence of channel propagation uncertainties“. Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/43535.
Der volle Inhalt der QuelleBertin, Michaël. „Propagation des incertitudes dans un modèle réduit de propagation des infrasons“. Thesis, Cachan, Ecole normale supérieure, 2014. http://www.theses.fr/2014DENS0020/document.
Der volle Inhalt der QuelleThe perturbation of a system can give rise to wave propagation. A classical approach to understand this phenomenon is to look for natural modes of vibration of the medium. Mathematically, finding these modes requires to seek the eigenvalues and eigenfunctions of the propagation operator. However, from a numerical point of view, the operation can be costly because the matrices can be of very large size. Furthermore, in most applications, uncertainties are inevitably associated with our model. The question then arises as to whether we should allocate significant computational resources for simulation while the accuracy of the result is not guaranteed. We propose in this thesis an approach that allows both a better understanding of the influence of uncertainties on the propagation and a significant decrease of computational costs for infrasound propagation in the atmosphere. The main idea is that all modes do not have the same importance and only a few of them is often sufficient to account for the phenomenon without a significant loss of accuracy. These modes appear to be those which are most sensitive to atmospheric disturbances. Specifically, a sensitivity analysis is used to identify the most influential structures of the atmosphere, the associated groups of modes and their associated parts of the infrasound signal. These groups of modes can be specifically targeted in a spectrum calculation with the projection of the operator onto Krylov subspaces, that allows a significant decrease of the computational cost. This method of model reduction can be applied in a statistical framework as well and estimations of the expectation and the variance of the results are carried out without a significant loss of accuracy and still with a low cost
Moreno, de Castro María [Verfasser]. „Propagation of uncertainties in mesocosm experiments on ocean acidification / María Moreno de Castro“. Kiel : Universitätsbibliothek Kiel, 2016. http://d-nb.info/1102933058/34.
Der volle Inhalt der QuelleDavis, Daniel E. „A Technique for Evaluating the Uncertainties in Path Loss Predictions Caused by Sparsely Sampled Terrain Data“. Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23314.
Der volle Inhalt der QuelleThat is accomplished by accurately solving the electromagnetic fields over many randomly rough surfaces which pass through the sparse topographic data points, many possible communication links, all of which fit the underlying data, are represented. The power variation
caused by the different surface realizations is that due to the sparse sampling. Additionally, to verify that this solution technique is a good model, experimental propagation measurements were taken, and compared to the computations.
Master of Science
Lu, Yen-Sen [Verfasser]. „Propagation of land surface model uncertainties in simulated terrestrial system states / Yen-Sen Lu“. Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1161462252/34.
Der volle Inhalt der QuelleGeraci, Gianluca. „Schemes and Strategies to Propagate and Analyze Uncertainties in Computational Fluid Dynamics Applications“. Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00954413.
Der volle Inhalt der QuelleSaussus, Denis. „Probabilistic distributions of ultimate axial pile resistance derived from propagation of epistemic and aleatory material and model uncertainties“. Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/32819.
Der volle Inhalt der QuelleChiang, Keng-Yen. „Thermal hydraulic limits analysis for the MIT Research Reactor low enrichment uranium core conversion using statistical propagation of parametric uncertainties“. Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/77069.
Der volle Inhalt der QuelleCataloged from PDF version of thesis.
Includes bibliographical references.
The MIT Research Reactor (MITR) is evaluating the conversion from highly enriched uranium (HEU) to low enrichment uranium (LEU) fuel. In addition to the fuel element re-design from 15 to 18 plates per element, a reactor power upgraded from 6 MW to 7 MW is proposed in order to maintain the same reactor performance of the HEU core. Previous approaches in analyzing the impact of engineering uncertainties on thermal hydraulic limits via the use of engineering hot channel factors (EHCFs) were unable to explicitly quantify the uncertainty and confidence level in reactor parameters. The objective of this study is to develop a methodology for MITR thermal hydraulic limits analysis by statistically combining engineering uncertainties in order to eliminate unnecessary conservatism inherent in traditional analyses. This methodology was employed to analyze the Limiting Safety System Settings (LSSS) for the MITR LEU core, based on the criterion of onset of nucleate boiling (ONB). Key parameters, such as coolant channel tolerances and heat transfer coefficients, were considered as normal distributions using Oracle Crystal Ball for the LSSS evaluation. The LSSS power is determined with 99.7% confidence level. The LSSS power calculated using this new methodology is 9.1 MW, based on core outlet coolant temperature of 60 'C, and primary coolant flow rate of 1800 gpm, compared to 8.3 MW obtained from the analytical method using the EHCFs with same operating conditions. The same methodology was also used to calculate the safety limit (SL) to ensure that adequate safety margin exists between LSSS and SL. The criterion used to calculate SL is the onset of flow instability. The calculated SL is 10.6 MW, which is 1.5 MW higher than LSSS, permitting sufficient margin between LSSS and SL.
by Keng-Yen Chiang.
S.M.
Leissing, Thomas. „Nonlinear acoustic wave propagation in complex media : application to propagation over urban environments“. Phd thesis, Université Paris-Est, 2009. http://tel.archives-ouvertes.fr/tel-00584398.
Der volle Inhalt der QuelleDumont, Nicolas. „Méthodes numériques et modèle réduit de chimie tabulée pour la propagation d'incertitudes de cinétique chimique“. Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC037.
Der volle Inhalt der QuelleNumerical simulation plays a key role in the field of combustion today, either in the research area by permitting a better understanding of phenomenons taking place inside reactive flows or in the development of industrial application by reducing designing cost of systems. Large Eddy Simulation is at the time the most suited tool for the simulation of reactive flows. Large Eddy Simulation of reactive flows is in practice only possible thanks to a modeling of different phenomenons:- turbulence is modeled for small structures allowing to resolve only big structures which results in lower computational cost- chemistry is modeled using reduction methods which allows to drastically reduce computational costThe maturity of Large Eddy Simulation of reactive flows makes it today a reliable, predictive and promising tool. It now makes sense to focus on the impact of the parameters involved in the different models on the simulation results. This study of the impact of the modeling parameters can be seen from the perspective of uncertainties propagation, and can give interesting informations both from a practical side for the robust design of systems but also on the theoretical side in order to improve the models used and guide the experimental measurements to be made for the reliability improvement of these models.The context of this thesis is the development of efficient methods allowing the propagation of uncertainties present in the chemical kinetic parameters of the reaction mechanisms within Large Eddy Simulation, these methods having to be non-intrusive in order to take advantage of the existence of the different computation codes which are tools requiring heavy means for their development. Such a propagation of uncertainties using a brute-force method suffers from the "curse of dimensionality" because of the large number of chemical kinetic parameters, implying a practical impossibility with the current means of computation which justifies the development of efficient methods.The objective of the thesis is the development of a reduced model that can be used for uncertainties propagation in Large Eddy simulations. The handling and implementation of various tools resulting from the uncertainties propagation framework has been an essential preliminary work in this thesis in order to bring this knowledge and skills into the EM2C laboratory.The method developed in this thesis for the propagation of chemical kinetic parameters uncertainties is limited to chemistry models in which the advancement of the combustion process is summarized by the evolution of a progress variable given by a transport equation, the access to other informations being made through the use of a table. Through the study of the evolution of a constant pressure adiabatic reactor containing a homogeneous mixture of air and dihydrogen, it is shown that a large part of the uncertainties of such a system can be explained by the uncertainties of the progress variable. This makes it possible to define a chemical table that can be used to propagate uncertainties of chemical kinetic parameters in Large Eddy Simulations. The introduction of the uncertainties is then done only by the modeling of the source term present in the transport equation of the progress variable, which can be parameterized with the help of few uncertain parameters thus avoiding the "curse of dimensionality"
Bücher zum Thema "Propagation uncertainties"
Center, Langley Research, Hrsg. Propagation of experimental uncertainties from the tunnel to the body coordinate system in 3-D LDV flow field studies. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.
Den vollen Inhalt der Quelle findenCenter, Langley Research, Hrsg. Propagation of experimental uncertainties from the tunnel to the body coordinate system in 3-D LDV flow field studies. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.
Den vollen Inhalt der Quelle findenNational Aeronautics and Space Administration (NASA) Staff. Propagation of Experimental Uncertainties from the Tunnel to the Body Coordinate System in 3-D LDV Flow Field Studies. Independently Published, 2018.
Den vollen Inhalt der Quelle findenA Review of techniques for propagating data and parameter uncertainties in high-level radioactive waste repository performance assessment models. Division of High-Level Waste Management, Office of Nuclear Material Safety and Safeguards, U.S. Nuclear Regulatory Commission, 1990.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Propagation uncertainties"
Gupta, S. V. „Propagation of Uncertainty“. In Measurement Uncertainties, 109–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20989-5_5.
Der volle Inhalt der QuellePinto, Paolo Emilio. „Modeling and Propagation of Uncertainties“. In SYNER-G: Typology Definition and Fragility Functions for Physical Elements at Seismic Risk, 29–45. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7872-6_2.
Der volle Inhalt der QuelleGrabe, Michael. „Error Propagation, Two Variables“. In Measurement Uncertainties in Science and Technology, 81–96. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04888-8_6.
Der volle Inhalt der QuelleGrabe, Michael. „Error Propagation, m Variables“. In Measurement Uncertainties in Science and Technology, 97–104. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04888-8_7.
Der volle Inhalt der QuelleChikhaoui, Khaoula, Noureddine Bouhaddi, Najib Kacem, Mohamed Guedri und Mohamed Soula. „Uncertainties Propagation through Robust Reduced Model“. In Design and Modeling of Mechanical Systems - II, 537–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17527-0_53.
Der volle Inhalt der QuelleCrowder, Stephen, Collin Delker, Eric Forrest und Nevin Martin. „Analytical Methods for the Propagation of Uncertainties“. In Introduction to Statistics in Metrology, 131–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53329-8_7.
Der volle Inhalt der QuelleLitvinenko, Alexander, Dmitry Logashenko, Raul Tempone, Gabriel Wittum und David Keyes. „Propagation of Uncertainties in Density-Driven Flow“. In Lecture Notes in Computational Science and Engineering, 101–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81362-8_5.
Der volle Inhalt der QuelleLi, Wenye. „Clustering with Uncertainties: An Affinity Propagation-Based Approach“. In Neural Information Processing, 437–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34500-5_52.
Der volle Inhalt der QuelleCrowder, Stephen, Collin Delker, Eric Forrest und Nevin Martin. „Monte Carlo Methods for the Propagation of Uncertainties“. In Introduction to Statistics in Metrology, 153–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53329-8_8.
Der volle Inhalt der QuelleBouchon, Bernadette, und Sylvie Desprès. „Propagation of uncertainties and inaccuracies in knowledge-based system“. In Uncertainty in Knowledge-Based Systems, 58–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/3-540-18579-8_4.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Propagation uncertainties"
Novák, Drahomír. „FReET: Software for Uncertainties Propagation“. In 5th International Conference on Statistics: Theory and Applications (ICSTA 2023). Avestia Publishing, 2023. http://dx.doi.org/10.11159/icsta23.150.
Der volle Inhalt der QuelleTaillet, Richard. „Cosmic Ray propagation uncertainties and Dark Matter“. In Identification of Dark Matter 2010. Trieste, Italy: Sissa Medialab, 2011. http://dx.doi.org/10.22323/1.110.0015.
Der volle Inhalt der QuelleMao, Wengang, Jingxia Yue, Da Wu, Luis De Gracia und Naoki Osawa. „Uncertainties of Crack Propagation Analysis in Ship Structures“. In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54226.
Der volle Inhalt der QuelleBell, Kristine L., und Robert E. Zarnich. „MAP-PF multitarget tracking with propagation modeling uncertainties“. In 2013 Asilomar Conference on Signals, Systems and Computers. IEEE, 2013. http://dx.doi.org/10.1109/acssc.2013.6810602.
Der volle Inhalt der QuelleGarg, Diksha, Mary Hall Reno und NuSpaceSim Collaboration. „Neutrino propagation through Earth: modeling uncertainties using nuPyProp“. In 38th International Cosmic Ray Conference. Trieste, Italy: Sissa Medialab, 2023. http://dx.doi.org/10.22323/1.444.1115.
Der volle Inhalt der QuelleArz, Uwe, Jens Leinhos und Dirk Schubert. „Uncertainties in Coplanar Waveguide Capacitance Measurements“. In 2006 IEEE Workshop on Signal Propagation on Interconnects. IEEE, 2006. http://dx.doi.org/10.1109/spi.2006.289162.
Der volle Inhalt der QuelleDiamant, Roee, und Lutz Lampe. „Underwater localization with time-synchronization and propagation speed uncertainties“. In 2011 8th Workshop on Positioning, Navigation and Communication (WPNC). IEEE, 2011. http://dx.doi.org/10.1109/wpnc.2011.5961023.
Der volle Inhalt der QuelleTaylor, Craig, William Graf, Yajie Jerry Lee, Charles Huyck und Zhenghui Hu. „Propagation of Uncertainties through Robust Simulation and Future Research“. In 5th Asian-Pacific Symposium on Structural Reliability and its Applications. Singapore: Research Publishing Services, 2012. http://dx.doi.org/10.3850/978-981-07-2219-7_p255.
Der volle Inhalt der QuellePourgol-Mohammad, Mohammad. „Uncertainty Propagation in Complex Codes Calculations“. In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-16570.
Der volle Inhalt der QuelleFei, Zhouxiang, Yi Huang und Jiafeng Zhou. „Crosstalk variations caused by uncertainties in three-conductor transmission lines“. In 2015 Loughborough Antennas & Propagation Conference (LAPC). IEEE, 2015. http://dx.doi.org/10.1109/lapc.2015.7366057.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Propagation uncertainties"
Masri, Sami F. Analytical and Experimental Studies of the Quantification and Propagation of Uncertainties in Nonlinear System Modeling and Simulation. Fort Belvoir, VA: Defense Technical Information Center, März 2007. http://dx.doi.org/10.21236/ada473592.
Der volle Inhalt der QuelleDunn, Floyd E., Lin-wen Hu und Erik Wilson. The STAT7 Code for Statistical Propagation of Uncertainties In Steady-State Thermal Hydraulics Analysis of Plate-Fueled Reactors. Office of Scientific and Technical Information (OSTI), Dezember 2016. http://dx.doi.org/10.2172/1349053.
Der volle Inhalt der QuellePham, Son, Lin-wen Hu und Erik Wilson. The STAT7 Code for Statistical Propagation of Uncertainties In Steady-State Thermal Hydraulics Analysis of Plate-Fueled Reactors. Office of Scientific and Technical Information (OSTI), Juli 2020. http://dx.doi.org/10.2172/1825880.
Der volle Inhalt der QuelleYang, Se, Dhongik Yoon, Lin-wen Hu und Erik Wilson. The STAT7 Code for Statistical Propagation of Uncertainties in Steady State Thermal Hydraulics Analysis of Plate Fueled Reactors. Office of Scientific and Technical Information (OSTI), Januar 2023. http://dx.doi.org/10.2172/1923025.
Der volle Inhalt der QuelleWendelberger, James G. Non-Destructive Assay (NDA) Uncertainties Impact on Physical Inventory Difference (ID) and Material Balance Determination: Sources of Error, Precision/Accuracy, and ID/Propagation of Error (POV). Office of Scientific and Technical Information (OSTI), Oktober 2016. http://dx.doi.org/10.2172/1304800.
Der volle Inhalt der QuelleZimmerman, D., K. Wahl, A. Gutjahr und P. Davis. A review of techniques for propagating data and parameter uncertainties in high-level radioactive waste repository performance assessment models. Office of Scientific and Technical Information (OSTI), März 1990. http://dx.doi.org/10.2172/6961264.
Der volle Inhalt der QuelleBray, Jonathan, Ross Boulanger, Misko Cubrinovski, Kohji Tokimatsu, Steven Kramer, Thomas O'Rourke, Ellen Rathje, Russell Green, Peter Robertson und Christine Beyzaei. U.S.—New Zealand— Japan International Workshop, Liquefaction-Induced Ground Movement Effects, University of California, Berkeley, California, 2-4 November 2016. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, März 2017. http://dx.doi.org/10.55461/gzzx9906.
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